librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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Downtown-Case/CohereForAI_c4ai-command-r-08-2024-exl2-3.75bpw	Downtown-Case	2024-08-30T22:15:59Z	26	1	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-08-30T21:40:34Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox --- # Model Card for C4AI Command R 08-2024 ## Model Summary <!-- Provide a quick summary of what the model is/does. --> C4AI Command R 08-2024 is a research release of a 35 billion parameter highly performant generative model. Command R 08-2024 is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command R 08-2024 has the capability for multilingual generation, trained on 23 languages and evaluated in 10 languages and highly performant RAG capabilities. Developed by: Cohere and [Cohere For AI](https://cohere.for.ai) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: c4ai-command-r-08-2024 - Model Size: 35 billion parameters - Context length: 128K Try C4AI Command R If you want to try Command R before downloading the weights, the model is hosted in a hugging face space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command?model=command-r-08-2024). Usage Please use `transformers` version 4.39.1 or higher ```python # pip install 'transformers>=4.39.1' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-08-2024 chat template messages = [{"role": "user", "content": "Hello, how are you?"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Hello, how are you?<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. We use grouped query attention (GQA) to improve inference speed. Languages covered: The model has been trained on 23 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian) and evaluated on 10 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese). Context length: Command R 08-2024 supports a context length of 128K. ### Tool use & Agent capabilities: Command R 08-2024 has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance. Command R 08-2024’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R 08-2024 may use one of its supplied tools more than once. The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the `directly_answer` tool, but it can be removed or renamed if required. Comprehensive documentation for working with Command R 08-2024's tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). Command R 08-2024 also supports Hugging Face's [tool use API](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) The code snippet below shows a minimal working example on how to render a prompt. <details> <summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary> ```python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # Define tools available for the model to use: tools = [ { "name": "internet_search", "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet", "parameter_definitions": { "query": { "description": "Query to search the internet with", "type": 'str', "required": True } } }, { 'name': "directly_answer", "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history", 'parameter_definitions': {} } ] # render the tool use prompt as a string: tool_use_prompt = tokenizer.apply_tool_use_template( conversation, tools=tools, tokenize=False, add_generation_prompt=True, ) print(tool_use_prompt) ``` </details> <details> <summary><b>Usage: Rendering prompts with the Tool Use API [CLICK TO EXPAND]</b> </summary> ```python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # Define tools available for the model to use # Type hints and docstrings from Python functions are automatically extracted def internet_search(query: str): """ Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query: Query to search the internet with """ pass def directly_answer(): """ Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass tools = [internet_search, directly_answer] # render the tool use prompt as a string: tool_use_prompt = tokenizer.apply_chat_template( conversation, tools=tools, tokenize=False, add_generation_prompt=True, ) print(tool_use_prompt) ``` </details> <details> <summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary> ```` <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|SYSTEM_TOKEN\|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling. ## Available Tools Here is a list of tools that you have available to you: ```python def internet_search(query: str) -> List[Dict]: """Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query (str): Query to search the internet with """ pass ``` ```python def directly_answer() -> List[Dict]: """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass ```<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Whats the biggest penguin in the world?<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|SYSTEM_TOKEN\|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example: ```json [ { "tool_name": title of the tool in the specification, "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters } ]```<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> ```` </details> <details> <summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary> ```` Action: ```json [ { "tool_name": "internet_search", "parameters": { "query": "biggest penguin in the world" } } ] ``` ```` </details> ### Grounded Generation and RAG Capabilities: Command R 08-2024 has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG).This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation. Command R 08-2024’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured. By default, Command R 08-2024 will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation. The model is trained with a number of other answering modes, which can be selected by prompt changes. A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens. Comprehensive documentation for working with Command R 08-2024's grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). The code snippet below shows a minimal working example on how to render a prompt. <details> <summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary> ````python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # define documents to ground on: documents = [ { "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." }, { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."} ] # render the tool use prompt as a string: grounded_generation_prompt = tokenizer.apply_grounded_generation_template( conversation, documents=documents, citation_mode="accurate", # or "fast" tokenize=False, add_generation_prompt=True, ) print(grounded_generation_prompt) ```` </details> <details> <summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary> ```` <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|SYSTEM_TOKEN\|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Whats the biggest penguin in the world?<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|SYSTEM_TOKEN\|><results> Document: 0 title: Tall penguins text: Emperor penguins are the tallest growing up to 122 cm in height. Document: 1 title: Penguin habitats text: Emperor penguins only live in Antarctica. </results><\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|SYSTEM_TOKEN\|>Carefully perform the following instructions, in order, starting each with a new line. Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'. Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'. Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup. Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> ```` </details> <details> <summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary> ```` Relevant Documents: 0,1 Cited Documents: 0,1 Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres. Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0> ```` </details> ### Code Capabilities: Command R 08-2024 has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions. ### Model Card Contact For errors or additional questions about details in this model card, contact [info@for.ai](mailto:info@for.ai). ### Terms of Use: We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try Chat: You can try Command-R chat in the playground [here](https://dashboard.cohere.com/playground/chat).
mradermacher/Einstein-Replete-7B-GGUF	mradermacher	2024-08-30T22:13:14Z	22	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NotASI/Einstein-Replete-7B", "base_model:quantized:NotASI/Einstein-Replete-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-29T08:06:38Z	--- base_model: NotASI/Einstein-Replete-7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/NotASI/Einstein-Replete-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ3_S.gguf) \| IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ3_M.gguf) \| IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Einstein-Replete-7B-i1-GGUF	mradermacher	2024-08-30T22:13:10Z	32	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NotASI/Einstein-Replete-7B", "base_model:quantized:NotASI/Einstein-Replete-7B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-08-29T10:07:35Z	--- base_model: NotASI/Einstein-Replete-7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/NotASI/Einstein-Replete-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.5 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.5 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mergekit-community/mergekit-ties-svasnwf	mergekit-community	2024-08-30T22:09:46Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Hastagaras/Jamet-8B-L3-MK.V-Blackroot", "base_model:merge:Hastagaras/Jamet-8B-L3-MK.V-Blackroot", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:Vikhrmodels/Vikhr-7B-instruct_0.4", "base_model:merge:Vikhrmodels/Vikhr-7B-instruct_0.4", "base_model:crestf411/L3.1-8B-sunfall-v0.6.1-dpo", "base_model:merge:crestf411/L3.1-8B-sunfall-v0.6.1-dpo", "base_model:maldv/badger-iota-llama-3-8b", "base_model:merge:maldv/badger-iota-llama-3-8b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T22:05:06Z	--- base_model: - Hastagaras/Jamet-8B-L3-MK.V-Blackroot - Vikhrmodels/Vikhr-7B-instruct_0.4 - crestf411/L3.1-8B-sunfall-v0.6.1-dpo - Sao10K/L3-8B-Stheno-v3.2 - maldv/badger-iota-llama-3-8b library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) as a base. ### Models Merged The following models were included in the merge: * [Hastagaras/Jamet-8B-L3-MK.V-Blackroot](https://huggingface.co/Hastagaras/Jamet-8B-L3-MK.V-Blackroot) * [crestf411/L3.1-8B-sunfall-v0.6.1-dpo](https://huggingface.co/crestf411/L3.1-8B-sunfall-v0.6.1-dpo) * [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) * [maldv/badger-iota-llama-3-8b](https://huggingface.co/maldv/badger-iota-llama-3-8b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Vikhrmodels/Vikhr-7B-instruct_0.4 - model: crestf411/L3.1-8B-sunfall-v0.6.1-dpo # Another RP Model trained on... stuff parameters: density: 0.4 weight: 0.25 - model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot # Another RP / Storytelling Model parameters: density: 0.5 weight: 0.3 - model: maldv/badger-iota-llama-3-8b #Megamerge - Helps with General Knowledge parameters: density: 0.6 weight: 0.35 - model: Sao10K/L3-8B-Stheno-v3.2 # This is Stheno v3.2's Initial Name parameters: density: 0.7 weight: 0.4 merge_method: ties base_model: Vikhrmodels/Vikhr-7B-instruct_0.4 parameters: int8_mask: true rescale: true normalize: false dtype: bfloat16 ```
shashank970613/lora-flan-t5-large-chat	shashank970613	2024-08-30T22:08:32Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-08-30T20:52:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gglabs/Mistral-Nemo-12B-FC-Chat-0830-2-epoch	gglabs	2024-08-30T22:06:30Z	5	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T21:31:36Z	--- base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - Developed by: gglabs - License: apache-2.0 - Finetuned from model : unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ellarose/test-trainer-alternate	ellarose	2024-08-30T22:06:04Z	5	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-08-28T21:54:34Z	--- base_model: sentence-transformers/paraphrase-mpnet-base-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: ballades (formes fixes) - text: prison fiction - text: gregorian chants - text: argentina--buenos aires, port of - text: passepieds (music) inference: true model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9555555555555556 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:-----------\|:-----------------------------------------------------------------------------------------------------------------\| \| subject \| <ul><li>'vidourle river (france)'</li><li>'knockout kings 2000 (game)'</li><li>'social practice (art)'</li></ul> \| \| genre/form \| <ul><li>'hadith stories'</li><li>'discographies'</li><li>'dance drama'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.9556 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("ellarose/test-trainer-alternate") # Run inference preds = model("prison fiction") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 1 \| 2.415 \| 10 \| \| Label \| Training Sample Count \| \|:-----------\|:----------------------\| \| subject \| 500 \| \| genre/form \| 500 \| ### Training Hyperparameters - batch_size: (40, 40) - num_epochs: (10, 10) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:---------:\|:-------------:\|:---------------:\| \| 0.0001 \| 1 \| 0.3379 \| - \| \| 0.0040 \| 50 \| 0.3311 \| - \| \| 0.0080 \| 100 \| 0.3642 \| - \| \| 0.0120 \| 150 \| 0.3077 \| - \| \| 0.0160 \| 200 \| 0.2542 \| - \| \| 0.0200 \| 250 \| 0.233 \| - \| \| 0.0240 \| 300 \| 0.23 \| - \| \| 0.0279 \| 350 \| 0.2247 \| - \| \| 0.0319 \| 400 \| 0.2009 \| - \| \| 0.0359 \| 450 \| 0.2354 \| - \| \| 0.0399 \| 500 \| 0.1823 \| - \| \| 0.0439 \| 550 \| 0.2048 \| - \| \| 0.0479 \| 600 \| 0.1546 \| - \| \| 0.0519 \| 650 \| 0.1363 \| - \| \| 0.0559 \| 700 \| 0.1031 \| - \| \| 0.0599 \| 750 \| 0.0668 \| - \| \| 0.0639 \| 800 \| 0.1156 \| - \| \| 0.0679 \| 850 \| 0.0435 \| - \| \| 0.0719 \| 900 \| 0.0495 \| - \| \| 0.0758 \| 950 \| 0.046 \| - \| \| 0.0798 \| 1000 \| 0.0424 \| - \| \| 0.0838 \| 1050 \| 0.1312 \| - \| \| 0.0878 \| 1100 \| 0.0246 \| - \| \| 0.0918 \| 1150 \| 0.0273 \| - \| \| 0.0958 \| 1200 \| 0.0075 \| - \| \| 0.0998 \| 1250 \| 0.0203 \| - \| \| 0.1038 \| 1300 \| 0.0073 \| - \| \| 0.1078 \| 1350 \| 0.0328 \| - \| \| 0.1118 \| 1400 \| 0.0274 \| - \| \| 0.1158 \| 1450 \| 0.0042 \| - \| \| 0.1198 \| 1500 \| 0.0494 \| - \| \| 0.1238 \| 1550 \| 0.0413 \| - \| \| 0.1277 \| 1600 \| 0.0036 \| - \| \| 0.1317 \| 1650 \| 0.0329 \| - \| \| 0.1357 \| 1700 \| 0.0168 \| - \| \| 0.1397 \| 1750 \| 0.0028 \| - \| \| 0.1437 \| 1800 \| 0.0227 \| - \| \| 0.1477 \| 1850 \| 0.002 \| - \| \| 0.1517 \| 1900 \| 0.0121 \| - \| \| 0.1557 \| 1950 \| 0.0018 \| - \| \| 0.1597 \| 2000 \| 0.0019 \| - \| \| 0.1637 \| 2050 \| 0.001 \| - \| \| 0.1677 \| 2100 \| 0.0009 \| - \| \| 0.1717 \| 2150 \| 0.0012 \| - \| \| 0.1756 \| 2200 \| 0.0007 \| - \| \| 0.1796 \| 2250 \| 0.001 \| - \| \| 0.1836 \| 2300 \| 0.0008 \| - \| \| 0.1876 \| 2350 \| 0.0009 \| - \| \| 0.1916 \| 2400 \| 0.001 \| - \| \| 0.1956 \| 2450 \| 0.0009 \| - \| \| 0.1996 \| 2500 \| 0.0247 \| - \| \| 0.2036 \| 2550 \| 0.0007 \| - \| \| 0.2076 \| 2600 \| 0.0008 \| - \| \| 0.2116 \| 2650 \| 0.0008 \| - \| \| 0.2156 \| 2700 \| 0.0006 \| - \| \| 0.2196 \| 2750 \| 0.0023 \| - \| \| 0.2236 \| 2800 \| 0.0007 \| - \| \| 0.2275 \| 2850 \| 0.0004 \| - \| \| 0.2315 \| 2900 \| 0.0054 \| - \| \| 0.2355 \| 2950 \| 0.0007 \| - \| \| 0.2395 \| 3000 \| 0.0004 \| - \| \| 0.2435 \| 3050 \| 0.0007 \| - \| \| 0.2475 \| 3100 \| 0.0244 \| - \| \| 0.2515 \| 3150 \| 0.0243 \| - \| \| 0.2555 \| 3200 \| 0.0005 \| - \| \| 0.2595 \| 3250 \| 0.0004 \| - \| \| 0.2635 \| 3300 \| 0.0004 \| - \| \| 0.2675 \| 3350 \| 0.0004 \| - \| \| 0.2715 \| 3400 \| 0.0004 \| - \| \| 0.2754 \| 3450 \| 0.0482 \| - \| \| 0.2794 \| 3500 \| 0.0004 \| - \| \| 0.2834 \| 3550 \| 0.0005 \| - \| \| 0.2874 \| 3600 \| 0.0005 \| - \| \| 0.2914 \| 3650 \| 0.0007 \| - \| \| 0.2954 \| 3700 \| 0.0063 \| - \| \| 0.2994 \| 3750 \| 0.0043 \| - \| \| 0.3034 \| 3800 \| 0.0005 \| - \| \| 0.3074 \| 3850 \| 0.0366 \| - \| \| 0.3114 \| 3900 \| 0.0245 \| - \| \| 0.3154 \| 3950 \| 0.0242 \| - \| \| 0.3194 \| 4000 \| 0.0003 \| - \| \| 0.3234 \| 4050 \| 0.0007 \| - \| \| 0.3273 \| 4100 \| 0.0123 \| - \| \| 0.3313 \| 4150 \| 0.0004 \| - \| \| 0.3353 \| 4200 \| 0.0007 \| - \| \| 0.3393 \| 4250 \| 0.0238 \| - \| \| 0.3433 \| 4300 \| 0.0002 \| - \| \| 0.3473 \| 4350 \| 0.0238 \| - \| \| 0.3513 \| 4400 \| 0.0003 \| - \| \| 0.3553 \| 4450 \| 0.0224 \| - \| \| 0.3593 \| 4500 \| 0.0006 \| - \| \| 0.3633 \| 4550 \| 0.0005 \| - \| \| 0.3673 \| 4600 \| 0.0004 \| - \| \| 0.3713 \| 4650 \| 0.0025 \| - \| \| 0.3752 \| 4700 \| 0.0003 \| - \| \| 0.3792 \| 4750 \| 0.0218 \| - \| \| 0.3832 \| 4800 \| 0.001 \| - \| \| 0.3872 \| 4850 \| 0.0004 \| - \| \| 0.3912 \| 4900 \| 0.0004 \| - \| \| 0.3952 \| 4950 \| 0.0161 \| - \| \| 0.3992 \| 5000 \| 0.0008 \| - \| \| 0.4032 \| 5050 \| 0.0024 \| - \| \| 0.4072 \| 5100 \| 0.0003 \| - \| \| 0.4112 \| 5150 \| 0.0002 \| - \| \| 0.4152 \| 5200 \| 0.0005 \| - \| \| 0.4192 \| 5250 \| 0.0021 \| - \| \| 0.4232 \| 5300 \| 0.0235 \| - \| \| 0.4271 \| 5350 \| 0.0035 \| - \| \| 0.4311 \| 5400 \| 0.0007 \| - \| \| 0.4351 \| 5450 \| 0.0007 \| - \| \| 0.4391 \| 5500 \| 0.0217 \| - \| \| 0.4431 \| 5550 \| 0.0006 \| - \| \| 0.4471 \| 5600 \| 0.0054 \| - \| \| 0.4511 \| 5650 \| 0.002 \| - \| \| 0.4551 \| 5700 \| 0.0013 \| - \| \| 0.4591 \| 5750 \| 0.0026 \| - \| \| 0.4631 \| 5800 \| 0.0051 \| - \| \| 0.4671 \| 5850 \| 0.0003 \| - \| \| 0.4711 \| 5900 \| 0.0003 \| - \| \| 0.4750 \| 5950 \| 0.0119 \| - \| \| 0.4790 \| 6000 \| 0.0011 \| - \| \| 0.4830 \| 6050 \| 0.0253 \| - \| \| 0.4870 \| 6100 \| 0.0244 \| - \| \| 0.4910 \| 6150 \| 0.0002 \| - \| \| 0.4950 \| 6200 \| 0.0002 \| - \| \| 0.4990 \| 6250 \| 0.0002 \| - \| \| 0.5030 \| 6300 \| 0.0167 \| - \| \| 0.5070 \| 6350 \| 0.0002 \| - \| \| 0.5110 \| 6400 \| 0.0003 \| - \| \| 0.5150 \| 6450 \| 0.0012 \| - \| \| 0.5190 \| 6500 \| 0.003 \| - \| \| 0.5230 \| 6550 \| 0.0003 \| - \| \| 0.5269 \| 6600 \| 0.0003 \| - \| \| 0.5309 \| 6650 \| 0.0006 \| - \| \| 0.5349 \| 6700 \| 0.0026 \| - \| \| 0.5389 \| 6750 \| 0.0004 \| - \| \| 0.5429 \| 6800 \| 0.0001 \| - \| \| 0.5469 \| 6850 \| 0.0002 \| - \| \| 0.5509 \| 6900 \| 0.0003 \| - \| \| 0.5549 \| 6950 \| 0.0028 \| - \| \| 0.5589 \| 7000 \| 0.0022 \| - \| \| 0.5629 \| 7050 \| 0.0007 \| - \| \| 0.5669 \| 7100 \| 0.0004 \| - \| \| 0.5709 \| 7150 \| 0.0002 \| - \| \| 0.5749 \| 7200 \| 0.0001 \| - \| \| 0.5788 \| 7250 \| 0.0122 \| - \| \| 0.5828 \| 7300 \| 0.0017 \| - \| \| 0.5868 \| 7350 \| 0.0001 \| - \| \| 0.5908 \| 7400 \| 0.0002 \| - \| \| 0.5948 \| 7450 \| 0.0001 \| - \| \| 0.5988 \| 7500 \| 0.0003 \| - \| \| 0.6028 \| 7550 \| 0.0011 \| - \| \| 0.6068 \| 7600 \| 0.0002 \| - \| \| 0.6108 \| 7650 \| 0.0003 \| - \| \| 0.6148 \| 7700 \| 0.0001 \| - \| \| 0.6188 \| 7750 \| 0.0001 \| - \| \| 0.6228 \| 7800 \| 0.0001 \| - \| \| 0.6267 \| 7850 \| 0.0002 \| - \| \| 0.6307 \| 7900 \| 0.0149 \| - \| \| 0.6347 \| 7950 \| 0.0106 \| - \| \| 0.6387 \| 8000 \| 0.0015 \| - \| \| 0.6427 \| 8050 \| 0.0001 \| - \| \| 0.6467 \| 8100 \| 0.0009 \| - \| \| 0.6507 \| 8150 \| 0.0015 \| - \| \| 0.6547 \| 8200 \| 0.0306 \| - \| \| 0.6587 \| 8250 \| 0.0054 \| - \| \| 0.6627 \| 8300 \| 0.0011 \| - \| \| 0.6667 \| 8350 \| 0.0003 \| - \| \| 0.6707 \| 8400 \| 0.0001 \| - \| \| 0.6747 \| 8450 \| 0.0024 \| - \| \| 0.6786 \| 8500 \| 0.0001 \| - \| \| 0.6826 \| 8550 \| 0.0001 \| - \| \| 0.6866 \| 8600 \| 0.0001 \| - \| \| 0.6906 \| 8650 \| 0.0072 \| - \| \| 0.6946 \| 8700 \| 0.0001 \| - \| \| 0.6986 \| 8750 \| 0.0002 \| - \| \| 0.7026 \| 8800 \| 0.0001 \| - \| \| 0.7066 \| 8850 \| 0.0243 \| - \| \| 0.7106 \| 8900 \| 0.0001 \| - \| \| 0.7146 \| 8950 \| 0.0001 \| - \| \| 0.7186 \| 9000 \| 0.0001 \| - \| \| 0.7226 \| 9050 \| 0.0001 \| - \| \| 0.7265 \| 9100 \| 0.0001 \| - \| \| 0.7305 \| 9150 \| 0.0001 \| - \| \| 0.7345 \| 9200 \| 0.0008 \| - \| \| 0.7385 \| 9250 \| 0.021 \| - \| \| 0.7425 \| 9300 \| 0.0229 \| - \| \| 0.7465 \| 9350 \| 0.0001 \| - \| \| 0.7505 \| 9400 \| 0.002 \| - \| \| 0.7545 \| 9450 \| 0.0008 \| - \| \| 0.7585 \| 9500 \| 0.0225 \| - \| \| 0.7625 \| 9550 \| 0.0001 \| - \| \| 0.7665 \| 9600 \| 0.0041 \| - \| \| 0.7705 \| 9650 \| 0.0012 \| - \| \| 0.7745 \| 9700 \| 0.0034 \| - \| \| 0.7784 \| 9750 \| 0.0011 \| - \| \| 0.7824 \| 9800 \| 0.0008 \| - \| \| 0.7864 \| 9850 \| 0.0101 \| - \| \| 0.7904 \| 9900 \| 0.0039 \| - \| \| 0.7944 \| 9950 \| 0.0001 \| - \| \| 0.7984 \| 10000 \| 0.0005 \| - \| \| 0.8024 \| 10050 \| 0.0011 \| - \| \| 0.8064 \| 10100 \| 0.0025 \| - \| \| 0.8104 \| 10150 \| 0.0001 \| - \| \| 0.8144 \| 10200 \| 0.0003 \| - \| \| 0.8184 \| 10250 \| 0.0002 \| - \| \| 0.8224 \| 10300 \| 0.0002 \| - \| \| 0.8263 \| 10350 \| 0.0001 \| - \| \| 0.8303 \| 10400 \| 0.0007 \| - \| \| 0.8343 \| 10450 \| 0.0005 \| - \| \| 0.8383 \| 10500 \| 0.0005 \| - \| \| 0.8423 \| 10550 \| 0.0001 \| - \| \| 0.8463 \| 10600 \| 0.0206 \| - \| \| 0.8503 \| 10650 \| 0.0023 \| - \| \| 0.8543 \| 10700 \| 0.0001 \| - \| \| 0.8583 \| 10750 \| 0.0001 \| - \| \| 0.8623 \| 10800 \| 0.0001 \| - \| \| 0.8663 \| 10850 \| 0.0001 \| - \| \| 0.8703 \| 10900 \| 0.0001 \| - \| \| 0.8743 \| 10950 \| 0.0002 \| - \| \| 0.8782 \| 11000 \| 0.0007 \| - \| \| 0.8822 \| 11050 \| 0.0025 \| - \| \| 0.8862 \| 11100 \| 0.0001 \| - \| \| 0.8902 \| 11150 \| 0.0001 \| - \| \| 0.8942 \| 11200 \| 0.0001 \| - \| \| 0.8982 \| 11250 \| 0.0001 \| - \| \| 0.9022 \| 11300 \| 0.0047 \| - \| \| 0.9062 \| 11350 \| 0.0001 \| - \| \| 0.9102 \| 11400 \| 0.0002 \| - \| \| 0.9142 \| 11450 \| 0.0001 \| - \| \| 0.9182 \| 11500 \| 0.0013 \| - \| \| 0.9222 \| 11550 \| 0.0011 \| - \| \| 0.9261 \| 11600 \| 0.0001 \| - \| \| 0.9301 \| 11650 \| 0.001 \| - \| \| 0.9341 \| 11700 \| 0.0145 \| - \| \| 0.9381 \| 11750 \| 0.0001 \| - \| \| 0.9421 \| 11800 \| 0.0156 \| - \| \| 0.9461 \| 11850 \| 0.0001 \| - \| \| 0.9501 \| 11900 \| 0.0016 \| - \| \| 0.9541 \| 11950 \| 0.0001 \| - \| \| 0.9581 \| 12000 \| 0.0011 \| - \| \| 0.9621 \| 12050 \| 0.002 \| - \| \| 0.9661 \| 12100 \| 0.0001 \| - \| \| 0.9701 \| 12150 \| 0.0004 \| - \| \| 0.9741 \| 12200 \| 0.0007 \| - \| \| 0.9780 \| 12250 \| 0.0014 \| - \| \| 0.9820 \| 12300 \| 0.0255 \| - \| \| 0.9860 \| 12350 \| 0.004 \| - \| \| 0.9900 \| 12400 \| 0.002 \| - \| \| 0.9940 \| 12450 \| 0.0146 \| - \| \| 0.9980 \| 12500 \| 0.0008 \| - \| \| 1.0 \| 12525 \| - \| 0.0588 \| \| 1.0020 \| 12550 \| 0.0175 \| - \| \| 1.0060 \| 12600 \| 0.0001 \| - \| \| 1.0100 \| 12650 \| 0.0006 \| - \| \| 1.0140 \| 12700 \| 0.0002 \| - \| \| 1.0180 \| 12750 \| 0.0149 \| - \| \| 1.0220 \| 12800 \| 0.0001 \| - \| \| 1.0259 \| 12850 \| 0.0001 \| - \| \| 1.0299 \| 12900 \| 0.0001 \| - \| \| 1.0339 \| 12950 \| 0.003 \| - \| \| 1.0379 \| 13000 \| 0.0003 \| - \| \| 1.0419 \| 13050 \| 0.0254 \| - \| \| 1.0459 \| 13100 \| 0.0001 \| - \| \| 1.0499 \| 13150 \| 0.0001 \| - \| \| 1.0539 \| 13200 \| 0.0001 \| - \| \| 1.0579 \| 13250 \| 0.0001 \| - \| \| 1.0619 \| 13300 \| 0.0003 \| - \| \| 1.0659 \| 13350 \| 0.0244 \| - \| \| 1.0699 \| 13400 \| 0.0001 \| - \| \| 1.0739 \| 13450 \| 0.0001 \| - \| \| 1.0778 \| 13500 \| 0.0175 \| - \| \| 1.0818 \| 13550 \| 0.0002 \| - \| \| 1.0858 \| 13600 \| 0.0002 \| - \| \| 1.0898 \| 13650 \| 0.0001 \| - \| \| 1.0938 \| 13700 \| 0.0001 \| - \| \| 1.0978 \| 13750 \| 0.0002 \| - \| \| 1.1018 \| 13800 \| 0.0001 \| - \| \| 1.1058 \| 13850 \| 0.0001 \| - \| \| 1.1098 \| 13900 \| 0.0001 \| - \| \| 1.1138 \| 13950 \| 0.0005 \| - \| \| 1.1178 \| 14000 \| 0.0001 \| - \| \| 1.1218 \| 14050 \| 0.0001 \| - \| \| 1.1257 \| 14100 \| 0.0002 \| - \| \| 1.1297 \| 14150 \| 0.0001 \| - \| \| 1.1337 \| 14200 \| 0.0002 \| - \| \| 1.1377 \| 14250 \| 0.0008 \| - \| \| 1.1417 \| 14300 \| 0.0001 \| - \| \| 1.1457 \| 14350 \| 0.0001 \| - \| \| 1.1497 \| 14400 \| 0.0013 \| - \| \| 1.1537 \| 14450 \| 0.0001 \| - \| \| 1.1577 \| 14500 \| 0.0001 \| - \| \| 1.1617 \| 14550 \| 0.0004 \| - \| \| 1.1657 \| 14600 \| 0.0001 \| - \| \| 1.1697 \| 14650 \| 0.0001 \| - \| \| 1.1737 \| 14700 \| 0.001 \| - \| \| 1.1776 \| 14750 \| 0.0156 \| - \| \| 1.1816 \| 14800 \| 0.0001 \| - \| \| 1.1856 \| 14850 \| 0.0003 \| - \| \| 1.1896 \| 14900 \| 0.0045 \| - \| \| 1.1936 \| 14950 \| 0.0011 \| - \| \| 1.1976 \| 15000 \| 0.0015 \| - \| \| 1.2016 \| 15050 \| 0.0017 \| - \| \| 1.2056 \| 15100 \| 0.017 \| - \| \| 1.2096 \| 15150 \| 0.0001 \| - \| \| 1.2136 \| 15200 \| 0.0011 \| - \| \| 1.2176 \| 15250 \| 0.0003 \| - \| \| 1.2216 \| 15300 \| 0.0001 \| - \| \| 1.2255 \| 15350 \| 0.0001 \| - \| \| 1.2295 \| 15400 \| 0.0005 \| - \| \| 1.2335 \| 15450 \| 0.0144 \| - \| \| 1.2375 \| 15500 \| 0.0001 \| - \| \| 1.2415 \| 15550 \| 0.0001 \| - \| \| 1.2455 \| 15600 \| 0.0191 \| - \| \| 1.2495 \| 15650 \| 0.0001 \| - \| \| 1.2535 \| 15700 \| 0.0001 \| - \| \| 1.2575 \| 15750 \| 0.0001 \| - \| \| 1.2615 \| 15800 \| 0.0001 \| - \| \| 1.2655 \| 15850 \| 0.0008 \| - \| \| 1.2695 \| 15900 \| 0.0005 \| - \| \| 1.2735 \| 15950 \| 0.013 \| - \| \| 1.2774 \| 16000 \| 0.0001 \| - \| \| 1.2814 \| 16050 \| 0.0201 \| - \| \| 1.2854 \| 16100 \| 0.0008 \| - \| \| 1.2894 \| 16150 \| 0.0001 \| - \| \| 1.2934 \| 16200 \| 0.0001 \| - \| \| 1.2974 \| 16250 \| 0.0001 \| - \| \| 1.3014 \| 16300 \| 0.0001 \| - \| \| 1.3054 \| 16350 \| 0.0 \| - \| \| 1.3094 \| 16400 \| 0.0118 \| - \| \| 1.3134 \| 16450 \| 0.0253 \| - \| \| 1.3174 \| 16500 \| 0.0001 \| - \| \| 1.3214 \| 16550 \| 0.0012 \| - \| \| 1.3253 \| 16600 \| 0.0017 \| - \| \| 1.3293 \| 16650 \| 0.0001 \| - \| \| 1.3333 \| 16700 \| 0.0094 \| - \| \| 1.3373 \| 16750 \| 0.0001 \| - \| \| 1.3413 \| 16800 \| 0.0243 \| - \| \| 1.3453 \| 16850 \| 0.049 \| - \| \| 1.3493 \| 16900 \| 0.0001 \| - \| \| 1.3533 \| 16950 \| 0.0247 \| - \| \| 1.3573 \| 17000 \| 0.0001 \| - \| \| 1.3613 \| 17050 \| 0.0001 \| - \| \| 1.3653 \| 17100 \| 0.0001 \| - \| \| 1.3693 \| 17150 \| 0.0246 \| - \| \| 1.3733 \| 17200 \| 0.0001 \| - \| \| 1.3772 \| 17250 \| 0.0245 \| - \| \| 1.3812 \| 17300 \| 0.0012 \| - \| \| 1.3852 \| 17350 \| 0.0001 \| - \| \| 1.3892 \| 17400 \| 0.0001 \| - \| \| 1.3932 \| 17450 \| 0.0001 \| - \| \| 1.3972 \| 17500 \| 0.0001 \| - \| \| 1.4012 \| 17550 \| 0.0001 \| - \| \| 1.4052 \| 17600 \| 0.0002 \| - \| \| 1.4092 \| 17650 \| 0.0001 \| - \| \| 1.4132 \| 17700 \| 0.0001 \| - \| \| 1.4172 \| 17750 \| 0.0039 \| - \| \| 1.4212 \| 17800 \| 0.0012 \| - \| \| 1.4251 \| 17850 \| 0.0043 \| - \| \| 1.4291 \| 17900 \| 0.0001 \| - \| \| 1.4331 \| 17950 \| 0.0001 \| - \| \| 1.4371 \| 18000 \| 0.0001 \| - \| \| 1.4411 \| 18050 \| 0.0001 \| - \| \| 1.4451 \| 18100 \| 0.0043 \| - \| \| 1.4491 \| 18150 \| 0.0023 \| - \| \| 1.4531 \| 18200 \| 0.0001 \| - \| \| 1.4571 \| 18250 \| 0.0002 \| - \| \| 1.4611 \| 18300 \| 0.0001 \| - \| \| 1.4651 \| 18350 \| 0.0001 \| - \| \| 1.4691 \| 18400 \| 0.0001 \| - \| \| 1.4731 \| 18450 \| 0.0099 \| - \| \| 1.4770 \| 18500 \| 0.0001 \| - \| \| 1.4810 \| 18550 \| 0.0001 \| - \| \| 1.4850 \| 18600 \| 0.03 \| - \| \| 1.4890 \| 18650 \| 0.0001 \| - \| \| 1.4930 \| 18700 \| 0.0014 \| - \| \| 1.4970 \| 18750 \| 0.0 \| - \| \| 1.5010 \| 18800 \| 0.0246 \| - \| \| 1.5050 \| 18850 \| 0.0001 \| - \| \| 1.5090 \| 18900 \| 0.0001 \| - \| \| 1.5130 \| 18950 \| 0.0001 \| - \| \| 1.5170 \| 19000 \| 0.0001 \| - \| \| 1.5210 \| 19050 \| 0.0001 \| - \| \| 1.5250 \| 19100 \| 0.0001 \| - \| \| 1.5289 \| 19150 \| 0.0001 \| - \| \| 1.5329 \| 19200 \| 0.0032 \| - \| \| 1.5369 \| 19250 \| 0.0001 \| - \| \| 1.5409 \| 19300 \| 0.0001 \| - \| \| 1.5449 \| 19350 \| 0.0001 \| - \| \| 1.5489 \| 19400 \| 0.025 \| - \| \| 1.5529 \| 19450 \| 0.0028 \| - \| \| 1.5569 \| 19500 \| 0.0028 \| - \| \| 1.5609 \| 19550 \| 0.0001 \| - \| \| 1.5649 \| 19600 \| 0.005 \| - \| \| 1.5689 \| 19650 \| 0.0024 \| - \| \| 1.5729 \| 19700 \| 0.0001 \| - \| \| 1.5768 \| 19750 \| 0.0 \| - \| \| 1.5808 \| 19800 \| 0.0001 \| - \| \| 1.5848 \| 19850 \| 0.0001 \| - \| \| 1.5888 \| 19900 \| 0.0001 \| - \| \| 1.5928 \| 19950 \| 0.0001 \| - \| \| 1.5968 \| 20000 \| 0.0001 \| - \| \| 1.6008 \| 20050 \| 0.0001 \| - \| \| 1.6048 \| 20100 \| 0.0001 \| - \| \| 1.6088 \| 20150 \| 0.0001 \| - \| \| 1.6128 \| 20200 \| 0.0 \| - \| \| 1.6168 \| 20250 \| 0.0001 \| - \| \| 1.6208 \| 20300 \| 0.0001 \| - \| \| 1.6248 \| 20350 \| 0.0028 \| - \| \| 1.6287 \| 20400 \| 0.0019 \| - \| \| 1.6327 \| 20450 \| 0.0115 \| - \| \| 1.6367 \| 20500 \| 0.0029 \| - \| \| 1.6407 \| 20550 \| 0.0035 \| - \| \| 1.6447 \| 20600 \| 0.0005 \| - \| \| 1.6487 \| 20650 \| 0.0007 \| - \| \| 1.6527 \| 20700 \| 0.0374 \| - \| \| 1.6567 \| 20750 \| 0.0064 \| - \| \| 1.6607 \| 20800 \| 0.004 \| - \| \| 1.6647 \| 20850 \| 0.0009 \| - \| \| 1.6687 \| 20900 \| 0.0 \| - \| \| 1.6727 \| 20950 \| 0.0017 \| - \| \| 1.6766 \| 21000 \| 0.0001 \| - \| \| 1.6806 \| 21050 \| 0.0001 \| - \| \| 1.6846 \| 21100 \| 0.0001 \| - \| \| 1.6886 \| 21150 \| 0.0083 \| - \| \| 1.6926 \| 21200 \| 0.0001 \| - \| \| 1.6966 \| 21250 \| 0.0001 \| - \| \| 1.7006 \| 21300 \| 0.0001 \| - \| \| 1.7046 \| 21350 \| 0.0009 \| - \| \| 1.7086 \| 21400 \| 0.0001 \| - \| \| 1.7126 \| 21450 \| 0.0001 \| - \| \| 1.7166 \| 21500 \| 0.0015 \| - \| \| 1.7206 \| 21550 \| 0.0001 \| - \| \| 1.7246 \| 21600 \| 0.0001 \| - \| \| 1.7285 \| 21650 \| 0.0001 \| - \| \| 1.7325 \| 21700 \| 0.0007 \| - \| \| 1.7365 \| 21750 \| 0.0001 \| - \| \| 1.7405 \| 21800 \| 0.0213 \| - \| \| 1.7445 \| 21850 \| 0.0007 \| - \| \| 1.7485 \| 21900 \| 0.0029 \| - \| \| 1.7525 \| 21950 \| 0.0007 \| - \| \| 1.7565 \| 22000 \| 0.0025 \| - \| \| 1.7605 \| 22050 \| 0.0002 \| - \| \| 1.7645 \| 22100 \| 0.0173 \| - \| \| 1.7685 \| 22150 \| 0.0012 \| - \| \| 1.7725 \| 22200 \| 0.0027 \| - \| \| 1.7764 \| 22250 \| 0.0009 \| - \| \| 1.7804 \| 22300 \| 0.0013 \| - \| \| 1.7844 \| 22350 \| 0.024 \| - \| \| 1.7884 \| 22400 \| 0.0043 \| - \| \| 1.7924 \| 22450 \| 0.0001 \| - \| \| 1.7964 \| 22500 \| 0.0001 \| - \| \| 1.8004 \| 22550 \| 0.0109 \| - \| \| 1.8044 \| 22600 \| 0.0001 \| - \| \| 1.8084 \| 22650 \| 0.0002 \| - \| \| 1.8124 \| 22700 \| 0.0246 \| - \| \| 1.8164 \| 22750 \| 0.0001 \| - \| \| 1.8204 \| 22800 \| 0.0002 \| - \| \| 1.8244 \| 22850 \| 0.0248 \| - \| \| 1.8283 \| 22900 \| 0.0001 \| - \| \| 1.8323 \| 22950 \| 0.0119 \| - \| \| 1.8363 \| 23000 \| 0.0254 \| - \| \| 1.8403 \| 23050 \| 0.1233 \| - \| \| 1.8443 \| 23100 \| 0.0003 \| - \| \| 1.8483 \| 23150 \| 0.0001 \| - \| \| 1.8523 \| 23200 \| 0.0 \| - \| \| 1.8563 \| 23250 \| 0.0 \| - \| \| 1.8603 \| 23300 \| 0.0001 \| - \| \| 1.8643 \| 23350 \| 0.0001 \| - \| \| 1.8683 \| 23400 \| 0.0001 \| - \| \| 1.8723 \| 23450 \| 0.0001 \| - \| \| 1.8762 \| 23500 \| 0.0001 \| - \| \| 1.8802 \| 23550 \| 0.0017 \| - \| \| 1.8842 \| 23600 \| 0.0 \| - \| \| 1.8882 \| 23650 \| 0.0001 \| - \| \| 1.8922 \| 23700 \| 0.0 \| - \| \| 1.8962 \| 23750 \| 0.0001 \| - \| \| 1.9002 \| 23800 \| 0.0055 \| - \| \| 1.9042 \| 23850 \| 0.0001 \| - \| \| 1.9082 \| 23900 \| 0.0003 \| - \| \| 1.9122 \| 23950 \| 0.0002 \| - \| \| 1.9162 \| 24000 \| 0.0007 \| - \| \| 1.9202 \| 24050 \| 0.0011 \| - \| \| 1.9242 \| 24100 \| 0.0001 \| - \| \| 1.9281 \| 24150 \| 0.0001 \| - \| \| 1.9321 \| 24200 \| 0.0001 \| - \| \| 1.9361 \| 24250 \| 0.0001 \| - \| \| 1.9401 \| 24300 \| 0.0192 \| - \| \| 1.9441 \| 24350 \| 0.0001 \| - \| \| 1.9481 \| 24400 \| 0.0008 \| - \| \| 1.9521 \| 24450 \| 0.0 \| - \| \| 1.9561 \| 24500 \| 0.0004 \| - \| \| 1.9601 \| 24550 \| 0.0001 \| - \| \| 1.9641 \| 24600 \| 0.0001 \| - \| \| 1.9681 \| 24650 \| 0.0001 \| - \| \| 1.9721 \| 24700 \| 0.0001 \| - \| \| 1.9760 \| 24750 \| 0.0013 \| - \| \| 1.9800 \| 24800 \| 0.0129 \| - \| \| 1.9840 \| 24850 \| 0.0024 \| - \| \| 1.9880 \| 24900 \| 0.0015 \| - \| \| 1.9920 \| 24950 \| 0.0196 \| - \| \| 1.9960 \| 25000 \| 0.0164 \| - \| \| 2.0 \| 25050 \| 0.002 \| 0.0769 \| \| 2.0040 \| 25100 \| 0.0001 \| - \| \| 2.0080 \| 25150 \| 0.0156 \| - \| \| 2.0120 \| 25200 \| 0.0 \| - \| \| 2.0160 \| 25250 \| 0.0169 \| - \| \| 2.0200 \| 25300 \| 0.0023 \| - \| \| 2.0240 \| 25350 \| 0.0001 \| - \| \| 2.0279 \| 25400 \| 0.0 \| - \| \| 2.0319 \| 25450 \| 0.001 \| - \| \| 2.0359 \| 25500 \| 0.0152 \| - \| \| 2.0399 \| 25550 \| 0.0001 \| - \| \| 2.0439 \| 25600 \| 0.001 \| - \| \| 2.0479 \| 25650 \| 0.0001 \| - \| \| 2.0519 \| 25700 \| 0.002 \| - \| \| 2.0559 \| 25750 \| 0.0006 \| - \| \| 2.0599 \| 25800 \| 0.0031 \| - \| \| 2.0639 \| 25850 \| 0.0016 \| - \| \| 2.0679 \| 25900 \| 0.0 \| - \| \| 2.0719 \| 25950 \| 0.0001 \| - \| \| 2.0758 \| 26000 \| 0.0001 \| - \| \| 2.0798 \| 26050 \| 0.0159 \| - \| \| 2.0838 \| 26100 \| 0.0005 \| - \| \| 2.0878 \| 26150 \| 0.0001 \| - \| \| 2.0918 \| 26200 \| 0.0001 \| - \| \| 2.0958 \| 26250 \| 0.0006 \| - \| \| 2.0998 \| 26300 \| 0.0008 \| - \| \| 2.1038 \| 26350 \| 0.0 \| - \| \| 2.1078 \| 26400 \| 0.0 \| - \| \| 2.1118 \| 26450 \| 0.0183 \| - \| \| 2.1158 \| 26500 \| 0.0001 \| - \| \| 2.1198 \| 26550 \| 0.0167 \| - \| \| 2.1238 \| 26600 \| 0.0001 \| - \| \| 2.1277 \| 26650 \| 0.0014 \| - \| \| 2.1317 \| 26700 \| 0.0003 \| - \| \| 2.1357 \| 26750 \| 0.0014 \| - \| \| 2.1397 \| 26800 \| 0.0001 \| - \| \| 2.1437 \| 26850 \| 0.0001 \| - \| \| 2.1477 \| 26900 \| 0.0011 \| - \| \| 2.1517 \| 26950 \| 0.0 \| - \| \| 2.1557 \| 27000 \| 0.0001 \| - \| \| 2.1597 \| 27050 \| 0.0001 \| - \| \| 2.1637 \| 27100 \| 0.0007 \| - \| \| 2.1677 \| 27150 \| 0.0001 \| - \| \| 2.1717 \| 27200 \| 0.0 \| - \| \| 2.1756 \| 27250 \| 0.0001 \| - \| \| 2.1796 \| 27300 \| 0.0005 \| - \| \| 2.1836 \| 27350 \| 0.0 \| - \| \| 2.1876 \| 27400 \| 0.0002 \| - \| \| 2.1916 \| 27450 \| 0.0001 \| - \| \| 2.1956 \| 27500 \| 0.0001 \| - \| \| 2.1996 \| 27550 \| 0.025 \| - \| \| 2.2036 \| 27600 \| 0.0001 \| - \| \| 2.2076 \| 27650 \| 0.0001 \| - \| \| 2.2116 \| 27700 \| 0.0001 \| - \| \| 2.2156 \| 27750 \| 0.0001 \| - \| \| 2.2196 \| 27800 \| 0.0001 \| - \| \| 2.2236 \| 27850 \| 0.0001 \| - \| \| 2.2275 \| 27900 \| 0.0001 \| - \| \| 2.2315 \| 27950 \| 0.0 \| - \| \| 2.2355 \| 28000 \| 0.0001 \| - \| \| 2.2395 \| 28050 \| 0.0001 \| - \| \| 2.2435 \| 28100 \| 0.0245 \| - \| \| 2.2475 \| 28150 \| 0.0001 \| - \| \| 2.2515 \| 28200 \| 0.0251 \| - \| \| 2.2555 \| 28250 \| 0.0001 \| - \| \| 2.2595 \| 28300 \| 0.0497 \| - \| \| 2.2635 \| 28350 \| 0.0002 \| - \| \| 2.2675 \| 28400 \| 0.0487 \| - \| \| 2.2715 \| 28450 \| 0.0703 \| - \| \| 2.2754 \| 28500 \| 0.0248 \| - \| \| 2.2794 \| 28550 \| 0.0001 \| - \| \| 2.2834 \| 28600 \| 0.0245 \| - \| \| 2.2874 \| 28650 \| 0.0004 \| - \| \| 2.2914 \| 28700 \| 0.0001 \| - \| \| 2.2954 \| 28750 \| 0.0001 \| - \| \| 2.2994 \| 28800 \| 0.0 \| - \| \| 2.3034 \| 28850 \| 0.0002 \| - \| \| 2.3074 \| 28900 \| 0.0489 \| - \| \| 2.3114 \| 28950 \| 0.0245 \| - \| \| 2.3154 \| 29000 \| 0.0002 \| - \| \| 2.3194 \| 29050 \| 0.0001 \| - \| \| 2.3234 \| 29100 \| 0.0001 \| - \| \| 2.3273 \| 29150 \| 0.0247 \| - \| \| 2.3313 \| 29200 \| 0.0003 \| - \| \| 2.3353 \| 29250 \| 0.0001 \| - \| \| 2.3393 \| 29300 \| 0.0001 \| - \| \| 2.3433 \| 29350 \| 0.0246 \| - \| \| 2.3473 \| 29400 \| 0.0246 \| - \| \| 2.3513 \| 29450 \| 0.0246 \| - \| \| 2.3553 \| 29500 \| 0.0001 \| - \| \| 2.3593 \| 29550 \| 0.0001 \| - \| \| 2.3633 \| 29600 \| 0.0001 \| - \| \| 2.3673 \| 29650 \| 0.0246 \| - \| \| 2.3713 \| 29700 \| 0.0 \| - \| \| 2.3752 \| 29750 \| 0.0246 \| - \| \| 2.3792 \| 29800 \| 0.0001 \| - \| \| 2.3832 \| 29850 \| 0.0001 \| - \| \| 2.3872 \| 29900 \| 0.0001 \| - \| \| 2.3912 \| 29950 \| 0.0002 \| - \| \| 2.3952 \| 30000 \| 0.0248 \| - \| \| 2.3992 \| 30050 \| 0.0002 \| - \| \| 2.4032 \| 30100 \| 0.0001 \| - \| \| 2.4072 \| 30150 \| 0.0001 \| - \| \| 2.4112 \| 30200 \| 0.0001 \| - \| \| 2.4152 \| 30250 \| 0.0001 \| - \| \| 2.4192 \| 30300 \| 0.0001 \| - \| \| 2.4232 \| 30350 \| 0.0245 \| - \| \| 2.4271 \| 30400 \| 0.0001 \| - \| \| 2.4311 \| 30450 \| 0.0001 \| - \| \| 2.4351 \| 30500 \| 0.0001 \| - \| \| 2.4391 \| 30550 \| 0.0 \| - \| \| 2.4431 \| 30600 \| 0.0001 \| - \| \| 2.4471 \| 30650 \| 0.0001 \| - \| \| 2.4511 \| 30700 \| 0.0001 \| - \| \| 2.4551 \| 30750 \| 0.0001 \| - \| \| 2.4591 \| 30800 \| 0.0001 \| - \| \| 2.4631 \| 30850 \| 0.0002 \| - \| \| 2.4671 \| 30900 \| 0.0001 \| - \| \| 2.4711 \| 30950 \| 0.0245 \| - \| \| 2.4750 \| 31000 \| 0.0001 \| - \| \| 2.4790 \| 31050 \| 0.0249 \| - \| \| 2.4830 \| 31100 \| 0.0246 \| - \| \| 2.4870 \| 31150 \| 0.0001 \| - \| \| 2.4910 \| 31200 \| 0.0246 \| - \| \| 2.4950 \| 31250 \| 0.0001 \| - \| \| 2.4990 \| 31300 \| 0.0247 \| - \| \| 2.5030 \| 31350 \| 0.0001 \| - \| \| 2.5070 \| 31400 \| 0.0001 \| - \| \| 2.5110 \| 31450 \| 0.0003 \| - \| \| 2.5150 \| 31500 \| 0.0002 \| - \| \| 2.5190 \| 31550 \| 0.0002 \| - \| \| 2.5230 \| 31600 \| 0.0001 \| - \| \| 2.5269 \| 31650 \| 0.0001 \| - \| \| 2.5309 \| 31700 \| 0.0003 \| - \| \| 2.5349 \| 31750 \| 0.0001 \| - \| \| 2.5389 \| 31800 \| 0.0001 \| - \| \| 2.5429 \| 31850 \| 0.0001 \| - \| \| 2.5469 \| 31900 \| 0.0001 \| - \| \| 2.5509 \| 31950 \| 0.0493 \| - \| \| 2.5549 \| 32000 \| 0.0001 \| - \| \| 2.5589 \| 32050 \| 0.0001 \| - \| \| 2.5629 \| 32100 \| 0.0003 \| - \| \| 2.5669 \| 32150 \| 0.0001 \| - \| \| 2.5709 \| 32200 \| 0.025 \| - \| \| 2.5749 \| 32250 \| 0.0001 \| - \| \| 2.5788 \| 32300 \| 0.0249 \| - \| \| 2.5828 \| 32350 \| 0.0001 \| - \| \| 2.5868 \| 32400 \| 0.0001 \| - \| \| 2.5908 \| 32450 \| 0.0001 \| - \| \| 2.5948 \| 32500 \| 0.0001 \| - \| \| 2.5988 \| 32550 \| 0.0004 \| - \| \| 2.6028 \| 32600 \| 0.0001 \| - \| \| 2.6068 \| 32650 \| 0.0001 \| - \| \| 2.6108 \| 32700 \| 0.0001 \| - \| \| 2.6148 \| 32750 \| 0.0001 \| - \| \| 2.6188 \| 32800 \| 0.0001 \| - \| \| 2.6228 \| 32850 \| 0.0001 \| - \| \| 2.6267 \| 32900 \| 0.0001 \| - \| \| 2.6307 \| 32950 \| 0.0492 \| - \| \| 2.6347 \| 33000 \| 0.0001 \| - \| \| 2.6387 \| 33050 \| 0.0001 \| - \| \| 2.6427 \| 33100 \| 0.0 \| - \| \| 2.6467 \| 33150 \| 0.0001 \| - \| \| 2.6507 \| 33200 \| 0.0247 \| - \| \| 2.6547 \| 33250 \| 0.0001 \| - \| \| 2.6587 \| 33300 \| 0.0001 \| - \| \| 2.6627 \| 33350 \| 0.0001 \| - \| \| 2.6667 \| 33400 \| 0.0001 \| - \| \| 2.6707 \| 33450 \| 0.0001 \| - \| \| 2.6747 \| 33500 \| 0.0001 \| - \| \| 2.6786 \| 33550 \| 0.0001 \| - \| \| 2.6826 \| 33600 \| 0.0001 \| - \| \| 2.6866 \| 33650 \| 0.0002 \| - \| \| 2.6906 \| 33700 \| 0.0001 \| - \| \| 2.6946 \| 33750 \| 0.0001 \| - \| \| 2.6986 \| 33800 \| 0.0001 \| - \| \| 2.7026 \| 33850 \| 0.0001 \| - \| \| 2.7066 \| 33900 \| 0.0254 \| - \| \| 2.7106 \| 33950 \| 0.0001 \| - \| \| 2.7146 \| 34000 \| 0.0001 \| - \| \| 2.7186 \| 34050 \| 0.0001 \| - \| \| 2.7226 \| 34100 \| 0.0001 \| - \| \| 2.7265 \| 34150 \| 0.0001 \| - \| \| 2.7305 \| 34200 \| 0.0001 \| - \| \| 2.7345 \| 34250 \| 0.0002 \| - \| \| 2.7385 \| 34300 \| 0.0498 \| - \| \| 2.7425 \| 34350 \| 0.0001 \| - \| \| 2.7465 \| 34400 \| 0.0001 \| - \| \| 2.7505 \| 34450 \| 0.0001 \| - \| \| 2.7545 \| 34500 \| 0.0001 \| - \| \| 2.7585 \| 34550 \| 0.0248 \| - \| \| 2.7625 \| 34600 \| 0.0 \| - \| \| 2.7665 \| 34650 \| 0.0001 \| - \| \| 2.7705 \| 34700 \| 0.0001 \| - \| \| 2.7745 \| 34750 \| 0.0001 \| - \| \| 2.7784 \| 34800 \| 0.0001 \| - \| \| 2.7824 \| 34850 \| 0.0247 \| - \| \| 2.7864 \| 34900 \| 0.0001 \| - \| \| 2.7904 \| 34950 \| 0.0001 \| - \| \| 2.7944 \| 35000 \| 0.0001 \| - \| \| 2.7984 \| 35050 \| 0.0001 \| - \| \| 2.8024 \| 35100 \| 0.0001 \| - \| \| 2.8064 \| 35150 \| 0.0001 \| - \| \| 2.8104 \| 35200 \| 0.0001 \| - \| \| 2.8144 \| 35250 \| 0.0001 \| - \| \| 2.8184 \| 35300 \| 0.0001 \| - \| \| 2.8224 \| 35350 \| 0.0001 \| - \| \| 2.8263 \| 35400 \| 0.0 \| - \| \| 2.8303 \| 35450 \| 0.0001 \| - \| \| 2.8343 \| 35500 \| 0.0 \| - \| \| 2.8383 \| 35550 \| 0.0 \| - \| \| 2.8423 \| 35600 \| 0.0001 \| - \| \| 2.8463 \| 35650 \| 0.0254 \| - \| \| 2.8503 \| 35700 \| 0.0001 \| - \| \| 2.8543 \| 35750 \| 0.0001 \| - \| \| 2.8583 \| 35800 \| 0.0001 \| - \| \| 2.8623 \| 35850 \| 0.0 \| - \| \| 2.8663 \| 35900 \| 0.0001 \| - \| \| 2.8703 \| 35950 \| 0.0001 \| - \| \| 2.8743 \| 36000 \| 0.0002 \| - \| \| 2.8782 \| 36050 \| 0.0001 \| - \| \| 2.8822 \| 36100 \| 0.0001 \| - \| \| 2.8862 \| 36150 \| 0.0 \| - \| \| 2.8902 \| 36200 \| 0.0001 \| - \| \| 2.8942 \| 36250 \| 0.0001 \| - \| \| 2.8982 \| 36300 \| 0.0001 \| - \| \| 2.9022 \| 36350 \| 0.0001 \| - \| \| 2.9062 \| 36400 \| 0.0001 \| - \| \| 2.9102 \| 36450 \| 0.0001 \| - \| \| 2.9142 \| 36500 \| 0.0001 \| - \| \| 2.9182 \| 36550 \| 0.0001 \| - \| \| 2.9222 \| 36600 \| 0.0001 \| - \| \| 2.9261 \| 36650 \| 0.0002 \| - \| \| 2.9301 \| 36700 \| 0.0001 \| - \| \| 2.9341 \| 36750 \| 0.0248 \| - \| \| 2.9381 \| 36800 \| 0.0245 \| - \| \| 2.9421 \| 36850 \| 0.0001 \| - \| \| 2.9461 \| 36900 \| 0.0 \| - \| \| 2.9501 \| 36950 \| 0.0001 \| - \| \| 2.9541 \| 37000 \| 0.0001 \| - \| \| 2.9581 \| 37050 \| 0.0001 \| - \| \| 2.9621 \| 37100 \| 0.0001 \| - \| \| 2.9661 \| 37150 \| 0.0001 \| - \| \| 2.9701 \| 37200 \| 0.0001 \| - \| \| 2.9741 \| 37250 \| 0.0001 \| - \| \| 2.9780 \| 37300 \| 0.0 \| - \| \| 2.9820 \| 37350 \| 0.0503 \| - \| \| 2.9860 \| 37400 \| 0.0001 \| - \| \| 2.9900 \| 37450 \| 0.0246 \| - \| \| 2.9940 \| 37500 \| 0.0001 \| - \| \| 2.9980 \| 37550 \| 0.0001 \| - \| \| 3.0 \| 37575 \| - \| 0.0396 \| \| 3.0020 \| 37600 \| 0.0248 \| - \| \| 3.0060 \| 37650 \| 0.0001 \| - \| \| 3.0100 \| 37700 \| 0.0001 \| - \| \| 3.0140 \| 37750 \| 0.0245 \| - \| \| 3.0180 \| 37800 \| 0.0002 \| - \| \| 3.0220 \| 37850 \| 0.0 \| - \| \| 3.0259 \| 37900 \| 0.0001 \| - \| \| 3.0299 \| 37950 \| 0.0001 \| - \| \| 3.0339 \| 38000 \| 0.0003 \| - \| \| 3.0379 \| 38050 \| 0.0001 \| - \| \| 3.0419 \| 38100 \| 0.0001 \| - \| \| 3.0459 \| 38150 \| 0.0001 \| - \| \| 3.0499 \| 38200 \| 0.0001 \| - \| \| 3.0539 \| 38250 \| 0.0001 \| - \| \| 3.0579 \| 38300 \| 0.0001 \| - \| \| 3.0619 \| 38350 \| 0.0002 \| - \| \| 3.0659 \| 38400 \| 0.0251 \| - \| \| 3.0699 \| 38450 \| 0.0001 \| - \| \| 3.0739 \| 38500 \| 0.0001 \| - \| \| 3.0778 \| 38550 \| 0.0001 \| - \| \| 3.0818 \| 38600 \| 0.0001 \| - \| \| 3.0858 \| 38650 \| 0.0001 \| - \| \| 3.0898 \| 38700 \| 0.0001 \| - \| \| 3.0938 \| 38750 \| 0.0001 \| - \| \| 3.0978 \| 38800 \| 0.0001 \| - \| \| 3.1018 \| 38850 \| 0.0001 \| - \| \| 3.1058 \| 38900 \| 0.0001 \| - \| \| 3.1098 \| 38950 \| 0.0 \| - \| \| 3.1138 \| 39000 \| 0.0001 \| - \| \| 3.1178 \| 39050 \| 0.0001 \| - \| \| 3.1218 \| 39100 \| 0.0001 \| - \| \| 3.1257 \| 39150 \| 0.0001 \| - \| \| 3.1297 \| 39200 \| 0.0001 \| - \| \| 3.1337 \| 39250 \| 0.0001 \| - \| \| 3.1377 \| 39300 \| 0.0 \| - \| \| 3.1417 \| 39350 \| 0.0001 \| - \| \| 3.1457 \| 39400 \| 0.0002 \| - \| \| 3.1497 \| 39450 \| 0.0001 \| - \| \| 3.1537 \| 39500 \| 0.0002 \| - \| \| 3.1577 \| 39550 \| 0.0001 \| - \| \| 3.1617 \| 39600 \| 0.0717 \| - \| \| 3.1657 \| 39650 \| 0.0001 \| - \| \| 3.1697 \| 39700 \| 0.0001 \| - \| \| 3.1737 \| 39750 \| 0.0004 \| - \| \| 3.1776 \| 39800 \| 0.0244 \| - \| \| 3.1816 \| 39850 \| 0.0001 \| - \| \| 3.1856 \| 39900 \| 0.0239 \| - \| \| 3.1896 \| 39950 \| 0.0245 \| - \| \| 3.1936 \| 40000 \| 0.0245 \| - \| \| 3.1976 \| 40050 \| 0.0001 \| - \| \| 3.2016 \| 40100 \| 0.0184 \| - \| \| 3.2056 \| 40150 \| 0.0246 \| - \| \| 3.2096 \| 40200 \| 0.0001 \| - \| \| 3.2136 \| 40250 \| 0.0001 \| - \| \| 3.2176 \| 40300 \| 0.0001 \| - \| \| 3.2216 \| 40350 \| 0.0001 \| - \| \| 3.2255 \| 40400 \| 0.0001 \| - \| \| 3.2295 \| 40450 \| 0.0002 \| - \| \| 3.2335 \| 40500 \| 0.0248 \| - \| \| 3.2375 \| 40550 \| 0.0001 \| - \| \| 3.2415 \| 40600 \| 0.0244 \| - \| \| 3.2455 \| 40650 \| 0.0002 \| - \| \| 3.2495 \| 40700 \| 0.0001 \| - \| \| 3.2535 \| 40750 \| 0.0001 \| - \| \| 3.2575 \| 40800 \| 0.0 \| - \| \| 3.2615 \| 40850 \| 0.0 \| - \| \| 3.2655 \| 40900 \| 0.0001 \| - \| \| 3.2695 \| 40950 \| 0.0247 \| - \| \| 3.2735 \| 41000 \| 0.0001 \| - \| \| 3.2774 \| 41050 \| 0.0001 \| - \| \| 3.2814 \| 41100 \| 0.0246 \| - \| \| 3.2854 \| 41150 \| 0.0001 \| - \| \| 3.2894 \| 41200 \| 0.0001 \| - \| \| 3.2934 \| 41250 \| 0.0001 \| - \| \| 3.2974 \| 41300 \| 0.0001 \| - \| \| 3.3014 \| 41350 \| 0.0001 \| - \| \| 3.3054 \| 41400 \| 0.0246 \| - \| \| 3.3094 \| 41450 \| 0.0246 \| - \| \| 3.3134 \| 41500 \| 0.0246 \| - \| \| 3.3174 \| 41550 \| 0.0001 \| - \| \| 3.3214 \| 41600 \| 0.0003 \| - \| \| 3.3253 \| 41650 \| 0.0001 \| - \| \| 3.3293 \| 41700 \| 0.0001 \| - \| \| 3.3333 \| 41750 \| 0.025 \| - \| \| 3.3373 \| 41800 \| 0.0 \| - \| \| 3.3413 \| 41850 \| 0.0245 \| - \| \| 3.3453 \| 41900 \| 0.0001 \| - \| \| 3.3493 \| 41950 \| 0.0246 \| - \| \| 3.3533 \| 42000 \| 0.0001 \| - \| \| 3.3573 \| 42050 \| 0.0001 \| - \| \| 3.3613 \| 42100 \| 0.0001 \| - \| \| 3.3653 \| 42150 \| 0.0001 \| - \| \| 3.3693 \| 42200 \| 0.0248 \| - \| \| 3.3733 \| 42250 \| 0.0245 \| - \| \| 3.3772 \| 42300 \| 0.0001 \| - \| \| 3.3812 \| 42350 \| 0.0 \| - \| \| 3.3852 \| 42400 \| 0.0001 \| - \| \| 3.3892 \| 42450 \| 0.0001 \| - \| \| 3.3932 \| 42500 \| 0.0001 \| - \| \| 3.3972 \| 42550 \| 0.0001 \| - \| \| 3.4012 \| 42600 \| 0.0001 \| - \| \| 3.4052 \| 42650 \| 0.0001 \| - \| \| 3.4092 \| 42700 \| 0.0001 \| - \| \| 3.4132 \| 42750 \| 0.0001 \| - \| \| 3.4172 \| 42800 \| 0.0001 \| - \| \| 3.4212 \| 42850 \| 0.0 \| - \| \| 3.4251 \| 42900 \| 0.0 \| - \| \| 3.4291 \| 42950 \| 0.0001 \| - \| \| 3.4331 \| 43000 \| 0.0001 \| - \| \| 3.4371 \| 43050 \| 0.0001 \| - \| \| 3.4411 \| 43100 \| 0.0001 \| - \| \| 3.4451 \| 43150 \| 0.0002 \| - \| \| 3.4491 \| 43200 \| 0.0001 \| - \| \| 3.4531 \| 43250 \| 0.0002 \| - \| \| 3.4571 \| 43300 \| 0.0001 \| - \| \| 3.4611 \| 43350 \| 0.0 \| - \| \| 3.4651 \| 43400 \| 0.0001 \| - \| \| 3.4691 \| 43450 \| 0.0246 \| - \| \| 3.4731 \| 43500 \| 0.0001 \| - \| \| 3.4770 \| 43550 \| 0.0001 \| - \| \| 3.4810 \| 43600 \| 0.0246 \| - \| \| 3.4850 \| 43650 \| 0.0001 \| - \| \| 3.4890 \| 43700 \| 0.0001 \| - \| \| 3.4930 \| 43750 \| 0.0001 \| - \| \| 3.4970 \| 43800 \| 0.0245 \| - \| \| 3.5010 \| 43850 \| 0.0001 \| - \| \| 3.5050 \| 43900 \| 0.0001 \| - \| \| 3.5090 \| 43950 \| 0.0001 \| - \| \| 3.5130 \| 44000 \| 0.0001 \| - \| \| 3.5170 \| 44050 \| 0.0001 \| - \| \| 3.5210 \| 44100 \| 0.0001 \| - \| \| 3.5250 \| 44150 \| 0.0001 \| - \| \| 3.5289 \| 44200 \| 0.0001 \| - \| \| 3.5329 \| 44250 \| 0.0001 \| - \| \| 3.5369 \| 44300 \| 0.0 \| - \| \| 3.5409 \| 44350 \| 0.0001 \| - \| \| 3.5449 \| 44400 \| 0.0 \| - \| \| 3.5489 \| 44450 \| 0.0249 \| - \| \| 3.5529 \| 44500 \| 0.0 \| - \| \| 3.5569 \| 44550 \| 0.0002 \| - \| \| 3.5609 \| 44600 \| 0.0001 \| - \| \| 3.5649 \| 44650 \| 0.0002 \| - \| \| 3.5689 \| 44700 \| 0.0001 \| - \| \| 3.5729 \| 44750 \| 0.0 \| - \| \| 3.5768 \| 44800 \| 0.0 \| - \| \| 3.5808 \| 44850 \| 0.0 \| - \| \| 3.5848 \| 44900 \| 0.0 \| - \| \| 3.5888 \| 44950 \| 0.0 \| - \| \| 3.5928 \| 45000 \| 0.0001 \| - \| \| 3.5968 \| 45050 \| 0.0001 \| - \| \| 3.6008 \| 45100 \| 0.0001 \| - \| \| 3.6048 \| 45150 \| 0.0001 \| - \| \| 3.6088 \| 45200 \| 0.0002 \| - \| \| 3.6128 \| 45250 \| 0.0 \| - \| \| 3.6168 \| 45300 \| 0.0001 \| - \| \| 3.6208 \| 45350 \| 0.0001 \| - \| \| 3.6248 \| 45400 \| 0.0001 \| - \| \| 3.6287 \| 45450 \| 0.0244 \| - \| \| 3.6327 \| 45500 \| 0.0 \| - \| \| 3.6367 \| 45550 \| 0.0001 \| - \| \| 3.6407 \| 45600 \| 0.0 \| - \| \| 3.6447 \| 45650 \| 0.0001 \| - \| \| 3.6487 \| 45700 \| 0.0243 \| - \| \| 3.6527 \| 45750 \| 0.0252 \| - \| \| 3.6567 \| 45800 \| 0.0001 \| - \| \| 3.6607 \| 45850 \| 0.0001 \| - \| \| 3.6647 \| 45900 \| 0.0001 \| - \| \| 3.6687 \| 45950 \| 0.0 \| - \| \| 3.6727 \| 46000 \| 0.0001 \| - \| \| 3.6766 \| 46050 \| 0.0001 \| - \| \| 3.6806 \| 46100 \| 0.0002 \| - \| \| 3.6846 \| 46150 \| 0.0 \| - \| \| 3.6886 \| 46200 \| 0.0247 \| - \| \| 3.6926 \| 46250 \| 0.0 \| - \| \| 3.6966 \| 46300 \| 0.0001 \| - \| \| 3.7006 \| 46350 \| 0.0 \| - \| \| 3.7046 \| 46400 \| 0.0001 \| - \| \| 3.7086 \| 46450 \| 0.0001 \| - \| \| 3.7126 \| 46500 \| 0.0001 \| - \| \| 3.7166 \| 46550 \| 0.0 \| - \| \| 3.7206 \| 46600 \| 0.0001 \| - \| \| 3.7246 \| 46650 \| 0.0 \| - \| \| 3.7285 \| 46700 \| 0.0001 \| - \| \| 3.7325 \| 46750 \| 0.0001 \| - \| \| 3.7365 \| 46800 \| 0.0246 \| - \| \| 3.7405 \| 46850 \| 0.0 \| - \| \| 3.7445 \| 46900 \| 0.0001 \| - \| \| 3.7485 \| 46950 \| 0.0001 \| - \| \| 3.7525 \| 47000 \| 0.0001 \| - \| \| 3.7565 \| 47050 \| 0.0001 \| - \| \| 3.7605 \| 47100 \| 0.0001 \| - \| \| 3.7645 \| 47150 \| 0.025 \| - \| \| 3.7685 \| 47200 \| 0.0001 \| - \| \| 3.7725 \| 47250 \| 0.0002 \| - \| \| 3.7764 \| 47300 \| 0.0001 \| - \| \| 3.7804 \| 47350 \| 0.0247 \| - \| \| 3.7844 \| 47400 \| 0.0248 \| - \| \| 3.7884 \| 47450 \| 0.0001 \| - \| \| 3.7924 \| 47500 \| 0.0 \| - \| \| 3.7964 \| 47550 \| 0.0001 \| - \| \| 3.8004 \| 47600 \| 0.025 \| - \| \| 3.8044 \| 47650 \| 0.0001 \| - \| \| 3.8084 \| 47700 \| 0.0001 \| - \| \| 3.8124 \| 47750 \| 0.0002 \| - \| \| 3.8164 \| 47800 \| 0.0001 \| - \| \| 3.8204 \| 47850 \| 0.0001 \| - \| \| 3.8244 \| 47900 \| 0.0252 \| - \| \| 3.8283 \| 47950 \| 0.0001 \| - \| \| 3.8323 \| 48000 \| 0.0254 \| - \| \| 3.8363 \| 48050 \| 0.0249 \| - \| \| 3.8403 \| 48100 \| 0.0001 \| - \| \| 3.8443 \| 48150 \| 0.0001 \| - \| \| 3.8483 \| 48200 \| 0.0001 \| - \| \| 3.8523 \| 48250 \| 0.0 \| - \| \| 3.8563 \| 48300 \| 0.0001 \| - \| \| 3.8603 \| 48350 \| 0.0001 \| - \| \| 3.8643 \| 48400 \| 0.0001 \| - \| \| 3.8683 \| 48450 \| 0.0001 \| - \| \| 3.8723 \| 48500 \| 0.0001 \| - \| \| 3.8762 \| 48550 \| 0.0006 \| - \| \| 3.8802 \| 48600 \| 0.0003 \| - \| \| 3.8842 \| 48650 \| 0.0 \| - \| \| 3.8882 \| 48700 \| 0.0003 \| - \| \| 3.8922 \| 48750 \| 0.0001 \| - \| \| 3.8962 \| 48800 \| 0.0001 \| - \| \| 3.9002 \| 48850 \| 0.0001 \| - \| \| 3.9042 \| 48900 \| 0.0001 \| - \| \| 3.9082 \| 48950 \| 0.0001 \| - \| \| 3.9122 \| 49000 \| 0.0001 \| - \| \| 3.9162 \| 49050 \| 0.0246 \| - \| \| 3.9202 \| 49100 \| 0.0 \| - \| \| 3.9242 \| 49150 \| 0.0001 \| - \| \| 3.9281 \| 49200 \| 0.0001 \| - \| \| 3.9321 \| 49250 \| 0.0001 \| - \| \| 3.9361 \| 49300 \| 0.0246 \| - \| \| 3.9401 \| 49350 \| 0.0 \| - \| \| 3.9441 \| 49400 \| 0.0001 \| - \| \| 3.9481 \| 49450 \| 0.0002 \| - \| \| 3.9521 \| 49500 \| 0.0 \| - \| \| 3.9561 \| 49550 \| 0.0 \| - \| \| 3.9601 \| 49600 \| 0.0002 \| - \| \| 3.9641 \| 49650 \| 0.0248 \| - \| \| 3.9681 \| 49700 \| 0.0001 \| - \| \| 3.9721 \| 49750 \| 0.0001 \| - \| \| 3.9760 \| 49800 \| 0.0001 \| - \| \| 3.9800 \| 49850 \| 0.0248 \| - \| \| 3.9840 \| 49900 \| 0.0001 \| - \| \| 3.9880 \| 49950 \| 0.0245 \| - \| \| 3.9920 \| 50000 \| 0.0001 \| - \| \| 3.9960 \| 50050 \| 0.0487 \| - \| \| 4.0 \| 50100 \| 0.0002 \| 0.0927 \| \| 4.0040 \| 50150 \| 0.0001 \| - \| \| 4.0080 \| 50200 \| 0.0251 \| - \| \| 4.0120 \| 50250 \| 0.0245 \| - \| \| 4.0160 \| 50300 \| 0.0001 \| - \| \| 4.0200 \| 50350 \| 0.0001 \| - \| \| 4.0240 \| 50400 \| 0.0001 \| - \| \| 4.0279 \| 50450 \| 0.0 \| - \| \| 4.0319 \| 50500 \| 0.0001 \| - \| \| 4.0359 \| 50550 \| 0.0255 \| - \| \| 4.0399 \| 50600 \| 0.0001 \| - \| \| 4.0439 \| 50650 \| 0.0 \| - \| \| 4.0479 \| 50700 \| 0.0001 \| - \| \| 4.0519 \| 50750 \| 0.0001 \| - \| \| 4.0559 \| 50800 \| 0.0 \| - \| \| 4.0599 \| 50850 \| 0.0 \| - \| \| 4.0639 \| 50900 \| 0.0001 \| - \| \| 4.0679 \| 50950 \| 0.0001 \| - \| \| 4.0719 \| 51000 \| 0.0001 \| - \| \| 4.0758 \| 51050 \| 0.0001 \| - \| \| 4.0798 \| 51100 \| 0.0242 \| - \| \| 4.0838 \| 51150 \| 0.0001 \| - \| \| 4.0878 \| 51200 \| 0.0001 \| - \| \| 4.0918 \| 51250 \| 0.0001 \| - \| \| 4.0958 \| 51300 \| 0.0 \| - \| \| 4.0998 \| 51350 \| 0.0001 \| - \| \| 4.1038 \| 51400 \| 0.0 \| - \| \| 4.1078 \| 51450 \| 0.0 \| - \| \| 4.1118 \| 51500 \| 0.0246 \| - \| \| 4.1158 \| 51550 \| 0.0 \| - \| \| 4.1198 \| 51600 \| 0.0249 \| - \| \| 4.1238 \| 51650 \| 0.0001 \| - \| \| 4.1277 \| 51700 \| 0.0001 \| - \| \| 4.1317 \| 51750 \| 0.0001 \| - \| \| 4.1357 \| 51800 \| 0.0001 \| - \| \| 4.1397 \| 51850 \| 0.0 \| - \| \| 4.1437 \| 51900 \| 0.0001 \| - \| \| 4.1477 \| 51950 \| 0.0 \| - \| \| 4.1517 \| 52000 \| 0.0001 \| - \| \| 4.1557 \| 52050 \| 0.0001 \| - \| \| 4.1597 \| 52100 \| 0.0001 \| - \| \| 4.1637 \| 52150 \| 0.0001 \| - \| \| 4.1677 \| 52200 \| 0.0001 \| - \| \| 4.1717 \| 52250 \| 0.0001 \| - \| \| 4.1756 \| 52300 \| 0.0 \| - \| \| 4.1796 \| 52350 \| 0.0001 \| - \| \| 4.1836 \| 52400 \| 0.0001 \| - \| \| 4.1876 \| 52450 \| 0.0 \| - \| \| 4.1916 \| 52500 \| 0.0001 \| - \| \| 4.1956 \| 52550 \| 0.0001 \| - \| \| 4.1996 \| 52600 \| 0.0252 \| - \| \| 4.2036 \| 52650 \| 0.0001 \| - \| \| 4.2076 \| 52700 \| 0.0001 \| - \| \| 4.2116 \| 52750 \| 0.0001 \| - \| \| 4.2156 \| 52800 \| 0.0001 \| - \| \| 4.2196 \| 52850 \| 0.0001 \| - \| \| 4.2236 \| 52900 \| 0.0001 \| - \| \| 4.2275 \| 52950 \| 0.0 \| - \| \| 4.2315 \| 53000 \| 0.0 \| - \| \| 4.2355 \| 53050 \| 0.0001 \| - \| \| 4.2395 \| 53100 \| 0.0244 \| - \| \| 4.2435 \| 53150 \| 0.0001 \| - \| \| 4.2475 \| 53200 \| 0.0001 \| - \| \| 4.2515 \| 53250 \| 0.0248 \| - \| \| 4.2555 \| 53300 \| 0.0001 \| - \| \| 4.2595 \| 53350 \| 0.0 \| - \| \| 4.2635 \| 53400 \| 0.0 \| - \| \| 4.2675 \| 53450 \| 0.0245 \| - \| \| 4.2715 \| 53500 \| 0.0 \| - \| \| 4.2754 \| 53550 \| 0.0251 \| - \| \| 4.2794 \| 53600 \| 0.0 \| - \| \| 4.2834 \| 53650 \| 0.0001 \| - \| \| 4.2874 \| 53700 \| 0.0001 \| - \| \| 4.2914 \| 53750 \| 0.0001 \| - \| \| 4.2954 \| 53800 \| 0.0 \| - \| \| 4.2994 \| 53850 \| 0.0 \| - \| \| 4.3034 \| 53900 \| 0.0247 \| - \| \| 4.3074 \| 53950 \| 0.049 \| - \| \| 4.3114 \| 54000 \| 0.0 \| - \| \| 4.3154 \| 54050 \| 0.0001 \| - \| \| 4.3194 \| 54100 \| 0.0 \| - \| \| 4.3234 \| 54150 \| 0.0001 \| - \| \| 4.3273 \| 54200 \| 0.0001 \| - \| \| 4.3313 \| 54250 \| 0.0001 \| - \| \| 4.3353 \| 54300 \| 0.0001 \| - \| \| 4.3393 \| 54350 \| 0.0243 \| - \| \| 4.3433 \| 54400 \| 0.0001 \| - \| \| 4.3473 \| 54450 \| 0.0246 \| - \| \| 4.3513 \| 54500 \| 0.0 \| - \| \| 4.3553 \| 54550 \| 0.0001 \| - \| \| 4.3593 \| 54600 \| 0.0001 \| - \| \| 4.3633 \| 54650 \| 0.0001 \| - \| \| 4.3673 \| 54700 \| 0.0 \| - \| \| 4.3713 \| 54750 \| 0.0246 \| - \| \| 4.3752 \| 54800 \| 0.0 \| - \| \| 4.3792 \| 54850 \| 0.0 \| - \| \| 4.3832 \| 54900 \| 0.0001 \| - \| \| 4.3872 \| 54950 \| 0.0001 \| - \| \| 4.3912 \| 55000 \| 0.0001 \| - \| \| 4.3952 \| 55050 \| 0.0001 \| - \| \| 4.3992 \| 55100 \| 0.0001 \| - \| \| 4.4032 \| 55150 \| 0.0 \| - \| \| 4.4072 \| 55200 \| 0.0001 \| - \| \| 4.4112 \| 55250 \| 0.0 \| - \| \| 4.4152 \| 55300 \| 0.0 \| - \| \| 4.4192 \| 55350 \| 0.0001 \| - \| \| 4.4232 \| 55400 \| 0.0244 \| - \| \| 4.4271 \| 55450 \| 0.0 \| - \| \| 4.4311 \| 55500 \| 0.0 \| - \| \| 4.4351 \| 55550 \| 0.0001 \| - \| \| 4.4391 \| 55600 \| 0.0 \| - \| \| 4.4431 \| 55650 \| 0.0001 \| - \| \| 4.4471 \| 55700 \| 0.0001 \| - \| \| 4.4511 \| 55750 \| 0.0959 \| - \| \| 4.4551 \| 55800 \| 0.0002 \| - \| \| 4.4591 \| 55850 \| 0.0001 \| - \| \| 4.4631 \| 55900 \| 0.0001 \| - \| \| 4.4671 \| 55950 \| 0.0246 \| - \| \| 4.4711 \| 56000 \| 0.0001 \| - \| \| 4.4750 \| 56050 \| 0.0001 \| - \| \| 4.4790 \| 56100 \| 0.0246 \| - \| \| 4.4830 \| 56150 \| 0.024 \| - \| \| 4.4870 \| 56200 \| 0.0001 \| - \| \| 4.4910 \| 56250 \| 0.0001 \| - \| \| 4.4950 \| 56300 \| 0.0245 \| - \| \| 4.4990 \| 56350 \| 0.0001 \| - \| \| 4.5030 \| 56400 \| 0.0001 \| - \| \| 4.5070 \| 56450 \| 0.0001 \| - \| \| 4.5110 \| 56500 \| 0.0001 \| - \| \| 4.5150 \| 56550 \| 0.0001 \| - \| \| 4.5190 \| 56600 \| 0.0001 \| - \| \| 4.5230 \| 56650 \| 0.0001 \| - \| \| 4.5269 \| 56700 \| 0.0 \| - \| \| 4.5309 \| 56750 \| 0.0002 \| - \| \| 4.5349 \| 56800 \| 0.0001 \| - \| \| 4.5389 \| 56850 \| 0.0001 \| - \| \| 4.5429 \| 56900 \| 0.0001 \| - \| \| 4.5469 \| 56950 \| 0.0001 \| - \| \| 4.5509 \| 57000 \| 0.0 \| - \| \| 4.5549 \| 57050 \| 0.0001 \| - \| \| 4.5589 \| 57100 \| 0.0001 \| - \| \| 4.5629 \| 57150 \| 0.0001 \| - \| \| 4.5669 \| 57200 \| 0.0 \| - \| \| 4.5709 \| 57250 \| 0.0001 \| - \| \| 4.5749 \| 57300 \| 0.0001 \| - \| \| 4.5788 \| 57350 \| 0.0252 \| - \| \| 4.5828 \| 57400 \| 0.0 \| - \| \| 4.5868 \| 57450 \| 0.0 \| - \| \| 4.5908 \| 57500 \| 0.0001 \| - \| \| 4.5948 \| 57550 \| 0.0001 \| - \| \| 4.5988 \| 57600 \| 0.0001 \| - \| \| 4.6028 \| 57650 \| 0.0001 \| - \| \| 4.6068 \| 57700 \| 0.0 \| - \| \| 4.6108 \| 57750 \| 0.0 \| - \| \| 4.6148 \| 57800 \| 0.0001 \| - \| \| 4.6188 \| 57850 \| 0.0001 \| - \| \| 4.6228 \| 57900 \| 0.0 \| - \| \| 4.6267 \| 57950 \| 0.0244 \| - \| \| 4.6307 \| 58000 \| 0.0416 \| - \| \| 4.6347 \| 58050 \| 0.0001 \| - \| \| 4.6387 \| 58100 \| 0.0 \| - \| \| 4.6427 \| 58150 \| 0.0 \| - \| \| 4.6467 \| 58200 \| 0.0245 \| - \| \| 4.6507 \| 58250 \| 0.0001 \| - \| \| 4.6547 \| 58300 \| 0.0001 \| - \| \| 4.6587 \| 58350 \| 0.0 \| - \| \| 4.6627 \| 58400 \| 0.0001 \| - \| \| 4.6667 \| 58450 \| 0.0001 \| - \| \| 4.6707 \| 58500 \| 0.0001 \| - \| \| 4.6747 \| 58550 \| 0.0001 \| - \| \| 4.6786 \| 58600 \| 0.0001 \| - \| \| 4.6826 \| 58650 \| 0.0001 \| - \| \| 4.6866 \| 58700 \| 0.0002 \| - \| \| 4.6906 \| 58750 \| 0.0 \| - \| \| 4.6946 \| 58800 \| 0.0001 \| - \| \| 4.6986 \| 58850 \| 0.0001 \| - \| \| 4.7026 \| 58900 \| 0.0001 \| - \| \| 4.7066 \| 58950 \| 0.0253 \| - \| \| 4.7106 \| 59000 \| 0.0001 \| - \| \| 4.7146 \| 59050 \| 0.0 \| - \| \| 4.7186 \| 59100 \| 0.0001 \| - \| \| 4.7226 \| 59150 \| 0.0 \| - \| \| 4.7265 \| 59200 \| 0.0001 \| - \| \| 4.7305 \| 59250 \| 0.0001 \| - \| \| 4.7345 \| 59300 \| 0.0246 \| - \| \| 4.7385 \| 59350 \| 0.0252 \| - \| \| 4.7425 \| 59400 \| 0.0001 \| - \| \| 4.7465 \| 59450 \| 0.1531 \| - \| \| 4.7505 \| 59500 \| 0.0001 \| - \| \| 4.7545 \| 59550 \| 0.0001 \| - \| \| 4.7585 \| 59600 \| 0.025 \| - \| \| 4.7625 \| 59650 \| 0.0 \| - \| \| 4.7665 \| 59700 \| 0.0001 \| - \| \| 4.7705 \| 59750 \| 0.0001 \| - \| \| 4.7745 \| 59800 \| 0.0001 \| - \| \| 4.7784 \| 59850 \| 0.0244 \| - \| \| 4.7824 \| 59900 \| 0.0009 \| - \| \| 4.7864 \| 59950 \| 0.0001 \| - \| \| 4.7904 \| 60000 \| 0.0009 \| - \| \| 4.7944 \| 60050 \| 0.0015 \| - \| \| 4.7984 \| 60100 \| 0.0252 \| - \| \| 4.8024 \| 60150 \| 0.0001 \| - \| \| 4.8064 \| 60200 \| 0.0245 \| - \| \| 4.8104 \| 60250 \| 0.0003 \| - \| \| 4.8144 \| 60300 \| 0.0002 \| - \| \| 4.8184 \| 60350 \| 0.0001 \| - \| \| 4.8224 \| 60400 \| 0.0001 \| - \| \| 4.8263 \| 60450 \| 0.0249 \| - \| \| 4.8303 \| 60500 \| 0.0002 \| - \| \| 4.8343 \| 60550 \| 0.0001 \| - \| \| 4.8383 \| 60600 \| 0.0001 \| - \| \| 4.8423 \| 60650 \| 0.0001 \| - \| \| 4.8463 \| 60700 \| 0.0242 \| - \| \| 4.8503 \| 60750 \| 0.0001 \| - \| \| 4.8543 \| 60800 \| 0.0002 \| - \| \| 4.8583 \| 60850 \| 0.0001 \| - \| \| 4.8623 \| 60900 \| 0.0001 \| - \| \| 4.8663 \| 60950 \| 0.0005 \| - \| \| 4.8703 \| 61000 \| 0.0001 \| - \| \| 4.8743 \| 61050 \| 0.0247 \| - \| \| 4.8782 \| 61100 \| 0.0001 \| - \| \| 4.8822 \| 61150 \| 0.0001 \| - \| \| 4.8862 \| 61200 \| 0.0001 \| - \| \| 4.8902 \| 61250 \| 0.0001 \| - \| \| 4.8942 \| 61300 \| 0.0247 \| - \| \| 4.8982 \| 61350 \| 0.0245 \| - \| \| 4.9022 \| 61400 \| 0.0001 \| - \| \| 4.9062 \| 61450 \| 0.0001 \| - \| \| 4.9102 \| 61500 \| 0.0249 \| - \| \| 4.9142 \| 61550 \| 0.2375 \| - \| \| 4.9182 \| 61600 \| 0.0001 \| - \| \| 4.9222 \| 61650 \| 0.0001 \| - \| \| 4.9261 \| 61700 \| 0.0002 \| - \| \| 4.9301 \| 61750 \| 0.0001 \| - \| \| 4.9341 \| 61800 \| 0.0494 \| - \| \| 4.9381 \| 61850 \| 0.0001 \| - \| \| 4.9421 \| 61900 \| 0.0001 \| - \| \| 4.9461 \| 61950 \| 0.0001 \| - \| \| 4.9501 \| 62000 \| 0.0001 \| - \| \| 4.9541 \| 62050 \| 0.0001 \| - \| \| 4.9581 \| 62100 \| 0.024 \| - \| \| 4.9621 \| 62150 \| 0.0001 \| - \| \| 4.9661 \| 62200 \| 0.0001 \| - \| \| 4.9701 \| 62250 \| 0.0001 \| - \| \| 4.9741 \| 62300 \| 0.0002 \| - \| \| 4.9780 \| 62350 \| 0.0001 \| - \| \| 4.9820 \| 62400 \| 0.0502 \| - \| \| 4.9860 \| 62450 \| 0.0244 \| - \| \| 4.9900 \| 62500 \| 0.0001 \| - \| \| 4.9940 \| 62550 \| 0.0001 \| - \| \| 4.9980 \| 62600 \| 0.0001 \| - \| \| 5.0 \| 62625 \| - \| 0.1298 \| \| 5.0020 \| 62650 \| 0.025 \| - \| \| 5.0060 \| 62700 \| 0.0001 \| - \| \| 5.0100 \| 62750 \| 0.0244 \| - \| \| 5.0140 \| 62800 \| 0.0001 \| - \| \| 5.0180 \| 62850 \| 0.0001 \| - \| \| 5.0220 \| 62900 \| 0.0001 \| - \| \| 5.0259 \| 62950 \| 0.0001 \| - \| \| 5.0299 \| 63000 \| 0.0001 \| - \| \| 5.0339 \| 63050 \| 0.0002 \| - \| \| 5.0379 \| 63100 \| 0.0001 \| - \| \| 5.0419 \| 63150 \| 0.0001 \| - \| \| 5.0459 \| 63200 \| 0.0001 \| - \| \| 5.0499 \| 63250 \| 0.0001 \| - \| \| 5.0539 \| 63300 \| 0.0001 \| - \| \| 5.0579 \| 63350 \| 0.0001 \| - \| \| 5.0619 \| 63400 \| 0.0001 \| - \| \| 5.0659 \| 63450 \| 0.0249 \| - \| \| 5.0699 \| 63500 \| 0.0001 \| - \| \| 5.0739 \| 63550 \| 0.0001 \| - \| \| 5.0778 \| 63600 \| 0.0002 \| - \| \| 5.0818 \| 63650 \| 0.0001 \| - \| \| 5.0858 \| 63700 \| 0.0001 \| - \| \| 5.0898 \| 63750 \| 0.0001 \| - \| \| 5.0938 \| 63800 \| 0.0001 \| - \| \| 5.0978 \| 63850 \| 0.0001 \| - \| \| 5.1018 \| 63900 \| 0.0001 \| - \| \| 5.1058 \| 63950 \| 0.0001 \| - \| \| 5.1098 \| 64000 \| 0.0001 \| - \| \| 5.1138 \| 64050 \| 0.0001 \| - \| \| 5.1178 \| 64100 \| 0.0001 \| - \| \| 5.1218 \| 64150 \| 0.0001 \| - \| \| 5.1257 \| 64200 \| 0.0001 \| - \| \| 5.1297 \| 64250 \| 0.0001 \| - \| \| 5.1337 \| 64300 \| 0.0002 \| - \| \| 5.1377 \| 64350 \| 0.0001 \| - \| \| 5.1417 \| 64400 \| 0.0001 \| - \| \| 5.1457 \| 64450 \| 0.0002 \| - \| \| 5.1497 \| 64500 \| 0.0001 \| - \| \| 5.1537 \| 64550 \| 0.0001 \| - \| \| 5.1577 \| 64600 \| 0.0001 \| - \| \| 5.1617 \| 64650 \| 0.0003 \| - \| \| 5.1657 \| 64700 \| 0.0001 \| - \| \| 5.1697 \| 64750 \| 0.0001 \| - \| \| 5.1737 \| 64800 \| 0.0001 \| - \| \| 5.1776 \| 64850 \| 0.0243 \| - \| \| 5.1816 \| 64900 \| 0.0001 \| - \| \| 5.1856 \| 64950 \| 0.0003 \| - \| \| 5.1896 \| 65000 \| 0.0001 \| - \| \| 5.1936 \| 65050 \| 0.0001 \| - \| \| 5.1976 \| 65100 \| 0.0001 \| - \| \| 5.2016 \| 65150 \| 0.0001 \| - \| \| 5.2056 \| 65200 \| 0.0245 \| - \| \| 5.2096 \| 65250 \| 0.0001 \| - \| \| 5.2136 \| 65300 \| 0.0001 \| - \| \| 5.2176 \| 65350 \| 0.0001 \| - \| \| 5.2216 \| 65400 \| 0.0001 \| - \| \| 5.2255 \| 65450 \| 0.0001 \| - \| \| 5.2295 \| 65500 \| 0.0002 \| - \| \| 5.2335 \| 65550 \| 0.0248 \| - \| \| 5.2375 \| 65600 \| 0.0243 \| - \| \| 5.2415 \| 65650 \| 0.0001 \| - \| \| 5.2455 \| 65700 \| 0.0002 \| - \| \| 5.2495 \| 65750 \| 0.0001 \| - \| \| 5.2535 \| 65800 \| 0.0001 \| - \| \| 5.2575 \| 65850 \| 0.0 \| - \| \| 5.2615 \| 65900 \| 0.0001 \| - \| \| 5.2655 \| 65950 \| 0.0245 \| - \| \| 5.2695 \| 66000 \| 0.0001 \| - \| \| 5.2735 \| 66050 \| 0.0001 \| - \| \| 5.2774 \| 66100 \| 0.0001 \| - \| \| 5.2814 \| 66150 \| 0.0244 \| - \| \| 5.2854 \| 66200 \| 0.0001 \| - \| \| 5.2894 \| 66250 \| 0.0001 \| - \| \| 5.2934 \| 66300 \| 0.0001 \| - \| \| 5.2974 \| 66350 \| 0.0001 \| - \| \| 5.3014 \| 66400 \| 0.0247 \| - \| \| 5.3054 \| 66450 \| 0.0244 \| - \| \| 5.3094 \| 66500 \| 0.0001 \| - \| \| 5.3134 \| 66550 \| 0.0248 \| - \| \| 5.3174 \| 66600 \| 0.0001 \| - \| \| 5.3214 \| 66650 \| 0.0003 \| - \| \| 5.3253 \| 66700 \| 0.0001 \| - \| \| 5.3293 \| 66750 \| 0.0001 \| - \| \| 5.3333 \| 66800 \| 0.0249 \| - \| \| 5.3373 \| 66850 \| 0.0244 \| - \| \| 5.3413 \| 66900 \| 0.0001 \| - \| \| 5.3453 \| 66950 \| 0.0246 \| - \| \| 5.3493 \| 67000 \| 0.0 \| - \| \| 5.3533 \| 67050 \| 0.0001 \| - \| \| 5.3573 \| 67100 \| 0.0001 \| - \| \| 5.3613 \| 67150 \| 0.0001 \| - \| \| 5.3653 \| 67200 \| 0.0001 \| - \| \| 5.3693 \| 67250 \| 0.0494 \| - \| \| 5.3733 \| 67300 \| 0.0001 \| - \| \| 5.3772 \| 67350 \| 0.0001 \| - \| \| 5.3812 \| 67400 \| 0.0001 \| - \| \| 5.3852 \| 67450 \| 0.0001 \| - \| \| 5.3892 \| 67500 \| 0.0001 \| - \| \| 5.3932 \| 67550 \| 0.0001 \| - \| \| 5.3972 \| 67600 \| 0.0001 \| - \| \| 5.4012 \| 67650 \| 0.0001 \| - \| \| 5.4052 \| 67700 \| 0.0001 \| - \| \| 5.4092 \| 67750 \| 0.0001 \| - \| \| 5.4132 \| 67800 \| 0.0001 \| - \| \| 5.4172 \| 67850 \| 0.0001 \| - \| \| 5.4212 \| 67900 \| 0.0 \| - \| \| 5.4251 \| 67950 \| 0.0 \| - \| \| 5.4291 \| 68000 \| 0.0001 \| - \| \| 5.4331 \| 68050 \| 0.0001 \| - \| \| 5.4371 \| 68100 \| 0.0001 \| - \| \| 5.4411 \| 68150 \| 0.0001 \| - \| \| 5.4451 \| 68200 \| 0.0002 \| - \| \| 5.4491 \| 68250 \| 0.0001 \| - \| \| 5.4531 \| 68300 \| 0.0001 \| - \| \| 5.4571 \| 68350 \| 0.0001 \| - \| \| 5.4611 \| 68400 \| 0.0001 \| - \| \| 5.4651 \| 68450 \| 0.0245 \| - \| \| 5.4691 \| 68500 \| 0.0001 \| - \| \| 5.4731 \| 68550 \| 0.0001 \| - \| \| 5.4770 \| 68600 \| 0.0245 \| - \| \| 5.4810 \| 68650 \| 0.0001 \| - \| \| 5.4850 \| 68700 \| 0.0001 \| - \| \| 5.4890 \| 68750 \| 0.0001 \| - \| \| 5.4930 \| 68800 \| 0.0246 \| - \| \| 5.4970 \| 68850 \| 0.0 \| - \| \| 5.5010 \| 68900 \| 0.0001 \| - \| \| 5.5050 \| 68950 \| 0.0001 \| - \| \| 5.5090 \| 69000 \| 0.0001 \| - \| \| 5.5130 \| 69050 \| 0.0001 \| - \| \| 5.5170 \| 69100 \| 0.0001 \| - \| \| 5.5210 \| 69150 \| 0.0001 \| - \| \| 5.5250 \| 69200 \| 0.0001 \| - \| \| 5.5289 \| 69250 \| 0.0001 \| - \| \| 5.5329 \| 69300 \| 0.0001 \| - \| \| 5.5369 \| 69350 \| 0.0 \| - \| \| 5.5409 \| 69400 \| 0.0001 \| - \| \| 5.5449 \| 69450 \| 0.0001 \| - \| \| 5.5489 \| 69500 \| 0.0249 \| - \| \| 5.5529 \| 69550 \| 0.0 \| - \| \| 5.5569 \| 69600 \| 0.0001 \| - \| \| 5.5609 \| 69650 \| 0.0001 \| - \| \| 5.5649 \| 69700 \| 0.0016 \| - \| \| 5.5689 \| 69750 \| 0.0001 \| - \| \| 5.5729 \| 69800 \| 0.0 \| - \| \| 5.5768 \| 69850 \| 0.0 \| - \| \| 5.5808 \| 69900 \| 0.0 \| - \| \| 5.5848 \| 69950 \| 0.0 \| - \| \| 5.5888 \| 70000 \| 0.0 \| - \| \| 5.5928 \| 70050 \| 0.0001 \| - \| \| 5.5968 \| 70100 \| 0.0001 \| - \| \| 5.6008 \| 70150 \| 0.0001 \| - \| \| 5.6048 \| 70200 \| 0.0001 \| - \| \| 5.6088 \| 70250 \| 0.0001 \| - \| \| 5.6128 \| 70300 \| 0.0 \| - \| \| 5.6168 \| 70350 \| 0.0 \| - \| \| 5.6208 \| 70400 \| 0.0043 \| - \| \| 5.6248 \| 70450 \| 0.0151 \| - \| \| 5.6287 \| 70500 \| 0.0 \| - \| \| 5.6327 \| 70550 \| 0.0 \| - \| \| 5.6367 \| 70600 \| 0.0 \| - \| \| 5.6407 \| 70650 \| 0.0033 \| - \| \| 5.6447 \| 70700 \| 0.0274 \| - \| \| 5.6487 \| 70750 \| 0.0031 \| - \| \| 5.6527 \| 70800 \| 0.0248 \| - \| \| 5.6567 \| 70850 \| 0.0 \| - \| \| 5.6607 \| 70900 \| 0.0245 \| - \| \| 5.6647 \| 70950 \| 0.0248 \| - \| \| 5.6687 \| 71000 \| 0.0 \| - \| \| 5.6727 \| 71050 \| 0.0001 \| - \| \| 5.6766 \| 71100 \| 0.0001 \| - \| \| 5.6806 \| 71150 \| 0.0001 \| - \| \| 5.6846 \| 71200 \| 0.0001 \| - \| \| 5.6886 \| 71250 \| 0.0002 \| - \| \| 5.6926 \| 71300 \| 0.0 \| - \| \| 5.6966 \| 71350 \| 0.0001 \| - \| \| 5.7006 \| 71400 \| 0.0001 \| - \| \| 5.7046 \| 71450 \| 0.0001 \| - \| \| 5.7086 \| 71500 \| 0.0001 \| - \| \| 5.7126 \| 71550 \| 0.0001 \| - \| \| 5.7166 \| 71600 \| 0.0247 \| - \| \| 5.7206 \| 71650 \| 0.0001 \| - \| \| 5.7246 \| 71700 \| 0.0001 \| - \| \| 5.7285 \| 71750 \| 0.0001 \| - \| \| 5.7325 \| 71800 \| 0.0001 \| - \| \| 5.7365 \| 71850 \| 0.0001 \| - \| \| 5.7405 \| 71900 \| 0.0245 \| - \| \| 5.7445 \| 71950 \| 0.0001 \| - \| \| 5.7485 \| 72000 \| 0.0247 \| - \| \| 5.7525 \| 72050 \| 0.0001 \| - \| \| 5.7565 \| 72100 \| 0.0 \| - \| \| 5.7605 \| 72150 \| 0.0001 \| - \| \| 5.7645 \| 72200 \| 0.05 \| - \| \| 5.7685 \| 72250 \| 0.0001 \| - \| \| 5.7725 \| 72300 \| 0.0247 \| - \| \| 5.7764 \| 72350 \| 0.0002 \| - \| \| 5.7804 \| 72400 \| 0.0244 \| - \| \| 5.7844 \| 72450 \| 0.0 \| - \| \| 5.7884 \| 72500 \| 0.0001 \| - \| \| 5.7924 \| 72550 \| 0.0001 \| - \| \| 5.7964 \| 72600 \| 0.0 \| - \| \| 5.8004 \| 72650 \| 0.0001 \| - \| \| 5.8044 \| 72700 \| 0.0001 \| - \| \| 5.8084 \| 72750 \| 0.0001 \| - \| \| 5.8124 \| 72800 \| 0.0 \| - \| \| 5.8164 \| 72850 \| 0.0001 \| - \| \| 5.8204 \| 72900 \| 0.0001 \| - \| \| 5.8244 \| 72950 \| 0.025 \| - \| \| 5.8283 \| 73000 \| 0.0001 \| - \| \| 5.8323 \| 73050 \| 0.0 \| - \| \| 5.8363 \| 73100 \| 0.0247 \| - \| \| 5.8403 \| 73150 \| 0.0 \| - \| \| 5.8443 \| 73200 \| 0.0 \| - \| \| 5.8483 \| 73250 \| 0.0001 \| - \| \| 5.8523 \| 73300 \| 0.0 \| - \| \| 5.8563 \| 73350 \| 0.0 \| - \| \| 5.8603 \| 73400 \| 0.0001 \| - \| \| 5.8643 \| 73450 \| 0.0001 \| - \| \| 5.8683 \| 73500 \| 0.0 \| - \| \| 5.8723 \| 73550 \| 0.0248 \| - \| \| 5.8762 \| 73600 \| 0.0001 \| - \| \| 5.8802 \| 73650 \| 0.0001 \| - \| \| 5.8842 \| 73700 \| 0.0001 \| - \| \| 5.8882 \| 73750 \| 0.0001 \| - \| \| 5.8922 \| 73800 \| 0.0248 \| - \| \| 5.8962 \| 73850 \| 0.0001 \| - \| \| 5.9002 \| 73900 \| 0.0001 \| - \| \| 5.9042 \| 73950 \| 0.0001 \| - \| \| 5.9082 \| 74000 \| 0.0247 \| - \| \| 5.9122 \| 74050 \| 0.0246 \| - \| \| 5.9162 \| 74100 \| 0.0001 \| - \| \| 5.9202 \| 74150 \| 0.0001 \| - \| \| 5.9242 \| 74200 \| 0.0 \| - \| \| 5.9281 \| 74250 \| 0.0001 \| - \| \| 5.9321 \| 74300 \| 0.0 \| - \| \| 5.9361 \| 74350 \| 0.0 \| - \| \| 5.9401 \| 74400 \| 0.0 \| - \| \| 5.9441 \| 74450 \| 0.0001 \| - \| \| 5.9481 \| 74500 \| 0.0249 \| - \| \| 5.9521 \| 74550 \| 0.0 \| - \| \| 5.9561 \| 74600 \| 0.0002 \| - \| \| 5.9601 \| 74650 \| 0.0001 \| - \| \| 5.9641 \| 74700 \| 0.0001 \| - \| \| 5.9681 \| 74750 \| 0.0 \| - \| \| 5.9721 \| 74800 \| 0.0 \| - \| \| 5.9760 \| 74850 \| 0.0246 \| - \| \| 5.9800 \| 74900 \| 0.0001 \| - \| \| 5.9840 \| 74950 \| 0.0 \| - \| \| 5.9880 \| 75000 \| 0.0001 \| - \| \| 5.9920 \| 75050 \| 0.0 \| - \| \| 5.9960 \| 75100 \| 0.0001 \| - \| \| 6.0 \| 75150 \| 0.0001 \| 0.0594 \| \| 6.0040 \| 75200 \| 0.0001 \| - \| \| 6.0080 \| 75250 \| 0.0497 \| - \| \| 6.0120 \| 75300 \| 0.0248 \| - \| \| 6.0160 \| 75350 \| 0.0495 \| - \| \| 6.0200 \| 75400 \| 0.0 \| - \| \| 6.0240 \| 75450 \| 0.0001 \| - \| \| 6.0279 \| 75500 \| 0.0001 \| - \| \| 6.0319 \| 75550 \| 0.0001 \| - \| \| 6.0359 \| 75600 \| 0.0252 \| - \| \| 6.0399 \| 75650 \| 0.0001 \| - \| \| 6.0439 \| 75700 \| 0.0001 \| - \| \| 6.0479 \| 75750 \| 0.0001 \| - \| \| 6.0519 \| 75800 \| 0.0741 \| - \| \| 6.0559 \| 75850 \| 0.0 \| - \| \| 6.0599 \| 75900 \| 0.0 \| - \| \| 6.0639 \| 75950 \| 0.0001 \| - \| \| 6.0679 \| 76000 \| 0.0001 \| - \| \| 6.0719 \| 76050 \| 0.0 \| - \| \| 6.0758 \| 76100 \| 0.0001 \| - \| \| 6.0798 \| 76150 \| 0.0001 \| - \| \| 6.0838 \| 76200 \| 0.0247 \| - \| \| 6.0878 \| 76250 \| 0.0001 \| - \| \| 6.0918 \| 76300 \| 0.0001 \| - \| \| 6.0958 \| 76350 \| 0.0244 \| - \| \| 6.0998 \| 76400 \| 0.0 \| - \| \| 6.1038 \| 76450 \| 0.0001 \| - \| \| 6.1078 \| 76500 \| 0.0001 \| - \| \| 6.1118 \| 76550 \| 0.0001 \| - \| \| 6.1158 \| 76600 \| 0.0 \| - \| \| 6.1198 \| 76650 \| 0.0001 \| - \| \| 6.1238 \| 76700 \| 0.0 \| - \| \| 6.1277 \| 76750 \| 0.0245 \| - \| \| 6.1317 \| 76800 \| 0.0001 \| - \| \| 6.1357 \| 76850 \| 0.0001 \| - \| \| 6.1397 \| 76900 \| 0.0001 \| - \| \| 6.1437 \| 76950 \| 0.0001 \| - \| \| 6.1477 \| 77000 \| 0.0 \| - \| \| 6.1517 \| 77050 \| 0.0001 \| - \| \| 6.1557 \| 77100 \| 0.0001 \| - \| \| 6.1597 \| 77150 \| 0.0001 \| - \| \| 6.1637 \| 77200 \| 0.0001 \| - \| \| 6.1677 \| 77250 \| 0.0 \| - \| \| 6.1717 \| 77300 \| 0.0001 \| - \| \| 6.1756 \| 77350 \| 0.0001 \| - \| \| 6.1796 \| 77400 \| 0.0245 \| - \| \| 6.1836 \| 77450 \| 0.0 \| - \| \| 6.1876 \| 77500 \| 0.0496 \| - \| \| 6.1916 \| 77550 \| 0.0246 \| - \| \| 6.1956 \| 77600 \| 0.0001 \| - \| \| 6.1996 \| 77650 \| 0.025 \| - \| \| 6.2036 \| 77700 \| 0.0001 \| - \| \| 6.2076 \| 77750 \| 0.0 \| - \| \| 6.2116 \| 77800 \| 0.0001 \| - \| \| 6.2156 \| 77850 \| 0.0001 \| - \| \| 6.2196 \| 77900 \| 0.0248 \| - \| \| 6.2236 \| 77950 \| 0.0247 \| - \| \| 6.2275 \| 78000 \| 0.0002 \| - \| \| 6.2315 \| 78050 \| 0.0001 \| - \| \| 6.2355 \| 78100 \| 0.0246 \| - \| \| 6.2395 \| 78150 \| 0.0001 \| - \| \| 6.2435 \| 78200 \| 0.0001 \| - \| \| 6.2475 \| 78250 \| 0.0001 \| - \| \| 6.2515 \| 78300 \| 0.0249 \| - \| \| 6.2555 \| 78350 \| 0.0001 \| - \| \| 6.2595 \| 78400 \| 0.0251 \| - \| \| 6.2635 \| 78450 \| 0.0 \| - \| \| 6.2675 \| 78500 \| 0.0 \| - \| \| 6.2715 \| 78550 \| 0.0001 \| - \| \| 6.2754 \| 78600 \| 0.05 \| - \| \| 6.2794 \| 78650 \| 0.0001 \| - \| \| 6.2834 \| 78700 \| 0.0001 \| - \| \| 6.2874 \| 78750 \| 0.0 \| - \| \| 6.2914 \| 78800 \| 0.0001 \| - \| \| 6.2954 \| 78850 \| 0.0001 \| - \| \| 6.2994 \| 78900 \| 0.0 \| - \| \| 6.3034 \| 78950 \| 0.0246 \| - \| \| 6.3074 \| 79000 \| 0.0245 \| - \| \| 6.3114 \| 79050 \| 0.0001 \| - \| \| 6.3154 \| 79100 \| 0.0 \| - \| \| 6.3194 \| 79150 \| 0.0 \| - \| \| 6.3234 \| 79200 \| 0.0002 \| - \| \| 6.3273 \| 79250 \| 0.0001 \| - \| \| 6.3313 \| 79300 \| 0.0001 \| - \| \| 6.3353 \| 79350 \| 0.0 \| - \| \| 6.3393 \| 79400 \| 0.0001 \| - \| \| 6.3433 \| 79450 \| 0.0249 \| - \| \| 6.3473 \| 79500 \| 0.0001 \| - \| \| 6.3513 \| 79550 \| 0.0001 \| - \| \| 6.3553 \| 79600 \| 0.0001 \| - \| \| 6.3593 \| 79650 \| 0.0001 \| - \| \| 6.3633 \| 79700 \| 0.0 \| - \| \| 6.3673 \| 79750 \| 0.0247 \| - \| \| 6.3713 \| 79800 \| 0.0243 \| - \| \| 6.3752 \| 79850 \| 0.0 \| - \| \| 6.3792 \| 79900 \| 0.0001 \| - \| \| 6.3832 \| 79950 \| 0.0001 \| - \| \| 6.3872 \| 80000 \| 0.0001 \| - \| \| 6.3912 \| 80050 \| 0.0 \| - \| \| 6.3952 \| 80100 \| 0.0001 \| - \| \| 6.3992 \| 80150 \| 0.0 \| - \| \| 6.4032 \| 80200 \| 0.0249 \| - \| \| 6.4072 \| 80250 \| 0.0247 \| - \| \| 6.4112 \| 80300 \| 0.0 \| - \| \| 6.4152 \| 80350 \| 0.0248 \| - \| \| 6.4192 \| 80400 \| 0.0001 \| - \| \| 6.4232 \| 80450 \| 0.0247 \| - \| \| 6.4271 \| 80500 \| 0.0 \| - \| \| 6.4311 \| 80550 \| 0.0 \| - \| \| 6.4351 \| 80600 \| 0.0247 \| - \| \| 6.4391 \| 80650 \| 0.0246 \| - \| \| 6.4431 \| 80700 \| 0.0001 \| - \| \| 6.4471 \| 80750 \| 0.0242 \| - \| \| 6.4511 \| 80800 \| 0.0 \| - \| \| 6.4551 \| 80850 \| 0.0001 \| - \| \| 6.4591 \| 80900 \| 0.0001 \| - \| \| 6.4631 \| 80950 \| 0.0243 \| - \| \| 6.4671 \| 81000 \| 0.0001 \| - \| \| 6.4711 \| 81050 \| 0.0001 \| - \| \| 6.4750 \| 81100 \| 0.0491 \| - \| \| 6.4790 \| 81150 \| 0.0 \| - \| \| 6.4830 \| 81200 \| 0.0001 \| - \| \| 6.4870 \| 81250 \| 0.0001 \| - \| \| 6.4910 \| 81300 \| 0.0247 \| - \| \| 6.4950 \| 81350 \| 0.0 \| - \| \| 6.4990 \| 81400 \| 0.0001 \| - \| \| 6.5030 \| 81450 \| 0.0001 \| - \| \| 6.5070 \| 81500 \| 0.0001 \| - \| \| 6.5110 \| 81550 \| 0.0001 \| - \| \| 6.5150 \| 81600 \| 0.0001 \| - \| \| 6.5190 \| 81650 \| 0.0246 \| - \| \| 6.5230 \| 81700 \| 0.0246 \| - \| \| 6.5269 \| 81750 \| 0.0001 \| - \| \| 6.5309 \| 81800 \| 0.0001 \| - \| \| 6.5349 \| 81850 \| 0.0 \| - \| \| 6.5389 \| 81900 \| 0.0 \| - \| \| 6.5429 \| 81950 \| 0.0247 \| - \| \| 6.5469 \| 82000 \| 0.0248 \| - \| \| 6.5509 \| 82050 \| 0.0001 \| - \| \| 6.5549 \| 82100 \| 0.0 \| - \| \| 6.5589 \| 82150 \| 0.0001 \| - \| \| 6.5629 \| 82200 \| 0.0001 \| - \| \| 6.5669 \| 82250 \| 0.0001 \| - \| \| 6.5709 \| 82300 \| 0.0 \| - \| \| 6.5749 \| 82350 \| 0.0001 \| - \| \| 6.5788 \| 82400 \| 0.0001 \| - \| \| 6.5828 \| 82450 \| 0.0 \| - \| \| 6.5868 \| 82500 \| 0.0001 \| - \| \| 6.5908 \| 82550 \| 0.0001 \| - \| \| 6.5948 \| 82600 \| 0.0001 \| - \| \| 6.5988 \| 82650 \| 0.0001 \| - \| \| 6.6028 \| 82700 \| 0.0001 \| - \| \| 6.6068 \| 82750 \| 0.0001 \| - \| \| 6.6108 \| 82800 \| 0.0 \| - \| \| 6.6148 \| 82850 \| 0.0 \| - \| \| 6.6188 \| 82900 \| 0.0001 \| - \| \| 6.6228 \| 82950 \| 0.0244 \| - \| \| 6.6267 \| 83000 \| 0.0001 \| - \| \| 6.6307 \| 83050 \| 0.0247 \| - \| \| 6.6347 \| 83100 \| 0.0 \| - \| \| 6.6387 \| 83150 \| 0.0001 \| - \| \| 6.6427 \| 83200 \| 0.0246 \| - \| \| 6.6467 \| 83250 \| 0.0001 \| - \| \| 6.6507 \| 83300 \| 0.0001 \| - \| \| 6.6547 \| 83350 \| 0.0 \| - \| \| 6.6587 \| 83400 \| 0.0 \| - \| \| 6.6627 \| 83450 \| 0.0 \| - \| \| 6.6667 \| 83500 \| 0.0 \| - \| \| 6.6707 \| 83550 \| 0.0 \| - \| \| 6.6747 \| 83600 \| 0.0001 \| - \| \| 6.6786 \| 83650 \| 0.0001 \| - \| \| 6.6826 \| 83700 \| 0.0 \| - \| \| 6.6866 \| 83750 \| 0.0001 \| - \| \| 6.6906 \| 83800 \| 0.0 \| - \| \| 6.6946 \| 83850 \| 0.0001 \| - \| \| 6.6986 \| 83900 \| 0.0001 \| - \| \| 6.7026 \| 83950 \| 0.0001 \| - \| \| 6.7066 \| 84000 \| 0.025 \| - \| \| 6.7106 \| 84050 \| 0.0001 \| - \| \| 6.7146 \| 84100 \| 0.0 \| - \| \| 6.7186 \| 84150 \| 0.0 \| - \| \| 6.7226 \| 84200 \| 0.0 \| - \| \| 6.7265 \| 84250 \| 0.0001 \| - \| \| 6.7305 \| 84300 \| 0.0246 \| - \| \| 6.7345 \| 84350 \| 0.0001 \| - \| \| 6.7385 \| 84400 \| 0.0251 \| - \| \| 6.7425 \| 84450 \| 0.0 \| - \| \| 6.7465 \| 84500 \| 0.0 \| - \| \| 6.7505 \| 84550 \| 0.0001 \| - \| \| 6.7545 \| 84600 \| 0.0 \| - \| \| 6.7585 \| 84650 \| 0.0248 \| - \| \| 6.7625 \| 84700 \| 0.0 \| - \| \| 6.7665 \| 84750 \| 0.0001 \| - \| \| 6.7705 \| 84800 \| 0.0 \| - \| \| 6.7745 \| 84850 \| 0.0247 \| - \| \| 6.7784 \| 84900 \| 0.0 \| - \| \| 6.7824 \| 84950 \| 0.0001 \| - \| \| 6.7864 \| 85000 \| 0.0 \| - \| \| 6.7904 \| 85050 \| 0.0 \| - \| \| 6.7944 \| 85100 \| 0.0 \| - \| \| 6.7984 \| 85150 \| 0.0002 \| - \| \| 6.8024 \| 85200 \| 0.0 \| - \| \| 6.8064 \| 85250 \| 0.0001 \| - \| \| 6.8104 \| 85300 \| 0.0001 \| - \| \| 6.8144 \| 85350 \| 0.0 \| - \| \| 6.8184 \| 85400 \| 0.0001 \| - \| \| 6.8224 \| 85450 \| 0.0001 \| - \| \| 6.8263 \| 85500 \| 0.0 \| - \| \| 6.8303 \| 85550 \| 0.0001 \| - \| \| 6.8343 \| 85600 \| 0.0 \| - \| \| 6.8383 \| 85650 \| 0.0 \| - \| \| 6.8423 \| 85700 \| 0.0 \| - \| \| 6.8463 \| 85750 \| 0.0248 \| - \| \| 6.8503 \| 85800 \| 0.0 \| - \| \| 6.8543 \| 85850 \| 0.0001 \| - \| \| 6.8583 \| 85900 \| 0.0 \| - \| \| 6.8623 \| 85950 \| 0.0 \| - \| \| 6.8663 \| 86000 \| 0.0001 \| - \| \| 6.8703 \| 86050 \| 0.0 \| - \| \| 6.8743 \| 86100 \| 0.0001 \| - \| \| 6.8782 \| 86150 \| 0.0001 \| - \| \| 6.8822 \| 86200 \| 0.0 \| - \| \| 6.8862 \| 86250 \| 0.0 \| - \| \| 6.8902 \| 86300 \| 0.0 \| - \| \| 6.8942 \| 86350 \| 0.0 \| - \| \| 6.8982 \| 86400 \| 0.0 \| - \| \| 6.9022 \| 86450 \| 0.0001 \| - \| \| 6.9062 \| 86500 \| 0.0 \| - \| \| 6.9102 \| 86550 \| 0.0 \| - \| \| 6.9142 \| 86600 \| 0.0 \| - \| \| 6.9182 \| 86650 \| 0.0 \| - \| \| 6.9222 \| 86700 \| 0.0 \| - \| \| 6.9261 \| 86750 \| 0.0001 \| - \| \| 6.9301 \| 86800 \| 0.0246 \| - \| \| 6.9341 \| 86850 \| 0.025 \| - \| \| 6.9381 \| 86900 \| 0.0001 \| - \| \| 6.9421 \| 86950 \| 0.0 \| - \| \| 6.9461 \| 87000 \| 0.0 \| - \| \| 6.9501 \| 87050 \| 0.0 \| - \| \| 6.9541 \| 87100 \| 0.0001 \| - \| \| 6.9581 \| 87150 \| 0.0001 \| - \| \| 6.9621 \| 87200 \| 0.0 \| - \| \| 6.9661 \| 87250 \| 0.0 \| - \| \| 6.9701 \| 87300 \| 0.0001 \| - \| \| 6.9741 \| 87350 \| 0.0081 \| - \| \| 6.9780 \| 87400 \| 0.0 \| - \| \| 6.9820 \| 87450 \| 0.0469 \| - \| \| 6.9860 \| 87500 \| 0.0 \| - \| \| 6.9900 \| 87550 \| 0.0 \| - \| \| 6.9940 \| 87600 \| 0.0 \| - \| \| 6.9980 \| 87650 \| 0.0 \| - \| \| 7.0 \| 87675 \| - \| 0.039 \| \| 7.0020 \| 87700 \| 0.0248 \| - \| \| 7.0060 \| 87750 \| 0.0246 \| - \| \| 7.0100 \| 87800 \| 0.0 \| - \| \| 7.0140 \| 87850 \| 0.0001 \| - \| \| 7.0180 \| 87900 \| 0.0001 \| - \| \| 7.0220 \| 87950 \| 0.0 \| - \| \| 7.0259 \| 88000 \| 0.0 \| - \| \| 7.0299 \| 88050 \| 0.0001 \| - \| \| 7.0339 \| 88100 \| 0.0001 \| - \| \| 7.0379 \| 88150 \| 0.0 \| - \| \| 7.0419 \| 88200 \| 0.0034 \| - \| \| 7.0459 \| 88250 \| 0.0001 \| - \| \| 7.0499 \| 88300 \| 0.004 \| - \| \| 7.0539 \| 88350 \| 0.0 \| - \| \| 7.0579 \| 88400 \| 0.0 \| - \| \| 7.0619 \| 88450 \| 0.0001 \| - \| \| 7.0659 \| 88500 \| 0.0249 \| - \| \| 7.0699 \| 88550 \| 0.0 \| - \| \| 7.0739 \| 88600 \| 0.0 \| - \| \| 7.0778 \| 88650 \| 0.0001 \| - \| \| 7.0818 \| 88700 \| 0.0 \| - \| \| 7.0858 \| 88750 \| 0.0 \| - \| \| 7.0898 \| 88800 \| 0.0 \| - \| \| 7.0938 \| 88850 \| 0.0 \| - \| \| 7.0978 \| 88900 \| 0.0 \| - \| \| 7.1018 \| 88950 \| 0.0 \| - \| \| 7.1058 \| 89000 \| 0.0 \| - \| \| 7.1098 \| 89050 \| 0.0 \| - \| \| 7.1138 \| 89100 \| 0.0 \| - \| \| 7.1178 \| 89150 \| 0.0 \| - \| \| 7.1218 \| 89200 \| 0.0001 \| - \| \| 7.1257 \| 89250 \| 0.0024 \| - \| \| 7.1297 \| 89300 \| 0.0 \| - \| \| 7.1337 \| 89350 \| 0.0001 \| - \| \| 7.1377 \| 89400 \| 0.0 \| - \| \| 7.1417 \| 89450 \| 0.0 \| - \| \| 7.1457 \| 89500 \| 0.0001 \| - \| \| 7.1497 \| 89550 \| 0.0 \| - \| \| 7.1537 \| 89600 \| 0.0 \| - \| \| 7.1577 \| 89650 \| 0.0 \| - \| \| 7.1617 \| 89700 \| 0.0001 \| - \| \| 7.1657 \| 89750 \| 0.0001 \| - \| \| 7.1697 \| 89800 \| 0.0 \| - \| \| 7.1737 \| 89850 \| 0.0 \| - \| \| 7.1776 \| 89900 \| 0.0135 \| - \| \| 7.1816 \| 89950 \| 0.0001 \| - \| \| 7.1856 \| 90000 \| 0.0043 \| - \| \| 7.1896 \| 90050 \| 0.0072 \| - \| \| 7.1936 \| 90100 \| 0.0 \| - \| \| 7.1976 \| 90150 \| 0.0 \| - \| \| 7.2016 \| 90200 \| 0.0 \| - \| \| 7.2056 \| 90250 \| 0.0247 \| - \| \| 7.2096 \| 90300 \| 0.0 \| - \| \| 7.2136 \| 90350 \| 0.0 \| - \| \| 7.2176 \| 90400 \| 0.0 \| - \| \| 7.2216 \| 90450 \| 0.0 \| - \| \| 7.2255 \| 90500 \| 0.0001 \| - \| \| 7.2295 \| 90550 \| 0.0 \| - \| \| 7.2335 \| 90600 \| 0.0363 \| - \| \| 7.2375 \| 90650 \| 0.0 \| - \| \| 7.2415 \| 90700 \| 0.0 \| - \| \| 7.2455 \| 90750 \| 0.0 \| - \| \| 7.2495 \| 90800 \| 0.0 \| - \| \| 7.2535 \| 90850 \| 0.0 \| - \| \| 7.2575 \| 90900 \| 0.0 \| - \| \| 7.2615 \| 90950 \| 0.0138 \| - \| \| 7.2655 \| 91000 \| 0.0 \| - \| \| 7.2695 \| 91050 \| 0.0 \| - \| \| 7.2735 \| 91100 \| 0.0 \| - \| \| 7.2774 \| 91150 \| 0.0 \| - \| \| 7.2814 \| 91200 \| 0.0252 \| - \| \| 7.2854 \| 91250 \| 0.0 \| - \| \| 7.2894 \| 91300 \| 0.0 \| - \| \| 7.2934 \| 91350 \| 0.0 \| - \| \| 7.2974 \| 91400 \| 0.0105 \| - \| \| 7.3014 \| 91450 \| 0.0244 \| - \| \| 7.3054 \| 91500 \| 0.0 \| - \| \| 7.3094 \| 91550 \| 0.0 \| - \| \| 7.3134 \| 91600 \| 0.0247 \| - \| \| 7.3174 \| 91650 \| 0.0 \| - \| \| 7.3214 \| 91700 \| 0.0001 \| - \| \| 7.3253 \| 91750 \| 0.0 \| - \| \| 7.3293 \| 91800 \| 0.0 \| - \| \| 7.3333 \| 91850 \| 0.0189 \| - \| \| 7.3373 \| 91900 \| 0.0 \| - \| \| 7.3413 \| 91950 \| 0.0246 \| - \| \| 7.3453 \| 92000 \| 0.0 \| - \| \| 7.3493 \| 92050 \| 0.0 \| - \| \| 7.3533 \| 92100 \| 0.0 \| - \| \| 7.3573 \| 92150 \| 0.0 \| - \| \| 7.3613 \| 92200 \| 0.0 \| - \| \| 7.3653 \| 92250 \| 0.0247 \| - \| \| 7.3693 \| 92300 \| 0.0122 \| - \| \| 7.3733 \| 92350 \| 0.0 \| - \| \| 7.3772 \| 92400 \| 0.0 \| - \| \| 7.3812 \| 92450 \| 0.0022 \| - \| \| 7.3852 \| 92500 \| 0.0 \| - \| \| 7.3892 \| 92550 \| 0.0001 \| - \| \| 7.3932 \| 92600 \| 0.0 \| - \| \| 7.3972 \| 92650 \| 0.0 \| - \| \| 7.4012 \| 92700 \| 0.0 \| - \| \| 7.4052 \| 92750 \| 0.0032 \| - \| \| 7.4092 \| 92800 \| 0.0001 \| - \| \| 7.4132 \| 92850 \| 0.0037 \| - \| \| 7.4172 \| 92900 \| 0.0001 \| - \| \| 7.4212 \| 92950 \| 0.0028 \| - \| \| 7.4251 \| 93000 \| 0.0001 \| - \| \| 7.4291 \| 93050 \| 0.0 \| - \| \| 7.4331 \| 93100 \| 0.0039 \| - \| \| 7.4371 \| 93150 \| 0.0036 \| - \| \| 7.4411 \| 93200 \| 0.0 \| - \| \| 7.4451 \| 93250 \| 0.0 \| - \| \| 7.4491 \| 93300 \| 0.0 \| - \| \| 7.4531 \| 93350 \| 0.0 \| - \| \| 7.4571 \| 93400 \| 0.0001 \| - \| \| 7.4611 \| 93450 \| 0.0091 \| - \| \| 7.4651 \| 93500 \| 0.0 \| - \| \| 7.4691 \| 93550 \| 0.0 \| - \| \| 7.4731 \| 93600 \| 0.0275 \| - \| \| 7.4770 \| 93650 \| 0.0 \| - \| \| 7.4810 \| 93700 \| 0.0 \| - \| \| 7.4850 \| 93750 \| 0.0035 \| - \| \| 7.4890 \| 93800 \| 0.0246 \| - \| \| 7.4930 \| 93850 \| 0.0025 \| - \| \| 7.4970 \| 93900 \| 0.0 \| - \| \| 7.5010 \| 93950 \| 0.0 \| - \| \| 7.5050 \| 94000 \| 0.0 \| - \| \| 7.5090 \| 94050 \| 0.0 \| - \| \| 7.5130 \| 94100 \| 0.0 \| - \| \| 7.5170 \| 94150 \| 0.0 \| - \| \| 7.5210 \| 94200 \| 0.0023 \| - \| \| 7.5250 \| 94250 \| 0.0 \| - \| \| 7.5289 \| 94300 \| 0.0 \| - \| \| 7.5329 \| 94350 \| 0.0 \| - \| \| 7.5369 \| 94400 \| 0.0 \| - \| \| 7.5409 \| 94450 \| 0.0027 \| - \| \| 7.5449 \| 94500 \| 0.0028 \| - \| \| 7.5489 \| 94550 \| 0.0247 \| - \| \| 7.5529 \| 94600 \| 0.0 \| - \| \| 7.5569 \| 94650 \| 0.0 \| - \| \| 7.5609 \| 94700 \| 0.0 \| - \| \| 7.5649 \| 94750 \| 0.0036 \| - \| \| 7.5689 \| 94800 \| 0.0 \| - \| \| 7.5729 \| 94850 \| 0.0 \| - \| \| 7.5768 \| 94900 \| 0.0 \| - \| \| 7.5808 \| 94950 \| 0.0 \| - \| \| 7.5848 \| 95000 \| 0.0 \| - \| \| 7.5888 \| 95050 \| 0.0 \| - \| \| 7.5928 \| 95100 \| 0.0001 \| - \| \| 7.5968 \| 95150 \| 0.0 \| - \| \| 7.6008 \| 95200 \| 0.0 \| - \| \| 7.6048 \| 95250 \| 0.0001 \| - \| \| 7.6088 \| 95300 \| 0.0 \| - \| \| 7.6128 \| 95350 \| 0.0 \| - \| \| 7.6168 \| 95400 \| 0.0028 \| - \| \| 7.6208 \| 95450 \| 0.0119 \| - \| \| 7.6248 \| 95500 \| 0.0028 \| - \| \| 7.6287 \| 95550 \| 0.0 \| - \| \| 7.6327 \| 95600 \| 0.0001 \| - \| \| 7.6367 \| 95650 \| 0.0 \| - \| \| 7.6407 \| 95700 \| 0.0318 \| - \| \| 7.6447 \| 95750 \| 0.0037 \| - \| \| 7.6487 \| 95800 \| 0.0035 \| - \| \| 7.6527 \| 95850 \| 0.0089 \| - \| \| 7.6567 \| 95900 \| 0.0 \| - \| \| 7.6607 \| 95950 \| 0.006 \| - \| \| 7.6647 \| 96000 \| 0.0 \| - \| \| 7.6687 \| 96050 \| 0.0 \| - \| \| 7.6727 \| 96100 \| 0.0 \| - \| \| 7.6766 \| 96150 \| 0.0 \| - \| \| 7.6806 \| 96200 \| 0.0 \| - \| \| 7.6846 \| 96250 \| 0.0 \| - \| \| 7.6886 \| 96300 \| 0.0105 \| - \| \| 7.6926 \| 96350 \| 0.0 \| - \| \| 7.6966 \| 96400 \| 0.0 \| - \| \| 7.7006 \| 96450 \| 0.0 \| - \| \| 7.7046 \| 96500 \| 0.0 \| - \| \| 7.7086 \| 96550 \| 0.0 \| - \| \| 7.7126 \| 96600 \| 0.0 \| - \| \| 7.7166 \| 96650 \| 0.0024 \| - \| \| 7.7206 \| 96700 \| 0.0001 \| - \| \| 7.7246 \| 96750 \| 0.0 \| - \| \| 7.7285 \| 96800 \| 0.0123 \| - \| \| 7.7325 \| 96850 \| 0.0 \| - \| \| 7.7365 \| 96900 \| 0.0031 \| - \| \| 7.7405 \| 96950 \| 0.0 \| - \| \| 7.7445 \| 97000 \| 0.0025 \| - \| \| 7.7485 \| 97050 \| 0.0 \| - \| \| 7.7525 \| 97100 \| 0.0 \| - \| \| 7.7565 \| 97150 \| 0.0 \| - \| \| 7.7605 \| 97200 \| 0.0022 \| - \| \| 7.7645 \| 97250 \| 0.0251 \| - \| \| 7.7685 \| 97300 \| 0.002 \| - \| \| 7.7725 \| 97350 \| 0.0118 \| - \| \| 7.7764 \| 97400 \| 0.0019 \| - \| \| 7.7804 \| 97450 \| 0.0001 \| - \| \| 7.7844 \| 97500 \| 0.0123 \| - \| \| 7.7884 \| 97550 \| 0.0 \| - \| \| 7.7924 \| 97600 \| 0.0 \| - \| \| 7.7964 \| 97650 \| 0.0 \| - \| \| 7.8004 \| 97700 \| 0.0097 \| - \| \| 7.8044 \| 97750 \| 0.0 \| - \| \| 7.8084 \| 97800 \| 0.0 \| - \| \| 7.8124 \| 97850 \| 0.0 \| - \| \| 7.8164 \| 97900 \| 0.0001 \| - \| \| 7.8204 \| 97950 \| 0.0001 \| - \| \| 7.8244 \| 98000 \| 0.0251 \| - \| \| 7.8283 \| 98050 \| 0.0 \| - \| \| 7.8323 \| 98100 \| 0.009 \| - \| \| 7.8363 \| 98150 \| 0.0246 \| - \| \| 7.8403 \| 98200 \| 0.0 \| - \| \| 7.8443 \| 98250 \| 0.0 \| - \| \| 7.8483 \| 98300 \| 0.0 \| - \| \| 7.8523 \| 98350 \| 0.0 \| - \| \| 7.8563 \| 98400 \| 0.0 \| - \| \| 7.8603 \| 98450 \| 0.0001 \| - \| \| 7.8643 \| 98500 \| 0.0 \| - \| \| 7.8683 \| 98550 \| 0.0018 \| - \| \| 7.8723 \| 98600 \| 0.0 \| - \| \| 7.8762 \| 98650 \| 0.0001 \| - \| \| 7.8802 \| 98700 \| 0.0 \| - \| \| 7.8842 \| 98750 \| 0.0 \| - \| \| 7.8882 \| 98800 \| 0.0024 \| - \| \| 7.8922 \| 98850 \| 0.0 \| - \| \| 7.8962 \| 98900 \| 0.0 \| - \| \| 7.9002 \| 98950 \| 0.0 \| - \| \| 7.9042 \| 99000 \| 0.0027 \| - \| \| 7.9082 \| 99050 \| 0.0027 \| - \| \| 7.9122 \| 99100 \| 0.0 \| - \| \| 7.9162 \| 99150 \| 0.0 \| - \| \| 7.9202 \| 99200 \| 0.0 \| - \| \| 7.9242 \| 99250 \| 0.0 \| - \| \| 7.9281 \| 99300 \| 0.0138 \| - \| \| 7.9321 \| 99350 \| 0.0 \| - \| \| 7.9361 \| 99400 \| 0.0 \| - \| \| 7.9401 \| 99450 \| 0.0 \| - \| \| 7.9441 \| 99500 \| 0.0001 \| - \| \| 7.9481 \| 99550 \| 0.0019 \| - \| \| 7.9521 \| 99600 \| 0.0 \| - \| \| 7.9561 \| 99650 \| 0.0 \| - \| \| 7.9601 \| 99700 \| 0.0001 \| - \| \| 7.9641 \| 99750 \| 0.0 \| - \| \| 7.9681 \| 99800 \| 0.0 \| - \| \| 7.9721 \| 99850 \| 0.0016 \| - \| \| 7.9760 \| 99900 \| 0.0001 \| - \| \| 7.9800 \| 99950 \| 0.0265 \| - \| \| 7.9840 \| 100000 \| 0.0 \| - \| \| 7.9880 \| 100050 \| 0.0 \| - \| \| 7.9920 \| 100100 \| 0.0 \| - \| \| 7.9960 \| 100150 \| 0.0127 \| - \| \| 8.0 \| 100200 \| 0.0001 \| 0.0405 \| \| 8.0040 \| 100250 \| 0.0247 \| - \| \| 8.0080 \| 100300 \| 0.025 \| - \| \| 8.0120 \| 100350 \| 0.0 \| - \| \| 8.0160 \| 100400 \| 0.0072 \| - \| \| 8.0200 \| 100450 \| 0.0 \| - \| \| 8.0240 \| 100500 \| 0.0 \| - \| \| 8.0279 \| 100550 \| 0.0 \| - \| \| 8.0319 \| 100600 \| 0.0 \| - \| \| 8.0359 \| 100650 \| 0.0251 \| - \| \| 8.0399 \| 100700 \| 0.0 \| - \| \| 8.0439 \| 100750 \| 0.0 \| - \| \| 8.0479 \| 100800 \| 0.0042 \| - \| \| 8.0519 \| 100850 \| 0.0036 \| - \| \| 8.0559 \| 100900 \| 0.0 \| - \| \| 8.0599 \| 100950 \| 0.0 \| - \| \| 8.0639 \| 101000 \| 0.0 \| - \| \| 8.0679 \| 101050 \| 0.0001 \| - \| \| 8.0719 \| 101100 \| 0.0 \| - \| \| 8.0758 \| 101150 \| 0.0 \| - \| \| 8.0798 \| 101200 \| 0.0116 \| - \| \| 8.0838 \| 101250 \| 0.0027 \| - \| \| 8.0878 \| 101300 \| 0.0 \| - \| \| 8.0918 \| 101350 \| 0.0 \| - \| \| 8.0958 \| 101400 \| 0.0032 \| - \| \| 8.0998 \| 101450 \| 0.0 \| - \| \| 8.1038 \| 101500 \| 0.0 \| - \| \| 8.1078 \| 101550 \| 0.0 \| - \| \| 8.1118 \| 101600 \| 0.0097 \| - \| \| 8.1158 \| 101650 \| 0.0 \| - \| \| 8.1198 \| 101700 \| 0.0105 \| - \| \| 8.1238 \| 101750 \| 0.0 \| - \| \| 8.1277 \| 101800 \| 0.0026 \| - \| \| 8.1317 \| 101850 \| 0.0 \| - \| \| 8.1357 \| 101900 \| 0.0 \| - \| \| 8.1397 \| 101950 \| 0.0 \| - \| \| 8.1437 \| 102000 \| 0.0 \| - \| \| 8.1477 \| 102050 \| 0.0 \| - \| \| 8.1517 \| 102100 \| 0.0 \| - \| \| 8.1557 \| 102150 \| 0.0 \| - \| \| 8.1597 \| 102200 \| 0.0 \| - \| \| 8.1637 \| 102250 \| 0.0 \| - \| \| 8.1677 \| 102300 \| 0.0 \| - \| \| 8.1717 \| 102350 \| 0.0 \| - \| \| 8.1756 \| 102400 \| 0.0028 \| - \| \| 8.1796 \| 102450 \| 0.0 \| - \| \| 8.1836 \| 102500 \| 0.0037 \| - \| \| 8.1876 \| 102550 \| 0.0065 \| - \| \| 8.1916 \| 102600 \| 0.0 \| - \| \| 8.1956 \| 102650 \| 0.0001 \| - \| \| 8.1996 \| 102700 \| 0.0251 \| - \| \| 8.2036 \| 102750 \| 0.0 \| - \| \| 8.2076 \| 102800 \| 0.0 \| - \| \| 8.2116 \| 102850 \| 0.0 \| - \| \| 8.2156 \| 102900 \| 0.0 \| - \| \| 8.2196 \| 102950 \| 0.0023 \| - \| \| 8.2236 \| 103000 \| 0.0023 \| - \| \| 8.2275 \| 103050 \| 0.0 \| - \| \| 8.2315 \| 103100 \| 0.0246 \| - \| \| 8.2355 \| 103150 \| 0.0 \| - \| \| 8.2395 \| 103200 \| 0.0 \| - \| \| 8.2435 \| 103250 \| 0.0 \| - \| \| 8.2475 \| 103300 \| 0.0 \| - \| \| 8.2515 \| 103350 \| 0.0253 \| - \| \| 8.2555 \| 103400 \| 0.0 \| - \| \| 8.2595 \| 103450 \| 0.0148 \| - \| \| 8.2635 \| 103500 \| 0.0 \| - \| \| 8.2675 \| 103550 \| 0.0 \| - \| \| 8.2715 \| 103600 \| 0.0001 \| - \| \| 8.2754 \| 103650 \| 0.0271 \| - \| \| 8.2794 \| 103700 \| 0.0 \| - \| \| 8.2834 \| 103750 \| 0.0 \| - \| \| 8.2874 \| 103800 \| 0.0 \| - \| \| 8.2914 \| 103850 \| 0.0 \| - \| \| 8.2954 \| 103900 \| 0.0108 \| - \| \| 8.2994 \| 103950 \| 0.0245 \| - \| \| 8.3034 \| 104000 \| 0.0 \| - \| \| 8.3074 \| 104050 \| 0.0248 \| - \| \| 8.3114 \| 104100 \| 0.0 \| - \| \| 8.3154 \| 104150 \| 0.0 \| - \| \| 8.3194 \| 104200 \| 0.0 \| - \| \| 8.3234 \| 104250 \| 0.0001 \| - \| \| 8.3273 \| 104300 \| 0.0 \| - \| \| 8.3313 \| 104350 \| 0.0098 \| - \| \| 8.3353 \| 104400 \| 0.0 \| - \| \| 8.3393 \| 104450 \| 0.0247 \| - \| \| 8.3433 \| 104500 \| 0.0001 \| - \| \| 8.3473 \| 104550 \| 0.0 \| - \| \| 8.3513 \| 104600 \| 0.0 \| - \| \| 8.3553 \| 104650 \| 0.0001 \| - \| \| 8.3593 \| 104700 \| 0.0 \| - \| \| 8.3633 \| 104750 \| 0.0247 \| - \| \| 8.3673 \| 104800 \| 0.0 \| - \| \| 8.3713 \| 104850 \| 0.0001 \| - \| \| 8.3752 \| 104900 \| 0.0001 \| - \| \| 8.3792 \| 104950 \| 0.0 \| - \| \| 8.3832 \| 105000 \| 0.0 \| - \| \| 8.3872 \| 105050 \| 0.0 \| - \| \| 8.3912 \| 105100 \| 0.0 \| - \| \| 8.3952 \| 105150 \| 0.0 \| - \| \| 8.3992 \| 105200 \| 0.0001 \| - \| \| 8.4032 \| 105250 \| 0.0 \| - \| \| 8.4072 \| 105300 \| 0.0001 \| - \| \| 8.4112 \| 105350 \| 0.0001 \| - \| \| 8.4152 \| 105400 \| 0.0 \| - \| \| 8.4192 \| 105450 \| 0.0 \| - \| \| 8.4232 \| 105500 \| 0.025 \| - \| \| 8.4271 \| 105550 \| 0.0 \| - \| \| 8.4311 \| 105600 \| 0.0 \| - \| \| 8.4351 \| 105650 \| 0.0 \| - \| \| 8.4391 \| 105700 \| 0.0 \| - \| \| 8.4431 \| 105750 \| 0.0001 \| - \| \| 8.4471 \| 105800 \| 0.0 \| - \| \| 8.4511 \| 105850 \| 0.0 \| - \| \| 8.4551 \| 105900 \| 0.0001 \| - \| \| 8.4591 \| 105950 \| 0.0246 \| - \| \| 8.4631 \| 106000 \| 0.0 \| - \| \| 8.4671 \| 106050 \| 0.0 \| - \| \| 8.4711 \| 106100 \| 0.0246 \| - \| \| 8.4750 \| 106150 \| 0.0001 \| - \| \| 8.4790 \| 106200 \| 0.0 \| - \| \| 8.4830 \| 106250 \| 0.0 \| - \| \| 8.4870 \| 106300 \| 0.0246 \| - \| \| 8.4910 \| 106350 \| 0.0 \| - \| \| 8.4950 \| 106400 \| 0.0 \| - \| \| 8.4990 \| 106450 \| 0.0001 \| - \| \| 8.5030 \| 106500 \| 0.0001 \| - \| \| 8.5070 \| 106550 \| 0.0 \| - \| \| 8.5110 \| 106600 \| 0.0 \| - \| \| 8.5150 \| 106650 \| 0.0001 \| - \| \| 8.5190 \| 106700 \| 0.0 \| - \| \| 8.5230 \| 106750 \| 0.0 \| - \| \| 8.5269 \| 106800 \| 0.0 \| - \| \| 8.5309 \| 106850 \| 0.0001 \| - \| \| 8.5349 \| 106900 \| 0.0 \| - \| \| 8.5389 \| 106950 \| 0.0 \| - \| \| 8.5429 \| 107000 \| 0.0001 \| - \| \| 8.5469 \| 107050 \| 0.0 \| - \| \| 8.5509 \| 107100 \| 0.0 \| - \| \| 8.5549 \| 107150 \| 0.0 \| - \| \| 8.5589 \| 107200 \| 0.0 \| - \| \| 8.5629 \| 107250 \| 0.0001 \| - \| \| 8.5669 \| 107300 \| 0.0 \| - \| \| 8.5709 \| 107350 \| 0.0 \| - \| \| 8.5749 \| 107400 \| 0.0001 \| - \| \| 8.5788 \| 107450 \| 0.0251 \| - \| \| 8.5828 \| 107500 \| 0.0 \| - \| \| 8.5868 \| 107550 \| 0.0 \| - \| \| 8.5908 \| 107600 \| 0.0001 \| - \| \| 8.5948 \| 107650 \| 0.0 \| - \| \| 8.5988 \| 107700 \| 0.0 \| - \| \| 8.6028 \| 107750 \| 0.0 \| - \| \| 8.6068 \| 107800 \| 0.0001 \| - \| \| 8.6108 \| 107850 \| 0.0 \| - \| \| 8.6148 \| 107900 \| 0.0 \| - \| \| 8.6188 \| 107950 \| 0.0245 \| - \| \| 8.6228 \| 108000 \| 0.0 \| - \| \| 8.6267 \| 108050 \| 0.0 \| - \| \| 8.6307 \| 108100 \| 0.0249 \| - \| \| 8.6347 \| 108150 \| 0.0 \| - \| \| 8.6387 \| 108200 \| 0.0246 \| - \| \| 8.6427 \| 108250 \| 0.0 \| - \| \| 8.6467 \| 108300 \| 0.0001 \| - \| \| 8.6507 \| 108350 \| 0.0001 \| - \| \| 8.6547 \| 108400 \| 0.0001 \| - \| \| 8.6587 \| 108450 \| 0.0 \| - \| \| 8.6627 \| 108500 \| 0.0 \| - \| \| 8.6667 \| 108550 \| 0.0 \| - \| \| 8.6707 \| 108600 \| 0.0 \| - \| \| 8.6747 \| 108650 \| 0.0 \| - \| \| 8.6786 \| 108700 \| 0.0 \| - \| \| 8.6826 \| 108750 \| 0.0 \| - \| \| 8.6866 \| 108800 \| 0.0001 \| - \| \| 8.6906 \| 108850 \| 0.0 \| - \| \| 8.6946 \| 108900 \| 0.0 \| - \| \| 8.6986 \| 108950 \| 0.0 \| - \| \| 8.7026 \| 109000 \| 0.0 \| - \| \| 8.7066 \| 109050 \| 0.0248 \| - \| \| 8.7106 \| 109100 \| 0.0001 \| - \| \| 8.7146 \| 109150 \| 0.0 \| - \| \| 8.7186 \| 109200 \| 0.0 \| - \| \| 8.7226 \| 109250 \| 0.0 \| - \| \| 8.7265 \| 109300 \| 0.0246 \| - \| \| 8.7305 \| 109350 \| 0.0001 \| - \| \| 8.7345 \| 109400 \| 0.0 \| - \| \| 8.7385 \| 109450 \| 0.025 \| - \| \| 8.7425 \| 109500 \| 0.0 \| - \| \| 8.7465 \| 109550 \| 0.0 \| - \| \| 8.7505 \| 109600 \| 0.0 \| - \| \| 8.7545 \| 109650 \| 0.0 \| - \| \| 8.7585 \| 109700 \| 0.025 \| - \| \| 8.7625 \| 109750 \| 0.0001 \| - \| \| 8.7665 \| 109800 \| 0.0001 \| - \| \| 8.7705 \| 109850 \| 0.0248 \| - \| \| 8.7745 \| 109900 \| 0.0001 \| - \| \| 8.7784 \| 109950 \| 0.0 \| - \| \| 8.7824 \| 110000 \| 0.0 \| - \| \| 8.7864 \| 110050 \| 0.0 \| - \| \| 8.7904 \| 110100 \| 0.0 \| - \| \| 8.7944 \| 110150 \| 0.0 \| - \| \| 8.7984 \| 110200 \| 0.0001 \| - \| \| 8.8024 \| 110250 \| 0.0 \| - \| \| 8.8064 \| 110300 \| 0.0 \| - \| \| 8.8104 \| 110350 \| 0.0 \| - \| \| 8.8144 \| 110400 \| 0.0 \| - \| \| 8.8184 \| 110450 \| 0.0001 \| - \| \| 8.8224 \| 110500 \| 0.0001 \| - \| \| 8.8263 \| 110550 \| 0.0 \| - \| \| 8.8303 \| 110600 \| 0.0001 \| - \| \| 8.8343 \| 110650 \| 0.0 \| - \| \| 8.8383 \| 110700 \| 0.0 \| - \| \| 8.8423 \| 110750 \| 0.0 \| - \| \| 8.8463 \| 110800 \| 0.0247 \| - \| \| 8.8503 \| 110850 \| 0.0 \| - \| \| 8.8543 \| 110900 \| 0.0 \| - \| \| 8.8583 \| 110950 \| 0.0 \| - \| \| 8.8623 \| 111000 \| 0.0 \| - \| \| 8.8663 \| 111050 \| 0.0001 \| - \| \| 8.8703 \| 111100 \| 0.0 \| - \| \| 8.8743 \| 111150 \| 0.0001 \| - \| \| 8.8782 \| 111200 \| 0.0001 \| - \| \| 8.8822 \| 111250 \| 0.0 \| - \| \| 8.8862 \| 111300 \| 0.0 \| - \| \| 8.8902 \| 111350 \| 0.0001 \| - \| \| 8.8942 \| 111400 \| 0.0 \| - \| \| 8.8982 \| 111450 \| 0.0 \| - \| \| 8.9022 \| 111500 \| 0.0 \| - \| \| 8.9062 \| 111550 \| 0.0 \| - \| \| 8.9102 \| 111600 \| 0.0 \| - \| \| 8.9142 \| 111650 \| 0.0 \| - \| \| 8.9182 \| 111700 \| 0.0 \| - \| \| 8.9222 \| 111750 \| 0.0 \| - \| \| 8.9261 \| 111800 \| 0.0247 \| - \| \| 8.9301 \| 111850 \| 0.0 \| - \| \| 8.9341 \| 111900 \| 0.0248 \| - \| \| 8.9381 \| 111950 \| 0.0 \| - \| \| 8.9421 \| 112000 \| 0.0 \| - \| \| 8.9461 \| 112050 \| 0.0 \| - \| \| 8.9501 \| 112100 \| 0.0 \| - \| \| 8.9541 \| 112150 \| 0.0 \| - \| \| 8.9581 \| 112200 \| 0.0 \| - \| \| 8.9621 \| 112250 \| 0.0001 \| - \| \| 8.9661 \| 112300 \| 0.0 \| - \| \| 8.9701 \| 112350 \| 0.0001 \| - \| \| 8.9741 \| 112400 \| 0.0001 \| - \| \| 8.9780 \| 112450 \| 0.0247 \| - \| \| 8.9820 \| 112500 \| 0.0496 \| - \| \| 8.9860 \| 112550 \| 0.0 \| - \| \| 8.9900 \| 112600 \| 0.0001 \| - \| \| 8.9940 \| 112650 \| 0.0 \| - \| \| 8.9980 \| 112700 \| 0.0 \| - \| \| 9.0 \| 112725 \| - \| 0.0579 \| \| 9.0020 \| 112750 \| 0.0493 \| - \| \| 9.0060 \| 112800 \| 0.0 \| - \| \| 9.0100 \| 112850 \| 0.0001 \| - \| \| 9.0140 \| 112900 \| 0.0001 \| - \| \| 9.0180 \| 112950 \| 0.0 \| - \| \| 9.0220 \| 113000 \| 0.0 \| - \| \| 9.0259 \| 113050 \| 0.0 \| - \| \| 9.0299 \| 113100 \| 0.0 \| - \| \| 9.0339 \| 113150 \| 0.0001 \| - \| \| 9.0379 \| 113200 \| 0.0 \| - \| \| 9.0419 \| 113250 \| 0.0 \| - \| \| 9.0459 \| 113300 \| 0.0 \| - \| \| 9.0499 \| 113350 \| 0.0 \| - \| \| 9.0539 \| 113400 \| 0.0 \| - \| \| 9.0579 \| 113450 \| 0.0 \| - \| \| 9.0619 \| 113500 \| 0.0 \| - \| \| 9.0659 \| 113550 \| 0.0246 \| - \| \| 9.0699 \| 113600 \| 0.0 \| - \| \| 9.0739 \| 113650 \| 0.0 \| - \| \| 9.0778 \| 113700 \| 0.0001 \| - \| \| 9.0818 \| 113750 \| 0.0001 \| - \| \| 9.0858 \| 113800 \| 0.0 \| - \| \| 9.0898 \| 113850 \| 0.0001 \| - \| \| 9.0938 \| 113900 \| 0.0 \| - \| \| 9.0978 \| 113950 \| 0.0 \| - \| \| 9.1018 \| 114000 \| 0.0 \| - \| \| 9.1058 \| 114050 \| 0.0 \| - \| \| 9.1098 \| 114100 \| 0.0 \| - \| \| 9.1138 \| 114150 \| 0.0 \| - \| \| 9.1178 \| 114200 \| 0.0 \| - \| \| 9.1218 \| 114250 \| 0.0 \| - \| \| 9.1257 \| 114300 \| 0.0001 \| - \| \| 9.1297 \| 114350 \| 0.0 \| - \| \| 9.1337 \| 114400 \| 0.0001 \| - \| \| 9.1377 \| 114450 \| 0.0 \| - \| \| 9.1417 \| 114500 \| 0.0 \| - \| \| 9.1457 \| 114550 \| 0.0001 \| - \| \| 9.1497 \| 114600 \| 0.0 \| - \| \| 9.1537 \| 114650 \| 0.0 \| - \| \| 9.1577 \| 114700 \| 0.0 \| - \| \| 9.1617 \| 114750 \| 0.0001 \| - \| \| 9.1657 \| 114800 \| 0.0 \| - \| \| 9.1697 \| 114850 \| 0.0 \| - \| \| 9.1737 \| 114900 \| 0.0 \| - \| \| 9.1776 \| 114950 \| 0.0247 \| - \| \| 9.1816 \| 115000 \| 0.0001 \| - \| \| 9.1856 \| 115050 \| 0.0001 \| - \| \| 9.1896 \| 115100 \| 0.0001 \| - \| \| 9.1936 \| 115150 \| 0.0 \| - \| \| 9.1976 \| 115200 \| 0.0 \| - \| \| 9.2016 \| 115250 \| 0.0 \| - \| \| 9.2056 \| 115300 \| 0.0247 \| - \| \| 9.2096 \| 115350 \| 0.0 \| - \| \| 9.2136 \| 115400 \| 0.0 \| - \| \| 9.2176 \| 115450 \| 0.0 \| - \| \| 9.2216 \| 115500 \| 0.0 \| - \| \| 9.2255 \| 115550 \| 0.0 \| - \| \| 9.2295 \| 115600 \| 0.0245 \| - \| \| 9.2335 \| 115650 \| 0.0248 \| - \| \| 9.2375 \| 115700 \| 0.0 \| - \| \| 9.2415 \| 115750 \| 0.0001 \| - \| \| 9.2455 \| 115800 \| 0.0 \| - \| \| 9.2495 \| 115850 \| 0.0 \| - \| \| 9.2535 \| 115900 \| 0.0 \| - \| \| 9.2575 \| 115950 \| 0.0246 \| - \| \| 9.2615 \| 116000 \| 0.0 \| - \| \| 9.2655 \| 116050 \| 0.0 \| - \| \| 9.2695 \| 116100 \| 0.0 \| - \| \| 9.2735 \| 116150 \| 0.0 \| - \| \| 9.2774 \| 116200 \| 0.0 \| - \| \| 9.2814 \| 116250 \| 0.0246 \| - \| \| 9.2854 \| 116300 \| 0.0 \| - \| \| 9.2894 \| 116350 \| 0.0 \| - \| \| 9.2934 \| 116400 \| 0.0247 \| - \| \| 9.2974 \| 116450 \| 0.0245 \| - \| \| 9.3014 \| 116500 \| 0.0 \| - \| \| 9.3054 \| 116550 \| 0.0 \| - \| \| 9.3094 \| 116600 \| 0.0 \| - \| \| 9.3134 \| 116650 \| 0.0244 \| - \| \| 9.3174 \| 116700 \| 0.0001 \| - \| \| 9.3214 \| 116750 \| 0.0 \| - \| \| 9.3253 \| 116800 \| 0.0001 \| - \| \| 9.3293 \| 116850 \| 0.0232 \| - \| \| 9.3333 \| 116900 \| 0.0192 \| - \| \| 9.3373 \| 116950 \| 0.0246 \| - \| \| 9.3413 \| 117000 \| 0.0 \| - \| \| 9.3453 \| 117050 \| 0.0005 \| - \| \| 9.3493 \| 117100 \| 0.0007 \| - \| \| 9.3533 \| 117150 \| 0.0002 \| - \| \| 9.3573 \| 117200 \| 0.0001 \| - \| \| 9.3613 \| 117250 \| 0.0244 \| - \| \| 9.3653 \| 117300 \| 0.0002 \| - \| \| 9.3693 \| 117350 \| 0.0188 \| - \| \| 9.3733 \| 117400 \| 0.0001 \| - \| \| 9.3772 \| 117450 \| 0.0003 \| - \| \| 9.3812 \| 117500 \| 0.001 \| - \| \| 9.3852 \| 117550 \| 0.0 \| - \| \| 9.3892 \| 117600 \| 0.0001 \| - \| \| 9.3932 \| 117650 \| 0.0001 \| - \| \| 9.3972 \| 117700 \| 0.0003 \| - \| \| 9.4012 \| 117750 \| 0.0029 \| - \| \| 9.4052 \| 117800 \| 0.0003 \| - \| \| 9.4092 \| 117850 \| 0.0026 \| - \| \| 9.4132 \| 117900 \| 0.0019 \| - \| \| 9.4172 \| 117950 \| 0.0002 \| - \| \| 9.4212 \| 118000 \| 0.0007 \| - \| \| 9.4251 \| 118050 \| 0.0 \| - \| \| 9.4291 \| 118100 \| 0.0019 \| - \| \| 9.4331 \| 118150 \| 0.004 \| - \| \| 9.4371 \| 118200 \| 0.001 \| - \| \| 9.4411 \| 118250 \| 0.0016 \| - \| \| 9.4451 \| 118300 \| 0.0028 \| - \| \| 9.4491 \| 118350 \| 0.0001 \| - \| \| 9.4531 \| 118400 \| 0.0 \| - \| \| 9.4571 \| 118450 \| 0.0105 \| - \| \| 9.4611 \| 118500 \| 0.0013 \| - \| \| 9.4651 \| 118550 \| 0.0 \| - \| \| 9.4691 \| 118600 \| 0.0221 \| - \| \| 9.4731 \| 118650 \| 0.0001 \| - \| \| 9.4770 \| 118700 \| 0.0008 \| - \| \| 9.4810 \| 118750 \| 0.0001 \| - \| \| 9.4850 \| 118800 \| 0.0214 \| - \| \| 9.4890 \| 118850 \| 0.0001 \| - \| \| 9.4930 \| 118900 \| 0.0018 \| - \| \| 9.4970 \| 118950 \| 0.0011 \| - \| \| 9.5010 \| 119000 \| 0.0001 \| - \| \| 9.5050 \| 119050 \| 0.0009 \| - \| \| 9.5090 \| 119100 \| 0.0004 \| - \| \| 9.5130 \| 119150 \| 0.0004 \| - \| \| 9.5170 \| 119200 \| 0.0034 \| - \| \| 9.5210 \| 119250 \| 0.0016 \| - \| \| 9.5250 \| 119300 \| 0.0006 \| - \| \| 9.5289 \| 119350 \| 0.0 \| - \| \| 9.5329 \| 119400 \| 0.0001 \| - \| \| 9.5369 \| 119450 \| 0.0041 \| - \| \| 9.5409 \| 119500 \| 0.0029 \| - \| \| 9.5449 \| 119550 \| 0.0001 \| - \| \| 9.5489 \| 119600 \| 0.0189 \| - \| \| 9.5529 \| 119650 \| 0.0001 \| - \| \| 9.5569 \| 119700 \| 0.0 \| - \| \| 9.5609 \| 119750 \| 0.0 \| - \| \| 9.5649 \| 119800 \| 0.0042 \| - \| \| 9.5689 \| 119850 \| 0.0009 \| - \| \| 9.5729 \| 119900 \| 0.0 \| - \| \| 9.5768 \| 119950 \| 0.0 \| - \| \| 9.5808 \| 120000 \| 0.0 \| - \| \| 9.5848 \| 120050 \| 0.0007 \| - \| \| 9.5888 \| 120100 \| 0.0009 \| - \| \| 9.5928 \| 120150 \| 0.0006 \| - \| \| 9.5968 \| 120200 \| 0.0001 \| - \| \| 9.6008 \| 120250 \| 0.0001 \| - \| \| 9.6048 \| 120300 \| 0.0007 \| - \| \| 9.6088 \| 120350 \| 0.0001 \| - \| \| 9.6128 \| 120400 \| 0.0025 \| - \| \| 9.6168 \| 120450 \| 0.0136 \| - \| \| 9.6208 \| 120500 \| 0.0011 \| - \| \| 9.6248 \| 120550 \| 0.002 \| - \| \| 9.6287 \| 120600 \| 0.001 \| - \| \| 9.6327 \| 120650 \| 0.0008 \| - \| \| 9.6367 \| 120700 \| 0.0298 \| - \| \| 9.6407 \| 120750 \| 0.009 \| - \| \| 9.6447 \| 120800 \| 0.0042 \| - \| \| 9.6487 \| 120850 \| 0.0011 \| - \| \| 9.6527 \| 120900 \| 0.0089 \| - \| \| 9.6567 \| 120950 \| 0.0054 \| - \| \| 9.6607 \| 121000 \| 0.0019 \| - \| \| 9.6647 \| 121050 \| 0.0006 \| - \| \| 9.6687 \| 121100 \| 0.0 \| - \| \| 9.6727 \| 121150 \| 0.0 \| - \| \| 9.6766 \| 121200 \| 0.0001 \| - \| \| 9.6806 \| 121250 \| 0.0001 \| - \| \| 9.6846 \| 121300 \| 0.0 \| - \| \| 9.6886 \| 121350 \| 0.0128 \| - \| \| 9.6926 \| 121400 \| 0.0 \| - \| \| 9.6966 \| 121450 \| 0.0001 \| - \| \| 9.7006 \| 121500 \| 0.0 \| - \| \| 9.7046 \| 121550 \| 0.0007 \| - \| \| 9.7086 \| 121600 \| 0.0001 \| - \| \| 9.7126 \| 121650 \| 0.0001 \| - \| \| 9.7166 \| 121700 \| 0.0021 \| - \| \| 9.7206 \| 121750 \| 0.0001 \| - \| \| 9.7246 \| 121800 \| 0.0207 \| - \| \| 9.7285 \| 121850 \| 0.0001 \| - \| \| 9.7325 \| 121900 \| 0.0032 \| - \| \| 9.7365 \| 121950 \| 0.0008 \| - \| \| 9.7405 \| 122000 \| 0.0038 \| - \| \| 9.7445 \| 122050 \| 0.0005 \| - \| \| 9.7485 \| 122100 \| 0.0002 \| - \| \| 9.7525 \| 122150 \| 0.0005 \| - \| \| 9.7565 \| 122200 \| 0.0043 \| - \| \| 9.7605 \| 122250 \| 0.0003 \| - \| \| 9.7645 \| 122300 \| 0.021 \| - \| \| 9.7685 \| 122350 \| 0.0128 \| - \| \| 9.7725 \| 122400 \| 0.0032 \| - \| \| 9.7764 \| 122450 \| 0.0001 \| - \| \| 9.7804 \| 122500 \| 0.0 \| - \| \| 9.7844 \| 122550 \| 0.0119 \| - \| \| 9.7884 \| 122600 \| 0.0 \| - \| \| 9.7924 \| 122650 \| 0.0 \| - \| \| 9.7964 \| 122700 \| 0.0 \| - \| \| 9.8004 \| 122750 \| 0.0092 \| - \| \| 9.8044 \| 122800 \| 0.0001 \| - \| \| 9.8084 \| 122850 \| 0.0008 \| - \| \| 9.8124 \| 122900 \| 0.0009 \| - \| \| 9.8164 \| 122950 \| 0.0021 \| - \| \| 9.8204 \| 123000 \| 0.0 \| - \| \| 9.8244 \| 123050 \| 0.0174 \| - \| \| 9.8283 \| 123100 \| 0.0001 \| - \| \| 9.8323 \| 123150 \| 0.0095 \| - \| \| 9.8363 \| 123200 \| 0.0183 \| - \| \| 9.8403 \| 123250 \| 0.0001 \| - \| \| 9.8443 \| 123300 \| 0.0002 \| - \| \| 9.8483 \| 123350 \| 0.0 \| - \| \| 9.8523 \| 123400 \| 0.0004 \| - \| \| 9.8563 \| 123450 \| 0.0 \| - \| \| 9.8603 \| 123500 \| 0.0001 \| - \| \| 9.8643 \| 123550 \| 0.0028 \| - \| \| 9.8683 \| 123600 \| 0.0 \| - \| \| 9.8723 \| 123650 \| 0.0001 \| - \| \| 9.8762 \| 123700 \| 0.0 \| - \| \| 9.8802 \| 123750 \| 0.0004 \| - \| \| 9.8842 \| 123800 \| 0.0035 \| - \| \| 9.8882 \| 123850 \| 0.0001 \| - \| \| 9.8922 \| 123900 \| 0.0 \| - \| \| 9.8962 \| 123950 \| 0.0001 \| - \| \| 9.9002 \| 124000 \| 0.0038 \| - \| \| 9.9042 \| 124050 \| 0.0028 \| - \| \| 9.9082 \| 124100 \| 0.0002 \| - \| \| 9.9122 \| 124150 \| 0.0001 \| - \| \| 9.9162 \| 124200 \| 0.0 \| - \| \| 9.9202 \| 124250 \| 0.0005 \| - \| \| 9.9242 \| 124300 \| 0.016 \| - \| \| 9.9281 \| 124350 \| 0.0001 \| - \| \| 9.9321 \| 124400 \| 0.0001 \| - \| \| 9.9361 \| 124450 \| 0.0 \| - \| \| 9.9401 \| 124500 \| 0.0009 \| - \| \| 9.9441 \| 124550 \| 0.0 \| - \| \| 9.9481 \| 124600 \| 0.0015 \| - \| \| 9.9521 \| 124650 \| 0.0 \| - \| \| 9.9561 \| 124700 \| 0.0 \| - \| \| 9.9601 \| 124750 \| 0.0002 \| - \| \| 9.9641 \| 124800 \| 0.0 \| - \| \| 9.9681 \| 124850 \| 0.0028 \| - \| \| 9.9721 \| 124900 \| 0.0004 \| - \| \| 9.9760 \| 124950 \| 0.014 \| - \| \| 9.9800 \| 125000 \| 0.0138 \| - \| \| 9.9840 \| 125050 \| 0.0008 \| - \| \| 9.9880 \| 125100 \| 0.0001 \| - \| \| 9.9920 \| 125150 \| 0.0 \| - \| \| 9.9960 \| 125200 \| 0.0136 \| - \| \| 10.0 \| 125250 \| 0.0182 \| 0.0777 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - SetFit: 1.0.3 - Sentence Transformers: 3.0.1 - Transformers: 4.39.0 - PyTorch: 2.4.0 - Datasets: 2.21.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
jeanviet/lora-jeanviet-flux1	jeanviet	2024-08-30T22:03:54Z	5	1	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-08-30T21:57:53Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # lora jeanviet flux1 <Gallery /> ## Model description Flux Lora de Jeanviet ## Trigger words You should use `` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/jeanviet/lora-jeanviet-flux1/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-general-training](https://fal.ai/models/fal-ai/flux-lora-general-training).
nagthgr8/subject-gpt2	nagthgr8	2024-08-30T21:54:51Z	116	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T21:54:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jimmycarter/flux-training-seuss-lora-bsz-4	jimmycarter	2024-08-30T21:45:45Z	5	0	diffusers	[ "diffusers", "flux", "flux-diffusers", "text-to-image", "simpletuner", "lora", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-08-30T21:20:00Z	--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - lora - template:sd-lora inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'Dr. Seuss during a book signing event, seated at a table with an open book and pen in hand, his characteristic white beard, clear-rimmed glasses, and whimsical bow tie complementing his calm, attentive expression, all within the literary setting of a bookstore, reflecting his enduring connection with readers and the joy his work brought to many.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'Anime picture of famed author Dr. Seuss in a Studio Ghibli style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'Dr. Seuss in a leather jacket riding a Harley Davidson Motorcycle' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'Famous author Dr. Seuss holding a chainsaw while riding around on a unicycle, vintage TV still from the Dick Van Dyke show' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'A photograph of Dr. Seuss riding in a horse-drawn carriage' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png --- # flux-training-seuss-lora-bsz-4 This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` A photograph of Dr. Seuss riding in a horse-drawn carriage ``` ## Validation settings - CFG: `3.5` - CFG Rescale: `0.0` - Steps: `15` - Sampler: `None` - Seed: `42` - Resolution: `1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder was not trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 0 - Training steps: 200 - Learning rate: 0.0008 - Effective batch size: 4 - Micro-batch size: 4 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: bf16 - Quantised: No - Xformers: Not used - LoRA Rank: 16 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### default_dataset_arb - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 3 - Resolution: 1.5 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### default_dataset - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_512 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_576 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.331776 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_640 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.4096 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_704 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.495616 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_768 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_832 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.692224 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_896 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.802816 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_960 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.9216 megapixels - Cropped: True - Crop style: random - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'jimmycarter/flux-training-seuss-lora-bsz-4' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "A photograph of Dr. Seuss riding in a horse-drawn carriage" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, num_inference_steps=15, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.5, ).images[0] image.save("output.png", format="PNG") ```
Sharan1712/llama2_7B_unnaturalcore_qdora_loftq_4bit_6c	Sharan1712	2024-08-30T21:42:44Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-08-30T21:40:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Rodrigo1771/BioLinkBERT-base-drugtemist-en-ner	Rodrigo1771	2024-08-30T21:42:27Z	6	0	null	[ "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:Rodrigo1771/drugtemist-en-ner", "base_model:michiyasunaga/BioLinkBERT-base", "base_model:finetune:michiyasunaga/BioLinkBERT-base", "license:apache-2.0", "model-index", "region:us" ]	token-classification	2024-08-30T21:24:16Z	--- license: apache-2.0 base_model: michiyasunaga/BioLinkBERT-base tags: - token-classification - generated_from_trainer datasets: - Rodrigo1771/drugtemist-en-ner metrics: - precision - recall - f1 - accuracy model-index: - name: output results: - task: name: Token Classification type: token-classification dataset: name: Rodrigo1771/drugtemist-en-ner type: Rodrigo1771/drugtemist-en-ner config: DrugTEMIST English NER split: validation args: DrugTEMIST English NER metrics: - name: Precision type: precision value: 0.9327102803738317 - name: Recall type: recall value: 0.9301025163094129 - name: F1 type: f1 value: 0.9314045730284647 - name: Accuracy type: accuracy value: 0.9986953367008066 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the Rodrigo1771/drugtemist-en-ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0056 - Precision: 0.9327 - Recall: 0.9301 - F1: 0.9314 - Accuracy: 0.9987 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 434 \| 0.0057 \| 0.8938 \| 0.8938 \| 0.8938 \| 0.9981 \| \| 0.0182 \| 2.0 \| 868 \| 0.0044 \| 0.9024 \| 0.9301 \| 0.9160 \| 0.9985 \| \| 0.0039 \| 3.0 \| 1302 \| 0.0045 \| 0.9129 \| 0.9282 \| 0.9205 \| 0.9987 \| \| 0.0024 \| 4.0 \| 1736 \| 0.0051 \| 0.8821 \| 0.9348 \| 0.9077 \| 0.9983 \| \| 0.0017 \| 5.0 \| 2170 \| 0.0057 \| 0.9251 \| 0.9320 \| 0.9285 \| 0.9986 \| \| 0.0012 \| 6.0 \| 2604 \| 0.0061 \| 0.9001 \| 0.9236 \| 0.9117 \| 0.9984 \| \| 0.0009 \| 7.0 \| 3038 \| 0.0056 \| 0.9327 \| 0.9301 \| 0.9314 \| 0.9987 \| \| 0.0009 \| 8.0 \| 3472 \| 0.0068 \| 0.9118 \| 0.9348 \| 0.9231 \| 0.9986 \| \| 0.0006 \| 9.0 \| 3906 \| 0.0072 \| 0.9267 \| 0.9310 \| 0.9289 \| 0.9987 \| \| 0.0004 \| 10.0 \| 4340 \| 0.0073 \| 0.9192 \| 0.9329 \| 0.9260 \| 0.9986 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
mradermacher/yaoi-v1-instruct-GGUF	mradermacher	2024-08-30T21:41:09Z	18	1	transformers	[ "transformers", "gguf", "code", "yaoi", "en", "base_model:Ichate/yaoi-v1-instruct", "base_model:quantized:Ichate/yaoi-v1-instruct", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-08-30T12:31:07Z	--- base_model: Ichate/yaoi-v1-instruct language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - code - yaoi --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Ichate/yaoi-v1-instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/yaoi-v1-instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
jeanviet/flux-lora	jeanviet	2024-08-30T21:35:51Z	6	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-08-30T21:35:45Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: Portrait Photo license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # flux lora <Gallery /> ## Model description Flux Lora de Jeanviet ## Trigger words You should use `Portrait Photo` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/jeanviet/flux-lora/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-general-training](https://fal.ai/models/fal-ai/flux-lora-general-training).
RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf	RichardErkhov	2024-08-30T21:20:17Z	74	0	null	[ "gguf", "arxiv:2401.06466", "endpoints_compatible", "region:us" ]	null	2024-08-30T19:08:18Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) PersianMind-v1.0 - GGUF - Model creator: https://huggingface.co/universitytehran/ - Original model: https://huggingface.co/universitytehran/PersianMind-v1.0/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [PersianMind-v1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q2_K.gguf) \| Q2_K \| 2.4GB \| \| [PersianMind-v1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ3_XS.gguf) \| IQ3_XS \| 2.65GB \| \| [PersianMind-v1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ3_S.gguf) \| IQ3_S \| 2.79GB \| \| [PersianMind-v1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K_S.gguf) \| Q3_K_S \| 2.79GB \| \| [PersianMind-v1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ3_M.gguf) \| IQ3_M \| 2.95GB \| \| [PersianMind-v1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K.gguf) \| Q3_K \| 3.12GB \| \| [PersianMind-v1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K_M.gguf) \| Q3_K_M \| 3.12GB \| \| [PersianMind-v1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K_L.gguf) \| Q3_K_L \| 3.4GB \| \| [PersianMind-v1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ4_XS.gguf) \| IQ4_XS \| 3.45GB \| \| [PersianMind-v1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_0.gguf) \| Q4_0 \| 3.61GB \| \| [PersianMind-v1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ4_NL.gguf) \| IQ4_NL \| 3.63GB \| \| [PersianMind-v1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_K_S.gguf) \| Q4_K_S \| 3.64GB \| \| [PersianMind-v1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_K.gguf) \| Q4_K \| 3.85GB \| \| [PersianMind-v1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_K_M.gguf) \| Q4_K_M \| 3.85GB \| \| [PersianMind-v1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_1.gguf) \| Q4_1 \| 4.0GB \| \| [PersianMind-v1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_0.gguf) \| Q5_0 \| 4.39GB \| \| [PersianMind-v1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_K_S.gguf) \| Q5_K_S \| 4.39GB \| \| [PersianMind-v1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_K.gguf) \| Q5_K \| 4.51GB \| \| [PersianMind-v1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_K_M.gguf) \| Q5_K_M \| 4.51GB \| \| [PersianMind-v1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_1.gguf) \| Q5_1 \| 4.77GB \| \| [PersianMind-v1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q6_K.gguf) \| Q6_K \| 5.21GB \| \| [PersianMind-v1.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q8_0.gguf) \| Q8_0 \| 6.75GB \| Original model description: --- license: cc-by-nc-sa-4.0 language: - multilingual - fa - en library_name: transformers tags: - text-generation-inference inference: false metrics: - bleu - comet - accuracy - perplexity - spearmanr pipeline_tag: text-generation co2_eq_emissions: emissions: 232380 --- <p align="center"> <img src="PersianMind.jpg" alt="PersianMind logo" width=200/> </p> # <span style="font-variant:small-caps;">PersianMind</span> <span style="font-variant:small-caps;">PersianMind</span> is a cross-lingual Persian-English large language model. The model achieves state-of-the-art results on Persian subset of the [<span style="font-variant:small-caps;">Belebele</span>](https://github.com/facebookresearch/belebele) benchmark and the [ParsiNLU multiple-choice QA](https://github.com/persiannlp/parsinlu) task. It also attains performance comparable to GPT-3.5-turbo in a Persian reading comprehension task. ## Model Description - Developed by: [Pedram Rostami](mailto:pedram.rostami@ut.ac.ir), [Ali Salemi](mailto:alisalemi@ut.ac.ir), and [Mohammad Javad Dousti](mailto:mjdousti@ut.ac.ir) - Model type: Language model - Languages: English and Persian - License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (non-commercial use only.) ## How to Get Started with the Model Use the code below to get started with the model. Note that you need to install <code><b>sentencepiece</b></code> and <code><b>accelerate</b></code> libraries along with <code><b>PyTorch</b></code> and <code><b>🤗Transformers</b></code> to run this code. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained( "universitytehran/PersianMind-v1.0", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map={"": device}, ) tokenizer = AutoTokenizer.from_pretrained( "universitytehran/PersianMind-v1.0", ) TEMPLATE = "{context}\nYou: {prompt}\nPersianMind: " CONTEXT = "This is a conversation with PersianMind. It is an artificial intelligence model designed by a team of " \ "NLP experts at the University of Tehran to help you with various tasks such as answering questions, " \ "providing recommendations, and helping with decision making. You can ask it anything you want and " \ "it will do its best to give you accurate and relevant information." PROMPT = "در مورد هوش مصنوعی توضیح بده." model_input = TEMPLATE.format(context=CONTEXT, prompt=PROMPT) input_tokens = tokenizer(model_input, return_tensors="pt") input_tokens = input_tokens.to(device) generate_ids = model.generate(input_tokens, max_new_tokens=512, do_sample=False, repetition_penalty=1.1) model_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(model_output[len(model_input):]) ``` ### How to Quantize the Model Quantized models can be run on resource-constrained devices. To quantize the model, you should install the <code><b>bitsandbytes</b></code> library. In order to quantize the model in 8-bit (`INT8`), use the code below. ```python model = AutoModelForCausalLM.from_pretrained( "universitytehran/PersianMind-v1.0", device_map="auto", low_cpu_mem_usage=True, load_in_8bit=True ) ``` Alternatively, you can quantize the model in 4-bit (`NormalFloat4`) with the following code. ```python from transformers import BitsAndBytesConfig quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", ) model = AutoModelForCausalLM.from_pretrained( "universitytehran/PersianMind-v1.0", quantization_config=quantization_config, device_map="auto" ) ``` ### Evaluating Quantized Models \| Model \| <span style="font-variant:small-caps;">Belebele</span> (Persian) \| Fa→En Translation<br>(<span style="font-variant:small-caps;">Comet</span>) \| En→Fa Translation<br>(<span style="font-variant:small-caps;">Comet</span>) \| Model Size \| Tokens/sec \| \| :----------------------------------------------------------------: \| :--------------------------------------------------------------: \| :------------------------------------------------------------------------: \| :------------------------------------------------------------------------: \| :--------: \| :--------: \| \| <span style="font-variant:small-caps;">PersianMind</span> (`BF16`) \| 73.9 \| 83.61 \| 79.44 \| 13.7G \| 25.35 \| \| <span style="font-variant:small-caps;">PersianMind</span> (`INT8`) \| 73.7 \| 82.32 \| 78.61 \| 7.2G \| 11.36 \| \| <span style="font-variant:small-caps;">PersianMind</span> (`NF4`) \| 70.2 \| 82.07 \| 80.36 \| 3.9G \| 24.36 \| We evaluated quantized models in various tasks against the original model. Specifically, we evaluated all models using the reading comprehension multiple-choice question-answering benchmark of [<span style="font-variant:small-caps;">Belebele</span>](https://github.com/facebookresearch/belebele) (Persian subset) and reported the accuracy of each model. Additionally, we evaluated our models for Persian-to-English and English-to-Persian translation tasks. For this, we utilized the Persian-English subset of the [<span style="font-variant:small-caps;">Flores</span>-200](https://github.com/facebookresearch/flores/tree/main/flores200) dataset and reported our results using the <span style="font-variant:small-caps;">Comet</span> metric. Furthermore, we calculated the average number of generated tokens per second by each model during running the translation tasks. To understand resource efficiency, we measured the memory usage of each model by employing the `get_memory_footprint()` function. ## License <span style="font-variant:small-caps;">PersianMind</span> is subject to Meta's [LLaMa2 Community License](https://raw.githubusercontent.com/facebookresearch/llama/main/LICENSE). It is further licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/), which allows non-commercial use of the model. Commercial use of this model requires written agreement which must be obtained from the copyright holders who are listed as developers in this page. If you suspect any violations, please reach out to us. ## Citation If you find this model helpful, please ensure to cite the following paper. BibTeX:** ```bibtex @misc{persianmind, title={{PersianMind: A Cross-Lingual Persian-English Large Language Model}}, author={Rostami, Pedram and Salemi, Ali and Dousti, Mohammad Javad}, year={2024} eprint={2401.06466}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
clydeiii/scott-stapp-flux	clydeiii	2024-08-30T21:18:57Z	81	0	diffusers	[ "diffusers", "text-to-image", "lora", "flux", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-08-23T18:41:24Z	--- tags: - text-to-image - lora - diffusers - flux base_model: black-forest-labs/FLUX.1-dev instance_prompt: p3r5on widget: - text: p3r5on license: other --- ### Scott Stapp #### Flux LoRA --- # Prompt: p3r5on #### Sample pictures: !["" 0](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451563162__000003000_8.jpg) !["" 1](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451446248__000003000_5.jpg) !["" 2](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451368344__000003000_3.jpg) !["" 3](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451524223__000003000_7.jpg)
mradermacher/MN-12B-Lyra-v3-GGUF	mradermacher	2024-08-30T21:14:13Z	10	3	transformers	[ "transformers", "gguf", "en", "base_model:Sao10K/MN-12B-Lyra-v3", "base_model:quantized:Sao10K/MN-12B-Lyra-v3", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-29T07:52:37Z	--- base_model: Sao10K/MN-12B-Lyra-v3 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Sao10K/MN-12B-Lyra-v3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MN-12B-Lyra-v3-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Xilixmeaty40/vfdfdfdfdfd	Xilixmeaty40	2024-08-30T21:10:18Z	89	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "base_model:vinthony/SadTalker", "base_model:finetune:vinthony/SadTalker", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T20:26:42Z	--- license: apache-2.0 base_model: vinthony/SadTalker library_name: transformers ---
LouisSanna/orpo-model-output	LouisSanna	2024-08-30T20:57:31Z	119	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "trl", "orpo", "generated_from_trainer", "dataset:piqa", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T20:43:47Z	--- library_name: transformers license: mit base_model: openai-community/gpt2 tags: - trl - orpo - generated_from_trainer datasets: - piqa model-index: - name: orpo-model-output results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # orpo-model-output This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the piqa dataset. It achieves the following results on the evaluation set: - Loss: 3.2999 - Rewards/chosen: -0.3182 - Rewards/rejected: -0.3369 - Rewards/accuracies: 0.6693 - Rewards/margins: 0.0187 - Logps/rejected: -3.3688 - Logps/chosen: -3.1821 - Logits/rejected: -24.5739 - Logits/chosen: -24.6603 - Nll Loss: 3.2330 - Log Odds Ratio: -0.6695 - Log Odds Chosen: 0.2013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| Nll Loss \| Log Odds Ratio \| Log Odds Chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\|:--------:\|:--------------:\|:---------------:\| \| 3.1799 \| 1.0 \| 3223 \| 3.1575 \| -0.2992 \| -0.3103 \| 0.6472 \| 0.0111 \| -3.1030 \| -2.9917 \| -16.2781 \| -16.3180 \| 3.0900 \| -0.6758 \| 0.1201 \| \| 2.7077 \| 2.0 \| 6446 \| 3.1544 \| -0.3005 \| -0.3160 \| 0.6652 \| 0.0154 \| -3.1595 \| -3.0051 \| -21.7517 \| -21.8387 \| 3.0878 \| -0.6671 \| 0.1676 \| \| 2.2691 \| 3.0 \| 9669 \| 3.2999 \| -0.3182 \| -0.3369 \| 0.6693 \| 0.0187 \| -3.3688 \| -3.1821 \| -24.5739 \| -24.6603 \| 3.2330 \| -0.6695 \| 0.2013 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.0.0 - Datasets 2.16.1 - Tokenizers 0.19.1
ambrosfitz/tinyllama-history	ambrosfitz	2024-08-30T20:53:18Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "dataset:ambrosfitz/just_history_v2", "dataset:ambrosfitz/synth_history_sentences", "dataset:ambrosfitz/ps_history_txt", "dataset:ambrosfitz/might-history-merge_v2", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T00:43:28Z	--- library_name: transformers license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: tinyllama-history results: [] datasets: - ambrosfitz/just_history_v2 - ambrosfitz/synth_history_sentences - ambrosfitz/ps_history_txt - ambrosfitz/might-history-merge_v2 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama-history This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
stablediffusionapi/oxalis-anime-hentai-model	stablediffusionapi	2024-08-30T20:45:20Z	32	0	diffusers	[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-08-30T20:40:06Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Oxalis Anime Hentai Model API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/20484209241725050156.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "oxalis-anime-hentai-model" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/oxalis-anime-hentai-model) Model link: [View model](https://modelslab.com/models/oxalis-anime-hentai-model) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "oxalis-anime-hentai-model", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
stablediffusionapi/falkons-anime-and-hentai	stablediffusionapi	2024-08-30T20:45:20Z	29	0	diffusers	[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-08-30T20:40:53Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Falkons (Anime and Hentai) API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/14969003721725050261.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "falkons-anime-and-hentai" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/falkons-anime-and-hentai) Model link: [View model](https://modelslab.com/models/falkons-anime-and-hentai) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "falkons-anime-and-hentai", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
bartowski/c4ai-command-r-plus-08-2024-GGUF	bartowski	2024-08-30T20:45:08Z	5,335	19	transformers	[ "transformers", "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "base_model:CohereForAI/c4ai-command-r-plus-08-2024", "base_model:quantized:CohereForAI/c4ai-command-r-plus-08-2024", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2024-08-30T14:28:25Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox quantized_by: bartowski pipeline_tag: text-generation base_model: CohereForAI/c4ai-command-r-plus-08-2024 --- ## Llamacpp imatrix Quantizations of c4ai-command-r-plus-08-2024 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3634">b3634</a> for quantization. Original model: https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) ## Prompt format ``` <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|SYSTEM_TOKEN\|>{system_prompt}<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>{prompt}<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|><\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Split \| Description \| \| -------- \| ---------- \| --------- \| ----- \| ----------- \| \| [c4ai-command-r-plus-08-2024-Q8_0.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q8_0) \| Q8_0 \| 110.31GB \| true \| Extremely high quality, generally unneeded but max available quant. \| \| [c4ai-command-r-plus-08-2024-Q6_K.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q6_K) \| Q6_K \| 85.17GB \| true \| Very high quality, near perfect, recommended. \| \| [c4ai-command-r-plus-08-2024-Q5_K_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q5_K_M) \| Q5_K_M \| 73.62GB \| true \| High quality, recommended. \| \| [c4ai-command-r-plus-08-2024-Q4_K_L.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_K_L) \| Q4_K_L \| 63.51GB \| true \| Uses Q8_0 for embed and output weights. Good quality, recommended. \| \| [c4ai-command-r-plus-08-2024-Q4_K_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_K_M) \| Q4_K_M \| 62.75GB \| true \| Good quality, default size for must use cases, recommended. \| \| [c4ai-command-r-plus-08-2024-Q4_K_S.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_K_S) \| Q4_K_S \| 59.64GB \| true \| Slightly lower quality with more space savings, recommended. \| \| [c4ai-command-r-plus-08-2024-Q4_0.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_0) \| Q4_0 \| 59.43GB \| true \| Legacy format, generally not worth using over similarly sized formats \| \| [c4ai-command-r-plus-08-2024-Q4_0_4_8.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_0_4_8) \| Q4_0_4_8 \| 59.22GB \| true \| Optimized for ARM and CPU inference, much faster than Q4_0 at similar quality. \| \| [c4ai-command-r-plus-08-2024-Q4_0_4_4.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_0_4_4) \| Q4_0_4_4 \| 59.22GB \| true \| Optimized for ARM and CPU inference, much faster than Q4_0 at similar quality. \| \| [c4ai-command-r-plus-08-2024-IQ4_XS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-IQ4_XS) \| IQ4_XS \| 56.20GB \| true \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [c4ai-command-r-plus-08-2024-Q3_K_XL.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q3_K_XL) \| Q3_K_XL \| 56.16GB \| true \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \| \| [c4ai-command-r-plus-08-2024-Q3_K_L.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q3_K_L) \| Q3_K_L \| 55.40GB \| true \| Lower quality but usable, good for low RAM availability. \| \| [c4ai-command-r-plus-08-2024-Q3_K_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q3_K_M) \| Q3_K_M \| 50.98GB \| true \| Low quality. \| \| [c4ai-command-r-plus-08-2024-IQ3_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ3_M.gguf) \| IQ3_M \| 47.68GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [c4ai-command-r-plus-08-2024-Q3_K_S.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-Q3_K_S.gguf) \| Q3_K_S \| 45.85GB \| false \| Low quality, not recommended. \| \| [c4ai-command-r-plus-08-2024-IQ3_XXS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ3_XXS.gguf) \| IQ3_XXS \| 40.66GB \| false \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [c4ai-command-r-plus-08-2024-Q2_K_L.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-Q2_K_L.gguf) \| Q2_K_L \| 40.26GB \| false \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \| \| [c4ai-command-r-plus-08-2024-Q2_K.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-Q2_K.gguf) \| Q2_K \| 39.50GB \| false \| Very low quality but surprisingly usable. \| \| [c4ai-command-r-plus-08-2024-IQ2_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ2_M.gguf) \| IQ2_M \| 36.04GB \| false \| Relatively low quality, uses SOTA techniques to be surprisingly usable. \| \| [c4ai-command-r-plus-08-2024-IQ2_XS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ2_XS.gguf) \| IQ2_XS \| 31.63GB \| false \| Low quality, uses SOTA techniques to be usable. \| \| [c4ai-command-r-plus-08-2024-IQ2_XXS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ2_XXS.gguf) \| IQ2_XXS \| 28.61GB \| false \| Very low quality, uses SOTA techniques to be usable. \| \| [c4ai-command-r-plus-08-2024-IQ1_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ1_M.gguf) \| IQ1_M \| 25.22GB \| false \| Extremely low quality, not recommended. \| ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. Thanks! ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset Thank you ZeroWw for the inspiration to experiment with embed/output ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/c4ai-command-r-plus-08-2024-GGUF --include "c4ai-command-r-plus-08-2024-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/c4ai-command-r-plus-08-2024-GGUF --include "c4ai-command-r-plus-08-2024-Q8_0/" --local-dir ./ ``` You can either specify a new local-dir (c4ai-command-r-plus-08-2024-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
stablediffusionapi/kawaii-realistic-anime-mi	stablediffusionapi	2024-08-30T20:33:23Z	30	0	diffusers	[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-08-30T20:29:59Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Kawaii Realistic Anime Mix API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/13606319581725049585.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "kawaii-realistic-anime-mi" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/kawaii-realistic-anime-mi) Model link: [View model](https://modelslab.com/models/kawaii-realistic-anime-mi) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "kawaii-realistic-anime-mi", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
taku-yoshioka/rlhf-line-marcja-0828	taku-yoshioka	2024-08-30T20:19:51Z	48	0	transformers	[ "transformers", "pytorch", "safetensors", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-08-29T00:52:48Z	--- license: apache-2.0 tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="taku-yoshioka//tmp/tmpxviy4d9k/taku-yoshioka/rlhf-line-marcja-0828") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("taku-yoshioka//tmp/tmpxviy4d9k/taku-yoshioka/rlhf-line-marcja-0828") model = AutoModelForCausalLMWithValueHead.from_pretrained("taku-yoshioka//tmp/tmpxviy4d9k/taku-yoshioka/rlhf-line-marcja-0828") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
Xilixmeaty40/sad	Xilixmeaty40	2024-08-30T20:16:41Z	76	0	transformers	[ "transformers", "safetensors", "bert", "code", "chemistry", "biology", "music", "es", "dataset:fka/awesome-chatgpt-prompts", "dataset:LLLM-Lab/huanhuan_sadtalker", "arxiv:1910.09700", "base_model:vinthony/SadTalker", "base_model:finetune:vinthony/SadTalker", "license:gemma", "endpoints_compatible", "region:us" ]	null	2024-06-19T20:35:23Z	--- license: gemma datasets: - fka/awesome-chatgpt-prompts - LLLM-Lab/huanhuan_sadtalker language: - es metrics: - accuracy base_model: vinthony/SadTalker library_name: transformers tags: - code - chemistry - biology - music extra_gated_heading: Access sad on Hugging Face extra_gated_button_content: Acknowledge license extra_gated_prompt: >- To access sad on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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magnifi/Phi3_intent_v32_3_epoch_7_lr_0.002_r_16_a_16	magnifi	2024-08-30T20:02:16Z	75	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T19:59:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lgk03/WITHINAPPS_NDD-dimeshift_test-tags-CWAdj	lgk03	2024-08-30T20:01:51Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-08-30T19:39:40Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-dimeshift_test-tags-CWAdj results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # WITHINAPPS_NDD-dimeshift_test-tags-CWAdj This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3579 - Accuracy: 0.8865 - F1: 0.9038 - Precision: 0.9379 - Recall: 0.8865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 0.9897 \| 72 \| 0.4258 \| 0.8624 \| 0.8858 \| 0.9303 \| 0.8624 \| \| No log \| 1.9931 \| 145 \| 0.3954 \| 0.8723 \| 0.8929 \| 0.9321 \| 0.8723 \| \| No log \| 2.9966 \| 218 \| 0.3774 \| 0.5370 \| 0.6318 \| 0.9294 \| 0.5370 \| \| No log \| 4.0 \| 291 \| 0.3647 \| 0.8865 \| 0.9038 \| 0.9379 \| 0.8865 \| \| No log \| 4.9485 \| 360 \| 0.3579 \| 0.8865 \| 0.9038 \| 0.9379 \| 0.8865 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2	unclemusclez	2024-08-30T20:00:21Z	111	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gguf", "llama", "sentence-similarity", "feature-extraction", "autotrain", "dataset:skratos115/opendevin_DataDevinator", "base_model:HuggingFaceTB/SmolLM-135M-Instruct", "base_model:quantized:HuggingFaceTB/SmolLM-135M-Instruct", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	sentence-similarity	2024-08-30T15:44:10Z	--- library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - autotrain base_model: HuggingFaceTB/SmolLM-135M-Instruct widget: - source_sentence: 'search_query: i love autotrain' sentences: - 'search_query: huggingface auto train' - 'search_query: hugging face auto train' - 'search_query: i love autotrain' pipeline_tag: sentence-similarity datasets: - skratos115/opendevin_DataDevinator --- AVAILABLE ON OLLAMA: https://ollama.com/unclemusclez/smollm-135m-instruct-devinator # Model Trained Using AutoTrain - Problem type: Sentence Transformers ## Validation Metrics No validation metrics available ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the Hugging Face Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'search_query: autotrain', 'search_query: auto train', 'search_query: i love autotrain', ] embeddings = model.encode(sentences) print(embeddings.shape) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) ```
unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.1	unclemusclez	2024-08-30T20:00:02Z	112	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gguf", "llama", "sentence-similarity", "feature-extraction", "autotrain", "dataset:skratos115/opendevin_DataDevinator", "base_model:HuggingFaceTB/SmolLM-135M-Instruct", "base_model:quantized:HuggingFaceTB/SmolLM-135M-Instruct", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	sentence-similarity	2024-08-30T19:10:03Z	--- library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - autotrain base_model: HuggingFaceTB/SmolLM-135M-Instruct widget: - source_sentence: 'search_query: i love autotrain' sentences: - 'search_query: huggingface auto train' - 'search_query: hugging face auto train' - 'search_query: i love autotrain' pipeline_tag: sentence-similarity datasets: - skratos115/opendevin_DataDevinator --- AVAILABLE ON OLLAMA: https://ollama.com/unclemusclez/smollm-135m-instruct-devinator # Model Trained Using AutoTrain - Problem type: Sentence Transformers ## Validation Metrics No validation metrics available ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the Hugging Face Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'search_query: autotrain', 'search_query: auto train', 'search_query: i love autotrain', ] embeddings = model.encode(sentences) print(embeddings.shape) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) ```
jeiku/NeuroNoise_v1	jeiku	2024-08-30T19:44:38Z	8	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml", "base_model:finetune:IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T19:33:10Z	--- library_name: transformers license: other base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml tags: - generated_from_trainer model-index: - name: outputs/out results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/Gryphe-3.5-16k-Subset type: sharegpt conversation: chatml - path: ResplendentAI/bluemoon type: sharegpt conversation: chatml - path: openerotica/freedom-rp type: sharegpt conversation: chatml - path: MinervaAI/Aesir-Preview type: sharegpt conversation: chatml - path: anthracite-org/stheno-filtered-v1.1 type: sharegpt conversation: chatml - path: NewEden/Kalo-Opus-Instruct-22k-Refusal-Murdered type: sharegpt conversation: chatml - path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml - path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml - path: jeiku/jeikutxt type: completion - path: ResplendentAI/Sissification_Hypno_1k type: alpaca - path: ResplendentAI/theory_of_mind_fixed_output type: alpaca - path: ResplendentAI/Synthetic_Soul_1k type: alpaca chat_template: chatml val_set_size: 0.01 output_dir: ./outputs/out adapter: lora_r: lora_alpha: lora_dropout: lora_target_linear: sequence_len: 8192 # sequence_len: 32768 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true wandb_project: Neuro4B wandb_entity: wandb_watch: wandb_name: Neuro4B wandb_log_model: gradient_accumulation_steps: 32 micro_batch_size: 2 num_epochs: 2 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.00001 weight_decay: 0.05 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.1 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 2 debug: deepspeed: deepspeed_configs/zero3.json fsdp: fsdp_config: special_tokens: pad_token: <\|finetune_right_pad_id\|> ``` </details><br> # outputs/out This model is a fine-tuned version of [IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml](https://huggingface.co/IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4207 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 19 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.0722 \| 0.0094 \| 1 \| 3.0240 \| \| 1.5947 \| 0.2529 \| 27 \| 2.8578 \| \| 1.4579 \| 0.5057 \| 54 \| 2.5541 \| \| 1.4422 \| 0.7586 \| 81 \| 2.4550 \| \| 1.4039 \| 1.0006 \| 108 \| 2.4334 \| \| 1.3428 \| 1.2534 \| 135 \| 2.4217 \| \| 1.3054 \| 1.5063 \| 162 \| 2.4259 \| \| 1.3378 \| 1.7591 \| 189 \| 2.4207 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf	RichardErkhov	2024-08-30T19:40:18Z	5	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T13:59:54Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) FusionNet_SOLAR - GGUF - Model creator: https://huggingface.co/TomGrc/ - Original model: https://huggingface.co/TomGrc/FusionNet_SOLAR/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [FusionNet_SOLAR.Q2_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q2_K.gguf) \| Q2_K \| 5.52GB \| \| [FusionNet_SOLAR.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ3_XS.gguf) \| IQ3_XS \| 6.13GB \| \| [FusionNet_SOLAR.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ3_S.gguf) \| IQ3_S \| 6.48GB \| \| [FusionNet_SOLAR.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K_S.gguf) \| Q3_K_S \| 6.44GB \| \| [FusionNet_SOLAR.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ3_M.gguf) \| IQ3_M \| 6.69GB \| \| [FusionNet_SOLAR.Q3_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K.gguf) \| Q3_K \| 7.18GB \| \| [FusionNet_SOLAR.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K_M.gguf) \| Q3_K_M \| 7.18GB \| \| [FusionNet_SOLAR.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K_L.gguf) \| Q3_K_L \| 7.82GB \| \| [FusionNet_SOLAR.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ4_XS.gguf) \| IQ4_XS \| 8.06GB \| \| [FusionNet_SOLAR.Q4_0.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_0.gguf) \| Q4_0 \| 8.4GB \| \| [FusionNet_SOLAR.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ4_NL.gguf) \| IQ4_NL \| 8.49GB \| \| [FusionNet_SOLAR.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_K_S.gguf) \| Q4_K_S \| 3.76GB \| \| [FusionNet_SOLAR.Q4_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_K.gguf) \| Q4_K \| 8.94GB \| \| [FusionNet_SOLAR.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_K_M.gguf) \| Q4_K_M \| 8.94GB \| \| [FusionNet_SOLAR.Q4_1.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_1.gguf) \| Q4_1 \| 9.32GB \| \| [FusionNet_SOLAR.Q5_0.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_0.gguf) \| Q5_0 \| 10.24GB \| \| [FusionNet_SOLAR.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_K_S.gguf) \| Q5_K_S \| 10.24GB \| \| [FusionNet_SOLAR.Q5_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_K.gguf) \| Q5_K \| 10.52GB \| \| [FusionNet_SOLAR.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_K_M.gguf) \| Q5_K_M \| 10.52GB \| \| [FusionNet_SOLAR.Q5_1.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_1.gguf) \| Q5_1 \| 11.16GB \| \| [FusionNet_SOLAR.Q6_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q6_K.gguf) \| Q6_K \| 12.2GB \| \| [FusionNet_SOLAR.Q8_0.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q8_0.gguf) \| Q8_0 \| 15.8GB \| Original model description: --- language: - en license: mit pipeline_tag: text-generation model-index: - name: FusionNet_SOLAR results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.4 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.29 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 69.21 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 50.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard --- # FusionNet_SOLAR Fine-tuned model on English language using SOLAR Fusion method. ## Model description This is an experiment with the SOLAR Fusion method of FusionNet. This model has 16B parameters, and this model is fine-tuned. Enjoy! # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_SOLAR) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|71.08\| \|AI2 Reasoning Challenge (25-Shot)\|71.59\| \|HellaSwag (10-Shot) \|88.40\| \|MMLU (5-Shot) \|65.29\| \|TruthfulQA (0-shot) \|69.21\| \|Winogrande (5-shot) \|81.06\| \|GSM8k (5-shot) \|50.95\|
mradermacher/Coder-2B-GGUF	mradermacher	2024-08-30T19:39:11Z	27	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "en", "base_model:suriya7/Coder-2B", "base_model:quantized:suriya7/Coder-2B", "endpoints_compatible", "region:us" ]	null	2024-08-30T19:30:28Z	--- base_model: suriya7/Coder-2B language: - en library_name: transformers quantized_by: mradermacher tags: - unsloth - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/suriya7/Coder-2B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q2_K.gguf) \| Q2_K \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ3_XS.gguf) \| IQ3_XS \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ3_S.gguf) \| IQ3_S \| 1.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q3_K_S.gguf) \| Q3_K_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ3_M.gguf) \| IQ3_M \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q3_K_M.gguf) \| Q3_K_M \| 1.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q3_K_L.gguf) \| Q3_K_L \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ4_XS.gguf) \| IQ4_XS \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q4_K_S.gguf) \| Q4_K_S \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q4_K_M.gguf) \| Q4_K_M \| 1.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q5_K_S.gguf) \| Q5_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q5_K_M.gguf) \| Q5_K_M \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q6_K.gguf) \| Q6_K \| 2.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q8_0.gguf) \| Q8_0 \| 2.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.f16.gguf) \| f16 \| 5.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mertgulexe/distributed-model	mertgulexe	2024-08-30T19:37:47Z	104	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-08-30T19:30:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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lmstudio-community/c4ai-command-r-plus-08-2024-GGUF	lmstudio-community	2024-08-30T19:36:22Z	485	5	transformers	[ "transformers", "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "base_model:CohereForAI/c4ai-command-r-plus-08-2024", "base_model:quantized:CohereForAI/c4ai-command-r-plus-08-2024", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-08-30T16:02:51Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox quantized_by: bartowski pipeline_tag: text-generation base_model: CohereForAI/c4ai-command-r-plus-08-2024 lm_studio: param_count: 105b use_case: general release_date: 30-08-2024 model_creator: CohereForAI prompt_template: cohere_command_r base_model: Cohere system_prompt: You are a large language model called Command R built by the company Cohere. You act as a brilliant, sophisticated, AI-assistant chatbot trained to assist human users by providing thorough responses. original_repo: CohereForAI/c4ai-command-r-plus-08-2024 --- ## 💫 Community Model> C4AI Command R Plus 08-2024 by Cohere For AI 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [CohereForAI](https://huggingface.co/CohereForAI)<br> Original model: [c4ai-command-r-plus-08-2024](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3634](https://github.com/ggerganov/llama.cpp/releases/tag/b3634)<br> ## Model Summary: C4AI Command R Plus 08-2024 is an update to the originally released 105B paramater Command R. The original Command R model received sweeping praise for its incredible RAG and multilingual abilities, and this model is no different.<br> Not for commercial use, must adhere to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ## Prompt Template: Choose the `Cohere Command R` preset in your LM Studio. Under the hood, the model will see a prompt that's formatted like so: ``` <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>{prompt}<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> ``` This model also supports tool use and RAG prompt formats. For details on formatting for those use cases, view [tool use here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024#tool-use--agent-capabilities) and [RAG capabilities here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024#grounded-generation-and-rag-capabilities) ## Technical Details C4AI Command R Plus 08-2024 has been trained on 23 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian). Due to this multilingual training, it excels in multilingual tasks. Command R Plus 08-2024 supports a context length of 128K. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio. ### Terms of Use (directly from Cohere For AI) We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).
Tawkat/qlora-med42llm3-nclex-august-v7	Tawkat	2024-08-30T19:36:21Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T16:32:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TheNovak/simpletuner-lora-Elbows	TheNovak	2024-08-30T19:20:49Z	14	0	diffusers	[ "diffusers", "flux", "flux-diffusers", "text-to-image", "simpletuner", "lora", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-08-30T07:33:43Z	--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - lora - template:sd-lora inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'Elbows the cat on a box' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # simpletuner-lora-Elbows This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` Elbows the cat on a box ``` ## Validation settings - CFG: `3.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder was not trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 5 - Training steps: 8200 - Learning rate: 8e-05 - Effective batch size: 1 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: bf16 - Quantised: No - Xformers: Not used - LoRA Rank: 64 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### Elbows - Repeats: 0 - Total number of images: 1392 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'jusscubs/simpletuner-lora-Elbows' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "Elbows the cat on a box" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.0, ).images[0] image.save("output.png", format="PNG") ```
showvikdbz/phi-3.5-it-25k	showvikdbz	2024-08-30T19:18:06Z	75	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T19:11:53Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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geshijoker/distilbert_ner_wnut_model	geshijoker	2024-08-30T19:14:00Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-08-30T19:12:11Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert_ner_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5339578454332553 - name: Recall type: recall value: 0.211306765523633 - name: F1 type: f1 value: 0.30278884462151395 - name: Accuracy type: accuracy value: 0.9365995468342525 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_ner_wnut_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2970 - Precision: 0.5340 - Recall: 0.2113 - F1: 0.3028 - Accuracy: 0.9366 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 107 \| 0.3140 \| 0.3744 \| 0.0732 \| 0.1225 \| 0.9306 \| \| No log \| 2.0 \| 214 \| 0.2970 \| 0.5340 \| 0.2113 \| 0.3028 \| 0.9366 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.2.1+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
csikasote/mms-zeroshot-bem-sv-male	csikasote	2024-08-30T19:07:14Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "BembaSpeech", "mms", "generated_from_trainer", "base_model:mms-meta/mms-zeroshot-300m", "base_model:finetune:mms-meta/mms-zeroshot-300m", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-08-30T13:55:57Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: mms-meta/mms-zeroshot-300m tags: - automatic-speech-recognition - BembaSpeech - mms - generated_from_trainer metrics: - wer model-index: - name: mms-zeroshot-bem-sv-male results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mms-zeroshot-bem-sv-male This model is a fine-tuned version of [mms-meta/mms-zeroshot-300m](https://huggingface.co/mms-meta/mms-zeroshot-300m) on the BEMBASPEECH - BEM dataset. It achieves the following results on the evaluation set: - Loss: 0.1874 - Wer: 0.3949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| No log \| 0.2183 \| 200 \| 2.3822 \| 1.0 \| \| No log \| 0.4367 \| 400 \| 0.2715 \| 0.5093 \| \| 2.7769 \| 0.6550 \| 600 \| 0.2489 \| 0.4820 \| \| 2.7769 \| 0.8734 \| 800 \| 0.2296 \| 0.4695 \| \| 0.6809 \| 1.0917 \| 1000 \| 0.2209 \| 0.4638 \| \| 0.6809 \| 1.3100 \| 1200 \| 0.2163 \| 0.4469 \| \| 0.6809 \| 1.5284 \| 1400 \| 0.2092 \| 0.4400 \| \| 0.6113 \| 1.7467 \| 1600 \| 0.2047 \| 0.4346 \| \| 0.6113 \| 1.9651 \| 1800 \| 0.2074 \| 0.4467 \| \| 0.5974 \| 2.1834 \| 2000 \| 0.2041 \| 0.4304 \| \| 0.5974 \| 2.4017 \| 2200 \| 0.2054 \| 0.4317 \| \| 0.5974 \| 2.6201 \| 2400 \| 0.1987 \| 0.4240 \| \| 0.5636 \| 2.8384 \| 2600 \| 0.2003 \| 0.4252 \| \| 0.5636 \| 3.0568 \| 2800 \| 0.1997 \| 0.4287 \| \| 0.5398 \| 3.2751 \| 3000 \| 0.2097 \| 0.4400 \| \| 0.5398 \| 3.4934 \| 3200 \| 0.1968 \| 0.4165 \| \| 0.5398 \| 3.7118 \| 3400 \| 0.2013 \| 0.4218 \| \| 0.5334 \| 3.9301 \| 3600 \| 0.2003 \| 0.4230 \| \| 0.5334 \| 4.1485 \| 3800 \| 0.1976 \| 0.4227 \| \| 0.5123 \| 4.3668 \| 4000 \| 0.1978 \| 0.4198 \| \| 0.5123 \| 4.5852 \| 4200 \| 0.2019 \| 0.4298 \| \| 0.5123 \| 4.8035 \| 4400 \| 0.1939 \| 0.4146 \| \| 0.5119 \| 5.0218 \| 4600 \| 0.1989 \| 0.4161 \| \| 0.5119 \| 5.2402 \| 4800 \| 0.1902 \| 0.4076 \| \| 0.4929 \| 5.4585 \| 5000 \| 0.1929 \| 0.4116 \| \| 0.4929 \| 5.6769 \| 5200 \| 0.1943 \| 0.4144 \| \| 0.4929 \| 5.8952 \| 5400 \| 0.1922 \| 0.4106 \| \| 0.4878 \| 6.1135 \| 5600 \| 0.1933 \| 0.4137 \| \| 0.4878 \| 6.3319 \| 5800 \| 0.1920 \| 0.4058 \| \| 0.4755 \| 6.5502 \| 6000 \| 0.1927 \| 0.4171 \| \| 0.4755 \| 6.7686 \| 6200 \| 0.1920 \| 0.4127 \| \| 0.4755 \| 6.9869 \| 6400 \| 0.1925 \| 0.4061 \| \| 0.475 \| 7.2052 \| 6600 \| 0.1884 \| 0.4058 \| \| 0.475 \| 7.4236 \| 6800 \| 0.1903 \| 0.4070 \| \| 0.4715 \| 7.6419 \| 7000 \| 0.1882 \| 0.3996 \| \| 0.4715 \| 7.8603 \| 7200 \| 0.1881 \| 0.4033 \| \| 0.4715 \| 8.0786 \| 7400 \| 0.1885 \| 0.4007 \| \| 0.4575 \| 8.2969 \| 7600 \| 0.1885 \| 0.4016 \| \| 0.4575 \| 8.5153 \| 7800 \| 0.1888 \| 0.4050 \| \| 0.4611 \| 8.7336 \| 8000 \| 0.1884 \| 0.4046 \| \| 0.4611 \| 8.9520 \| 8200 \| 0.1881 \| 0.3974 \| \| 0.4611 \| 9.1703 \| 8400 \| 0.1865 \| 0.3956 \| \| 0.4559 \| 9.3886 \| 8600 \| 0.1875 \| 0.3974 \| \| 0.4559 \| 9.6070 \| 8800 \| 0.1872 \| 0.3996 \| \| 0.4536 \| 9.8253 \| 9000 \| 0.1876 \| 0.3953 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
GlobalMeltdown/MaidenlessNoMore-7B-GGUF	GlobalMeltdown	2024-08-30T18:55:11Z	12	3	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "Roleplay", "RP", "Chat", "text-generation-inference", "merge ", "text generation", "en", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-08-29T21:05:23Z	--- base_model: MaidenlessNoMore-7B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo - Roleplay - RP - Chat - text-generation-inference - 'merge ' - text generation license: cc-by-4.0 language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65bbcee1320702b1043ef8ae/9OPS0wrdkzksmyuM6Nxdu.png) MaidenlessNoMore-7B-GGUF was my first attempt at merging an LLM I decided to use one of the first models I really enjoyed that not many people know of: https://huggingface.co/cookinai/Valkyrie-V1 with my other favorite model that has been my fallback model for a long time: https://huggingface.co/SanjiWatsuki/Kunoichi-7B This was more of an experiment than anything else. Hopefully this will lead to some more interesting merges and who knows what else in the future. I mean we have to start somewhere right? Alpaca or Alpaca roleplay is recommended. # GlobalMeltdown/MaidenlessNoMore-7B-GGUF This model was converted to GGUF format from [`GlobalMeltdown/MaidenlessNoMore-7B`](https://huggingface.co/GlobalMeltdown/MaidenlessNoMore-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/GlobalMeltdown/MaidenlessNoMore-7B) for more details on the model.
csikasote/mms-zeroshot-bem-sv-female	csikasote	2024-08-30T18:42:13Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "BembaSpeech", "mms", "generated_from_trainer", "base_model:mms-meta/mms-zeroshot-300m", "base_model:finetune:mms-meta/mms-zeroshot-300m", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-08-30T14:12:22Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: mms-meta/mms-zeroshot-300m tags: - automatic-speech-recognition - BembaSpeech - mms - generated_from_trainer metrics: - wer model-index: - name: mms-zeroshot-bem-sv-female results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mms-zeroshot-bem-sv-female This model is a fine-tuned version of [mms-meta/mms-zeroshot-300m](https://huggingface.co/mms-meta/mms-zeroshot-300m) on the BEMBASPEECH - BEM dataset. It achieves the following results on the evaluation set: - Loss: 0.2722 - Wer: 0.4375 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| No log \| 0.3992 \| 200 \| 1.9434 \| 1.0 \| \| No log \| 0.7984 \| 400 \| 0.3648 \| 0.5411 \| \| 2.5692 \| 1.1976 \| 600 \| 0.3371 \| 0.5174 \| \| 2.5692 \| 1.5968 \| 800 \| 0.3229 \| 0.5213 \| \| 0.3941 \| 1.9960 \| 1000 \| 0.3183 \| 0.4915 \| \| 0.3941 \| 2.3952 \| 1200 \| 0.3068 \| 0.5073 \| \| 0.3941 \| 2.7944 \| 1400 \| 0.3057 \| 0.4688 \| \| 0.3502 \| 3.1936 \| 1600 \| 0.3017 \| 0.4777 \| \| 0.3502 \| 3.5928 \| 1800 \| 0.2905 \| 0.4647 \| \| 0.3253 \| 3.9920 \| 2000 \| 0.2857 \| 0.4686 \| \| 0.3253 \| 4.3912 \| 2200 \| 0.2892 \| 0.4601 \| \| 0.3253 \| 4.7904 \| 2400 \| 0.2848 \| 0.4759 \| \| 0.3066 \| 5.1896 \| 2600 \| 0.2801 \| 0.4444 \| \| 0.3066 \| 5.5888 \| 2800 \| 0.2752 \| 0.4627 \| \| 0.2988 \| 5.9880 \| 3000 \| 0.2818 \| 0.4614 \| \| 0.2988 \| 6.3872 \| 3200 \| 0.2759 \| 0.4444 \| \| 0.2988 \| 6.7864 \| 3400 \| 0.2751 \| 0.4382 \| \| 0.2877 \| 7.1856 \| 3600 \| 0.2726 \| 0.4472 \| \| 0.2877 \| 7.5848 \| 3800 \| 0.2722 \| 0.4484 \| \| 0.2812 \| 7.9840 \| 4000 \| 0.2710 \| 0.4344 \| \| 0.2812 \| 8.3832 \| 4200 \| 0.2734 \| 0.4410 \| \| 0.2812 \| 8.7824 \| 4400 \| 0.2734 \| 0.4360 \| \| 0.2742 \| 9.1816 \| 4600 \| 0.2759 \| 0.4398 \| \| 0.2742 \| 9.5808 \| 4800 \| 0.2740 \| 0.4337 \| \| 0.2731 \| 9.9800 \| 5000 \| 0.2722 \| 0.4382 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
lgk03/WITHINAPPS_NDD-mrbs_test-tags-CWAdj	lgk03	2024-08-30T18:21:47Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-08-30T18:00:13Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-mrbs_test-tags-CWAdj results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # WITHINAPPS_NDD-mrbs_test-tags-CWAdj This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0932 - Accuracy: 0.8764 - F1: 0.8787 - Precision: 0.9031 - Recall: 0.8764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 71 \| 0.0974 \| 0.8764 \| 0.8787 \| 0.9031 \| 0.8764 \| \| No log \| 2.0 \| 142 \| 0.0972 \| 0.8764 \| 0.8787 \| 0.9031 \| 0.8764 \| \| No log \| 3.0 \| 213 \| 0.0957 \| 0.8764 \| 0.8787 \| 0.9031 \| 0.8764 \| \| No log \| 4.0 \| 284 \| 0.0947 \| 0.8764 \| 0.8787 \| 0.9031 \| 0.8764 \| \| No log \| 5.0 \| 355 \| 0.0932 \| 0.8764 \| 0.8787 \| 0.9031 \| 0.8764 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
arshiakarimian1/spam-student-4-256	arshiakarimian1	2024-08-30T18:16:01Z	173	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-29T23:15:08Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - generated_from_trainer model-index: - name: spam-student-4-256 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # spam-student-4-256 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
netcat420/MFANNv0.20.12	netcat420	2024-08-30T18:14:25Z	17	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated", "base_model:merge:mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated", "base_model:netcat420/MFANNv0.19", "base_model:merge:netcat420/MFANNv0.19", "base_model:netcat420/MFANNv0.20", "base_model:merge:netcat420/MFANNv0.20", "base_model:netcat420/MFANNv0.20.11", "base_model:merge:netcat420/MFANNv0.20.11", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T18:09:21Z	--- base_model: - netcat420/MFANNv0.19 - netcat420/MFANNv0.20.11 - netcat420/MFANNv0.20 - mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) as a base. ### Models Merged The following models were included in the merge: * [netcat420/MFANNv0.19](https://huggingface.co/netcat420/MFANNv0.19) * [netcat420/MFANNv0.20.11](https://huggingface.co/netcat420/MFANNv0.20.11) * [netcat420/MFANNv0.20](https://huggingface.co/netcat420/MFANNv0.20) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: netcat420/MFANNv0.20.11 parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: netcat420/MFANNv0.20 parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: netcat420/MFANNv0.19 parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 merge_method: ties base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated parameters: normalize: true int8_mask: true dtype: float16 ```
LearnerSX/847_capstone_tweets_bert_v2	LearnerSX	2024-08-30T17:35:49Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-08-19T01:20:05Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: 847_capstone_tweets_bert_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 847_capstone_tweets_bert_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6962 - Accuracy: 0.8508 - F1: 0.8487 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
gglabs/Mistral-Nemo-12B-FC-Chat-0830-1-epoch	gglabs	2024-08-30T17:27:37Z	6	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T16:57:25Z	--- base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - Developed by: gglabs - License: apache-2.0 - Finetuned from model : unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jacobposel/cc-rl-pattern-2	jacobposel	2024-08-30T17:20:15Z	7	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-08-30T16:12:53Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image instance_prompt: <lora:RL> --- # Cc Rl Pattern 2 Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `<lora:RL>` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jacobposel/cc-rl-pattern-2', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
Huseyin/bert-base-uncased-finetuned-ner	Huseyin	2024-08-30T17:15:38Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-07T11:39:18Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3910 - Precision: 0.9616 - Recall: 0.9637 - F1: 0.9627 - Accuracy: 0.9560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.3052 \| 1.0 \| 3334 \| 0.2630 \| 0.9365 \| 0.9367 \| 0.9366 \| 0.9228 \| \| 0.2104 \| 2.0 \| 6668 \| 0.2481 \| 0.9418 \| 0.9537 \| 0.9477 \| 0.9400 \| \| 0.163 \| 3.0 \| 10002 \| 0.2390 \| 0.9495 \| 0.9606 \| 0.9550 \| 0.9479 \| \| 0.1151 \| 4.0 \| 13336 \| 0.2516 \| 0.9549 \| 0.9616 \| 0.9583 \| 0.9515 \| \| 0.0809 \| 5.0 \| 16670 \| 0.2887 \| 0.9590 \| 0.9556 \| 0.9573 \| 0.9493 \| \| 0.0625 \| 6.0 \| 20004 \| 0.2912 \| 0.9573 \| 0.9611 \| 0.9592 \| 0.9520 \| \| 0.0516 \| 7.0 \| 23338 \| 0.3139 \| 0.9581 \| 0.9563 \| 0.9572 \| 0.9501 \| \| 0.0388 \| 8.0 \| 26672 \| 0.3070 \| 0.9605 \| 0.9600 \| 0.9602 \| 0.9531 \| \| 0.0273 \| 9.0 \| 30006 \| 0.3344 \| 0.9607 \| 0.9617 \| 0.9612 \| 0.9535 \| \| 0.0252 \| 10.0 \| 33340 \| 0.3547 \| 0.9608 \| 0.9638 \| 0.9623 \| 0.9554 \| \| 0.0242 \| 11.0 \| 36674 \| 0.3726 \| 0.9600 \| 0.9619 \| 0.9610 \| 0.9541 \| \| 0.0119 \| 12.0 \| 40008 \| 0.3727 \| 0.9602 \| 0.9623 \| 0.9612 \| 0.9546 \| \| 0.0078 \| 13.0 \| 43342 \| 0.3772 \| 0.9617 \| 0.9639 \| 0.9628 \| 0.9562 \| \| 0.0078 \| 14.0 \| 46676 \| 0.3904 \| 0.9615 \| 0.9638 \| 0.9627 \| 0.9560 \| \| 0.0026 \| 15.0 \| 50010 \| 0.3910 \| 0.9616 \| 0.9637 \| 0.9627 \| 0.9560 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
geshijoker/distilbert_sentiment	geshijoker	2024-08-30T17:08:28Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-08-30T16:49:06Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert_sentiment results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_sentiment This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Accuracy: 0.9299 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2792 \| 1.0 \| 782 \| 0.1936 \| 0.9251 \| \| 0.1419 \| 2.0 \| 1564 \| 0.1957 \| 0.9299 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.2.1+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF	mradermacher	2024-08-30T17:02:15Z	63	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos", "base_model:quantized:EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-08-30T11:24:20Z	--- base_model: EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.8 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.8 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.8 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
netcat420/MFANNv0.20.11	netcat420	2024-08-30T16:53:41Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T15:26:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mradermacher/pythia-410m-tulu-v2-mix-GGUF	mradermacher	2024-08-30T16:48:55Z	10	0	transformers	[ "transformers", "gguf", "alignment-handbook", "trl", "sft", "generated_from_trainer", "en", "dataset:allenai/tulu-v2-sft-mixture", "base_model:kykim0/pythia-410m-tulu-v2-mix", "base_model:quantized:kykim0/pythia-410m-tulu-v2-mix", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T16:47:18Z	--- base_model: kykim0/pythia-410m-tulu-v2-mix datasets: - allenai/tulu-v2-sft-mixture language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/kykim0/pythia-410m-tulu-v2-mix <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ3_XS.gguf) \| IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ3_S.gguf) \| IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ3_M.gguf) \| IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q4_K_M.gguf) \| Q4_K_M \| 0.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q8_0.gguf) \| Q8_0 \| 0.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.f16.gguf) \| f16 \| 0.9 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/pythia-1b-tulu-v2-mix-GGUF	mradermacher	2024-08-30T16:48:09Z	14	0	transformers	[ "transformers", "gguf", "alignment-handbook", "trl", "sft", "generated_from_trainer", "en", "dataset:allenai/tulu-v2-sft-mixture", "base_model:kykim0/pythia-1b-tulu-v2-mix", "base_model:quantized:kykim0/pythia-1b-tulu-v2-mix", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T16:44:01Z	--- base_model: kykim0/pythia-1b-tulu-v2-mix datasets: - allenai/tulu-v2-sft-mixture language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/kykim0/pythia-1b-tulu-v2-mix <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q2_K.gguf) \| Q2_K \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ3_XS.gguf) \| IQ3_XS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ3_S.gguf) \| IQ3_S \| 0.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q3_K_S.gguf) \| Q3_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ3_M.gguf) \| IQ3_M \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q3_K_M.gguf) \| Q3_K_M \| 0.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ4_XS.gguf) \| IQ4_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q4_K_S.gguf) \| Q4_K_S \| 0.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q5_K_S.gguf) \| Q5_K_S \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q6_K.gguf) \| Q6_K \| 0.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q8_0.gguf) \| Q8_0 \| 1.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.f16.gguf) \| f16 \| 2.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
LoneStriker/LongWriter-llama3.1-8b-6.0bpw-h6-exl2	LoneStriker	2024-08-30T16:39:33Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Long Context", "chatglm", "en", "zh", "dataset:THUDM/LongWriter-6k", "arxiv:2408.07055", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-08-30T16:36:45Z	--- language: - en - zh library_name: transformers tags: - Long Context - chatglm - llama datasets: - THUDM/LongWriter-6k license: llama3.1 --- # LongWriter-llama3.1-8b <p align="center"> 🤗 <a href="https://huggingface.co/datasets/THUDM/LongWriter-6k" target="_blank">[LongWriter Dataset] </a> • 💻 <a href="https://github.com/THUDM/LongWriter" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2408.07055" target="_blank">[LongWriter Paper]</a> </p> LongWriter-llama3.1-8b is trained based on [Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B), and is capable of generating 10,000+ words at once. Environment: `transformers>=4.43.0` Please ahere to the prompt template (system prompt is optional): `<<SYS>>\n{system prompt}\n<</SYS>>\n\n[INST]{query1}[/INST]{response1}[INST]{query2}[/INST]{response2}...` A simple demo for deployment of the model: ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("THUDM/LongWriter-llama3.1-8b", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("THUDM/LongWriter-llama3.1-8b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") model = model.eval() query = "Write a 10000-word China travel guide" prompt = f"[INST]{query}[/INST]" input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device) context_length = input.input_ids.shape[-1] output = model.generate( **input, max_new_tokens=32768, num_beams=1, do_sample=True, temperature=0.5, )[0] response = tokenizer.decode(output[context_length:], skip_special_tokens=True) print(response) ``` You can also deploy the model with [vllm](https://github.com/vllm-project/vllm), which allows 10,000+ words generation within a minute. Here is an example code: ```python model = LLM( model= "THUDM/LongWriter-llama3.1-8b", dtype="auto", trust_remote_code=True, tensor_parallel_size=1, max_model_len=32768, gpu_memory_utilization=0.5, ) tokenizer = model.get_tokenizer() generation_params = SamplingParams( temperature=0.5, top_p=0.8, top_k=50, max_tokens=32768, repetition_penalty=1, ) query = "Write a 10000-word China travel guide" prompt = f"[INST]{query}[/INST]" input_ids = tokenizer(prompt, truncation=False, return_tensors="pt").input_ids[0].tolist() outputs = model.generate( sampling_params=generation_params, prompt_token_ids=[input_ids], ) output = outputs[0] print(output.outputs[0].text) ``` License: [Llama-3.1 License](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) ## Citation If you find our work useful, please consider citing LongWriter: ``` @article{bai2024longwriter, title={LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs}, author={Yushi Bai and Jiajie Zhang and Xin Lv and Linzhi Zheng and Siqi Zhu and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li}, journal={arXiv preprint arXiv:2408.07055}, year={2024} } ```
Gabrioloruioni/BertRE	Gabrioloruioni	2024-08-30T16:37:34Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T10:13:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
anchovy/autogluon-chronos-t5-large	anchovy	2024-08-30T16:29:35Z	44	0	null	[ "safetensors", "t5", "time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series", "time-series-forecasting", "arxiv:2403.07815", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]	time-series-forecasting	2024-08-30T16:29:35Z	--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Large) Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <p align="center"> <img src="figures/main-figure.png" width="100%"> <br /> <span> Fig. 1: High-level depiction of Chronos. (<b>Left</b>) The input time series is scaled and quantized to obtain a sequence of tokens. (<b>Center</b>) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (<b>Right</b>) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. </span> </p> --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. \| Model \| Parameters \| Based on \| \| ---------------------------------------------------------------------- \| ---------- \| ---------------------------------------------------------------------- \| \| [chronos-t5-tiny](https://huggingface.co/amazon/chronos-t5-tiny) \| 8M \| [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) \| \| [chronos-t5-mini](https://huggingface.co/amazon/chronos-t5-mini) \| 20M \| [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) \| \| [chronos-t5-small](https://huggingface.co/amazon/chronos-t5-small) \| 46M \| [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) \| \| [chronos-t5-base](https://huggingface.co/amazon/chronos-t5-base) \| 200M \| [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) \| \| [chronos-t5-large](https://huggingface.co/amazon/chronos-t5-large) \| 710M \| [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) \| ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-large", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.
anchovy/autogluon-chronos-t5-mini	anchovy	2024-08-30T16:29:21Z	149	0	null	[ "safetensors", "t5", "time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series", "time-series-forecasting", "arxiv:2403.07815", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]	time-series-forecasting	2024-08-30T16:29:20Z	--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Mini) Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <p align="center"> <img src="figures/main-figure.png" width="100%"> <br /> <span> Fig. 1: High-level depiction of Chronos. (<b>Left</b>) The input time series is scaled and quantized to obtain a sequence of tokens. (<b>Center</b>) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (<b>Right</b>) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. </span> </p> --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. \| Model \| Parameters \| Based on \| \| ---------------------------------------------------------------------- \| ---------- \| ---------------------------------------------------------------------- \| \| [chronos-t5-tiny](https://huggingface.co/amazon/chronos-t5-tiny) \| 8M \| [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) \| \| [chronos-t5-mini](https://huggingface.co/amazon/chronos-t5-mini) \| 20M \| [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) \| \| [chronos-t5-small](https://huggingface.co/amazon/chronos-t5-small) \| 46M \| [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) \| \| [chronos-t5-base](https://huggingface.co/amazon/chronos-t5-base) \| 200M \| [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) \| \| [chronos-t5-large](https://huggingface.co/amazon/chronos-t5-large) \| 710M \| [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) \| ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-mini", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.
anchovy/autogluon-chronos-t5-tiny	anchovy	2024-08-30T16:29:03Z	6	0	null	[ "safetensors", "t5", "time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series", "time-series-forecasting", "arxiv:2403.07815", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]	time-series-forecasting	2024-08-30T16:29:03Z	--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Tiny) Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <p align="center"> <img src="figures/main-figure.png" width="100%"> <br /> <span> Fig. 1: High-level depiction of Chronos. (<b>Left</b>) The input time series is scaled and quantized to obtain a sequence of tokens. (<b>Center</b>) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (<b>Right</b>) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. </span> </p> --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. \| Model \| Parameters \| Based on \| \| ---------------------------------------------------------------------- \| ---------- \| ---------------------------------------------------------------------- \| \| [chronos-t5-tiny](https://huggingface.co/amazon/chronos-t5-tiny) \| 8M \| [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) \| \| [chronos-t5-mini](https://huggingface.co/amazon/chronos-t5-mini) \| 20M \| [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) \| \| [chronos-t5-small](https://huggingface.co/amazon/chronos-t5-small) \| 46M \| [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) \| \| [chronos-t5-base](https://huggingface.co/amazon/chronos-t5-base) \| 200M \| [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) \| \| [chronos-t5-large](https://huggingface.co/amazon/chronos-t5-large) \| 710M \| [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) \| ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-tiny", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.
RWKV/v5-EagleX-v2-7B-HF	RWKV	2024-08-30T16:26:20Z	86	13	transformers	[ "transformers", "pytorch", "safetensors", "rwkv5", "text-generation", "custom_code", "en", "dataset:cerebras/SlimPajama-627B", "dataset:EleutherAI/pile", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-17T21:41:27Z	--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - EleutherAI/pile language: - en --- ![An eagle flying high up in the sky](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F304f2c7a-fc67-4df4-ba57-c6f38f86826c_2688x1536.png) ### Huggingface RWKV EagleX 7B v2 Model > ! Important Note ! > > The following is the HF transformers implementation of the EagleX 7B 2.25T model. This is meant to be used with the huggingface transformers > > [For the full model weights on its own, to use with other RWKV libraries, refer to `RWKV/v5-EagleX-v2-7B-pth`](https://huggingface.co/RWKV/v5-EagleX-v2-7B-pth) > > > This is not an instruct tune model! (soon...) ## Quickstart with the hugging face transformer library ``` model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) ``` ## Evaluation The following shows the progression of the model from 1.1T trained to 2.25T trained. \|Model \|Eagle-7B-HF\|EagleX-7B-HF-v1\|EagleX-7B-HF-v2\| \|----------------------\|-----------\|---------------\|---------------\| \|Param Count \|7.52 B \|7.52 B \|7.52 B \| \|Tokens Trained \|1.1 T \|1.7 T \|2.25 T \| \|avg_acc \|0.4822 \|0.5391 \|0.5495 \| \|glue (acc) \|0.5752 \|0.7463 \|0.7439 \| \|anli (acc) \|0.3594 \|0.4847 \|0.5097 \| \|mnli (acc) \|0.3802 \|0.7928 \|0.7884 \| \|mnli_mismatch (acc) \|0.3687 \|0.7985 \|0.784 \| \|swag (acc) \|0.568 \|0.5814 \|0.5905 \| \|lambada_standard (acc)\|0.685 \|0.686 \|0.7004 \| \|lambada_openai (acc) \|0.7425 \|0.7522 \|0.7502 \| \|mmlu (acc) \|0.3321 \|0.4014 \|0.438 \| \|winogrande (acc) \|0.674 \|0.7206 \|0.7332 \| \|wnli (acc) \|0.4225 \|0.4648 \|0.493 \| \|truthfulqa (acc) \|0.3303 \|0.3268 \|0.3401 \| \|logiqa (acc) \|0.2458 \|0.2458 \|0.2458 \| \|logiqa2 (acc) \|0.2494 \|0.2595 \|0.2621 \| \|sciq (acc) \|0.955 \|0.96 \|0.93 \| \|piqa (acc) \|0.7704 \|0.7758 \|0.7764 \| \|arc_easy (acc) \|0.7382 \|0.7555 \|0.7445 \| \|arc_challenge (acc) \|0.3951 \|0.4087 \|0.4155 \| \|hellaswag (acc) \|0.5264 \|0.5411 \|0.56 \| \|openbookqa (acc) \|0.302 \|0.296 \|0.304 \| \|mathqa (acc) \|0.26 \|0.26 \|0.2593 \| \|arithmetic (acc) \|0.245 \|0.0634 \|0.1703 \| Compared against other top performing models in the same weight class. \|Model \|OLMo-7B \|falcon-7b \|Llama-2-7b-hf\|EagleX-7B-HF-v2\|Mistral-7B-v0.1\| \|----------------------\|---------------\|----------------\|-------------\|---------------\|---------------\| \|Param Count \|6.89 B \|6.92 B \|6.74 B \|7.52 B \|7.24 B \| \|Tokens Trained \|2.5 T \|1.5 T \|2 T \|2.25 T \|2 - 7 T? \| \|avg_acc \|0.4578 \|0.4775 \|0.5045 \|0.5495 \|0.5676 \| \|glue (acc) \|0.474 \|0.4578 \|0.4289 \|0.7439 \|0.515 \| \|anli (acc) \|0.3478 \|0.3541 \|0.3697 \|0.5097 \|0.3803 \| \|mnli (acc) \|0.3294 \|0.3893 \|0.4269 \|0.7884 \|0.4542 \| \|mnli_mismatch (acc) \|0.3348 \|0.404 \|0.4395 \|0.784 \|0.4632 \| \|swag (acc) \|0.5512 \|0.5685 \|0.5658 \|0.5905 \|0.5756 \| \|lambada_standard (acc)\|0.6396 \|0.6868 \|0.6808 \|0.7004 \|0.6944 \| \|lambada_openai (acc) \|0.6872 \|0.746 \|0.7353 \|0.7502 \|0.7553 \| \|mmlu (acc) \|0.2812 \|0.2512 \|0.4077 \|0.438 \|0.5964 \| \|winogrande (acc) \|0.6725 \|0.6709 \|0.6914 \|0.7332 \|0.7364 \| \|wnli (acc) \|0.5775 \|0.4789 \|0.4648 \|0.493 \|0.5775 \| \|truthfulqa (acc) \|0.3015 \|0.2826 \|0.3205 \|0.3401 \|0.3537 \| \|logiqa (acc) \|0.2335 \|0.2151 \|0.2535 \|0.2458 \|0.2427 \| \|logiqa2 (acc) \|0.2506 \|0.2252 \|0.2564 \|0.2621 \|0.3022 \| \|sciq (acc) \|0.927 \|0.944 \|0.939 \|0.93 \|0.959 \| \|piqa (acc) \|0.7878 \|0.7949 \|0.7807 \|0.7764 \|0.8052 \| \|arc_easy (acc) \|0.7353 \|0.7479 \|0.7643 \|0.7445 \|0.8081 \| \|arc_challenge (acc) \|0.3677 \|0.4027 \|0.4309 \|0.4155 \|0.5009 \| \|hellaswag (acc) \|0.5572 \|0.5772 \|0.5713 \|0.56 \|0.6131 \| \|openbookqa (acc) \|0.292 \|0.306 \|0.316 \|0.304 \|0.33 \| \|mathqa (acc) \|0.26 \|0.2884 \|0.2801 \|0.2593 \|0.3554 \| \|arithmetic (acc) \|0.0069 \|0.2367 \|0.4703 \|0.1703 \|0.9004 \| See the following, for the full details on this model: [https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b) #### Running on CPU via HF transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') input = input.strip().replace('\r\n','\n').replace('\n\n','\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) text = "请介绍北京的旅游景点" prompt = generate_prompt(text) inputs = tokenizer(prompt, return_tensors="pt") output = model.generate(inputs["input_ids"], max_new_tokens=333, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 请介绍北京的旅游景点 Assistant: 北京是中国的首都，拥有众多的旅游景点，以下是其中一些著名的景点： 1. 故宫：位于北京市中心，是明清两代的皇宫，内有大量的文物和艺术品。 2. 天安门广场：是中国最著名的广场之一，是中国人民政治协商会议的旧址，也是中国人民政治协商会议的中心。 3. 颐和园：是中国古代皇家园林之一，有着悠久的历史和丰富的文化内涵。 4. 长城：是中国古代的一道长城，全长约万里，是中国最著名的旅游景点之一。 5. 北京大学：是中国著名的高等教育机构之一，有着悠久的历史和丰富的文化内涵。 6. 北京动物园：是中国最大的动物园之一，有着丰富的动物资源和丰富的文化内涵。 7. 故宫博物院：是中国最著名的博物馆之一，收藏了大量的文物和艺术品，是中国最重要的文化遗产之一。 8. 天坛：是中国古代皇家 ``` #### Running on GPU via HF transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') input = input.strip().replace('\r\n','\n').replace('\n\n','\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True, torch_dtype=torch.float16).to(0) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) text = "介绍一下大熊猫" prompt = generate_prompt(text) inputs = tokenizer(prompt, return_tensors="pt").to(0) output = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 介绍一下大熊猫 Assistant: 大熊猫是一种中国特有的哺乳动物，也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体，有着黑色的毛发和白色的耳朵。大熊猫的食物主要是竹子，它们会在竹林中寻找竹子，并且会将竹子放在竹笼中进行储存。大熊猫的寿命约为20至30年，但由于栖息地的丧失和人类活动的 ``` #### Batch Inference ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n', '\n').replace('\n\n', '\n') input = input.strip().replace('\r\n', '\n').replace('\n\n', '\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) texts = ["请介绍北京的旅游景点", "介绍一下大熊猫", "乌兰察布"] prompts = [generate_prompt(text) for text in texts] inputs = tokenizer(prompts, return_tensors="pt", padding=True) outputs = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) for output in outputs: print(tokenizer.decode(output.tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 请介绍北京的旅游景点 Assistant: 北京是中国的首都，拥有丰富的旅游资源和历史文化遗产。以下是一些北京的旅游景点： 1. 故宫：位于北京市中心，是明清两代的皇宫，是中国最大的古代宫殿建筑群之一。 2. 天安门广场：位于北京市中心，是中国最著名的城市广场之一，也是中国最大的城市广场。 3. 颐和 User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 介绍一下大熊猫 Assistant: 大熊猫是一种生活在中国中部地区的哺乳动物，也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体，有着黑色的毛发和圆圆的眼睛。大熊猫是一种濒危物种，目前只有在野外的几个保护区才能看到它们的身影。大熊猫的食物主要是竹子，它们会在竹子上寻找食物，并且可以通 User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 乌兰察布 Assistant: 乌兰察布是中国新疆维吾尔自治区的一个县级市，位于新疆维吾尔自治区中部，是新疆的第二大城市。乌兰察布市是新疆的第一大城市，也是新疆的重要城市之一。乌兰察布市是新疆的经济中心，也是新疆的重要交通枢纽之一。乌兰察布市的人口约为2.5万人，其中汉族占绝大多数。乌 ``` ## Links - [Our wiki](https://wiki.rwkv.com) - [Full eval data](https://docs.google.com/spreadsheets/d/1CBLU6yKkW-8FMvGD4INO3qjeHZ0qkKnZFcM6n6lWNOs/edit#gid=912381775) - [Recursal.AI Cloud Platform](https://recursal.ai) - [HF Gradio Demo](https://huggingface.co/spaces/RWKV/v5-EagleX-v2-7B-gradio) - [Blog article, detailing our model launch](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b) ## Acknowledgement We are grateful for the help and support from the following key groups: - [Recursal.ai](https://recursal.ai) team for financing the GPU resources, and managing the training of this foundation model - you can run the Eagle line of RWKV models on their cloud / on-premise platform today. - EleutherAI for their support, especially in the v5/v6 Eagle/Finch paper - Linux Foundation AI & Data group for supporting and hosting the RWKV project
Bajiyo/whisper-medium-studio-records_test	Bajiyo	2024-08-30T16:25:51Z	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-08-30T10:27:48Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-studio-records_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-studio-records_test This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0333 - Wer: 15.6507 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.0568 \| 0.4110 \| 1000 \| 0.0894 \| 43.3939 \| \| 0.0362 \| 0.8220 \| 2000 \| 0.0589 \| 29.9079 \| \| 0.0149 \| 1.2330 \| 3000 \| 0.0463 \| 22.6922 \| \| 0.0117 \| 1.6441 \| 4000 \| 0.0375 \| 19.2088 \| \| 0.0039 \| 2.0551 \| 5000 \| 0.0355 \| 16.1483 \| \| 0.0032 \| 2.4661 \| 6000 \| 0.0333 \| 15.6507 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
mradermacher/TwinLlama-3.1-8B-DPO2-GGUF	mradermacher	2024-08-30T16:17:09Z	7	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "dpo", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T15:50:20Z	--- base_model: mlabonne/TwinLlama-3.1-8B-DPO2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - dpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mlabonne/TwinLlama-3.1-8B-DPO2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ProElectro07/BioMistral-sharded	ProElectro07	2024-08-30T16:13:23Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T16:08:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
CharlesLi/OpenELM-1_1B-DPO-full-1-5	CharlesLi	2024-08-30T16:09:04Z	133	0	transformers	[ "transformers", "tensorboard", "safetensors", "openelm", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2024-08-30T08:21:20Z	--- library_name: transformers tags: - trl - dpo - generated_from_trainer model-index: - name: OpenELM-1_1B-DPO-full-1-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # OpenELM-1_1B-DPO-full-1-5 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1836 - Rewards/chosen: -14.0 - Rewards/rejected: -17.625 - Rewards/accuracies: 0.7227 - Rewards/margins: 3.625 - Logps/rejected: -2048.0 - Logps/chosen: -1720.0 - Logits/rejected: 4.2812 - Logits/chosen: 2.625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6268 \| 0.1047 \| 100 \| 0.6449 \| -0.4805 \| -0.6680 \| 0.6406 \| 0.1885 \| -356.0 \| -366.0 \| -9.5625 \| -10.0 \| \| 0.5924 \| 0.2093 \| 200 \| 0.5985 \| -1.2031 \| -1.6172 \| 0.6875 \| 0.4199 \| -450.0 \| -438.0 \| -12.875 \| -13.125 \| \| 0.6197 \| 0.3140 \| 300 \| 0.5811 \| -1.375 \| -1.8438 \| 0.7090 \| 0.4668 \| -474.0 \| -456.0 \| -11.75 \| -12.1875 \| \| 0.5968 \| 0.4186 \| 400 \| 0.5933 \| -2.3125 \| -2.8438 \| 0.6934 \| 0.5273 \| -572.0 \| -548.0 \| -8.5625 \| -9.25 \| \| 0.5854 \| 0.5233 \| 500 \| 0.5737 \| -1.7422 \| -2.2812 \| 0.6953 \| 0.5352 \| -516.0 \| -492.0 \| -7.7188 \| -8.625 \| \| 0.5524 \| 0.6279 \| 600 \| 0.5768 \| -3.0156 \| -3.7031 \| 0.6914 \| 0.6953 \| -660.0 \| -620.0 \| -7.0312 \| -7.7188 \| \| 0.5602 \| 0.7326 \| 700 \| 0.5756 \| -3.1562 \| -3.9062 \| 0.7168 \| 0.75 \| -680.0 \| -636.0 \| -5.125 \| -6.3438 \| \| 0.5581 \| 0.8373 \| 800 \| 0.5854 \| -3.3906 \| -4.0312 \| 0.6914 \| 0.6289 \| -692.0 \| -656.0 \| -5.0938 \| -5.9688 \| \| 0.5793 \| 0.9419 \| 900 \| 0.5657 \| -3.1719 \| -3.9062 \| 0.7207 \| 0.7383 \| -680.0 \| -636.0 \| -3.9531 \| -5.0312 \| \| 0.2783 \| 1.0466 \| 1000 \| 0.6053 \| -4.75 \| -5.875 \| 0.7188 \| 1.125 \| -876.0 \| -792.0 \| -2.2188 \| -3.3594 \| \| 0.2417 \| 1.1512 \| 1100 \| 0.6139 \| -4.7812 \| -5.8125 \| 0.7070 \| 1.0469 \| -872.0 \| -796.0 \| -2.3594 \| -4.125 \| \| 0.2429 \| 1.2559 \| 1200 \| 0.5897 \| -5.7188 \| -6.8125 \| 0.7227 \| 1.0781 \| -968.0 \| -892.0 \| -0.7188 \| -2.1719 \| \| 0.2508 \| 1.3605 \| 1300 \| 0.5948 \| -5.4062 \| -6.4062 \| 0.6914 \| 1.0 \| -928.0 \| -860.0 \| -0.0104 \| -1.5156 \| \| 0.2169 \| 1.4652 \| 1400 \| 0.6104 \| -5.7812 \| -6.9062 \| 0.7031 \| 1.1016 \| -976.0 \| -896.0 \| 0.0820 \| -1.75 \| \| 0.2107 \| 1.5699 \| 1500 \| 0.6062 \| -6.0625 \| -7.2812 \| 0.6973 \| 1.1953 \| -1016.0 \| -924.0 \| -0.4590 \| -2.1719 \| \| 0.2472 \| 1.6745 \| 1600 \| 0.6158 \| -5.625 \| -6.7188 \| 0.7070 \| 1.1016 \| -960.0 \| -880.0 \| -2.0312 \| -3.9688 \| \| 0.2545 \| 1.7792 \| 1700 \| 0.6170 \| -6.25 \| -7.5 \| 0.7031 \| 1.25 \| -1040.0 \| -944.0 \| -1.2578 \| -3.2031 \| \| 0.2383 \| 1.8838 \| 1800 \| 0.6061 \| -5.625 \| -6.75 \| 0.7012 \| 1.1172 \| -964.0 \| -880.0 \| 0.7383 \| -1.1328 \| \| 0.2107 \| 1.9885 \| 1900 \| 0.6135 \| -6.5 \| -7.7812 \| 0.7383 \| 1.2578 \| -1064.0 \| -968.0 \| 0.3027 \| -1.4297 \| \| 0.0186 \| 2.0931 \| 2000 \| 0.7473 \| -8.0625 \| -9.875 \| 0.7090 \| 1.8594 \| -1280.0 \| -1120.0 \| 2.2812 \| 0.4980 \| \| 0.03 \| 2.1978 \| 2100 \| 0.8345 \| -9.9375 \| -12.25 \| 0.7070 \| 2.2812 \| -1512.0 \| -1312.0 \| 3.2031 \| 1.5938 \| \| 0.0284 \| 2.3025 \| 2200 \| 0.7741 \| -9.1875 \| -11.3125 \| 0.7012 \| 2.0781 \| -1416.0 \| -1240.0 \| 2.7812 \| 1.0156 \| \| 0.0352 \| 2.4071 \| 2300 \| 0.7983 \| -9.3125 \| -11.3125 \| 0.7090 \| 2.0156 \| -1424.0 \| -1248.0 \| 2.6406 \| 0.9961 \| \| 0.0345 \| 2.5118 \| 2400 \| 0.8249 \| -9.8125 \| -12.0 \| 0.7266 \| 2.1719 \| -1488.0 \| -1304.0 \| 3.2656 \| 1.5625 \| \| 0.0192 \| 2.6164 \| 2500 \| 0.8865 \| -10.25 \| -12.5625 \| 0.6973 \| 2.2969 \| -1544.0 \| -1344.0 \| 3.5938 \| 1.9609 \| \| 0.0261 \| 2.7211 \| 2600 \| 0.7963 \| -9.1875 \| -11.4375 \| 0.7129 \| 2.25 \| -1432.0 \| -1240.0 \| 2.7031 \| 0.8672 \| \| 0.0315 \| 2.8257 \| 2700 \| 0.7619 \| -9.0 \| -10.9375 \| 0.7109 \| 1.9766 \| -1384.0 \| -1216.0 \| 2.8594 \| 0.8320 \| \| 0.0293 \| 2.9304 \| 2800 \| 0.8241 \| -9.75 \| -12.0625 \| 0.7070 \| 2.2656 \| -1496.0 \| -1296.0 \| 3.1719 \| 1.3359 \| \| 0.0071 \| 3.0351 \| 2900 \| 0.8609 \| -10.0625 \| -12.5 \| 0.7188 \| 2.3906 \| -1536.0 \| -1328.0 \| 3.1719 \| 1.3125 \| \| 0.0099 \| 3.1397 \| 3000 \| 0.9558 \| -11.5 \| -14.1875 \| 0.7051 \| 2.6875 \| -1704.0 \| -1472.0 \| 3.4062 \| 1.6484 \| \| 0.0079 \| 3.2444 \| 3100 \| 0.9341 \| -11.125 \| -13.75 \| 0.7090 \| 2.6562 \| -1664.0 \| -1432.0 \| 3.25 \| 1.5078 \| \| 0.0104 \| 3.3490 \| 3200 \| 0.9926 \| -11.9375 \| -14.8125 \| 0.7090 \| 2.9062 \| -1768.0 \| -1512.0 \| 3.6719 \| 1.9922 \| \| 0.0089 \| 3.4537 \| 3300 \| 0.9665 \| -11.9375 \| -14.8125 \| 0.7188 \| 2.875 \| -1768.0 \| -1512.0 \| 3.8594 \| 2.2656 \| \| 0.0098 \| 3.5583 \| 3400 \| 0.9548 \| -11.1875 \| -13.875 \| 0.7109 \| 2.75 \| -1680.0 \| -1432.0 \| 4.0 \| 2.3438 \| \| 0.0109 \| 3.6630 \| 3500 \| 1.0670 \| -12.5625 \| -15.6875 \| 0.7168 \| 3.1406 \| -1856.0 \| -1576.0 \| 4.1875 \| 2.5312 \| \| 0.0081 \| 3.7677 \| 3600 \| 1.0376 \| -12.375 \| -15.4375 \| 0.7188 \| 3.0938 \| -1832.0 \| -1552.0 \| 4.125 \| 2.4844 \| \| 0.0081 \| 3.8723 \| 3700 \| 1.0725 \| -13.0 \| -16.25 \| 0.7168 \| 3.25 \| -1912.0 \| -1616.0 \| 4.1875 \| 2.5938 \| \| 0.0041 \| 3.9770 \| 3800 \| 1.1346 \| -13.5 \| -17.0 \| 0.7188 \| 3.4688 \| -1984.0 \| -1672.0 \| 4.2188 \| 2.5781 \| \| 0.0036 \| 4.0816 \| 3900 \| 1.1589 \| -13.8125 \| -17.375 \| 0.7168 \| 3.5156 \| -2024.0 \| -1696.0 \| 4.25 \| 2.625 \| \| 0.0016 \| 4.1863 \| 4000 \| 1.1790 \| -14.0625 \| -17.625 \| 0.7168 \| 3.5781 \| -2048.0 \| -1720.0 \| 4.2812 \| 2.6719 \| \| 0.0037 \| 4.2909 \| 4100 \| 1.1847 \| -14.0625 \| -17.625 \| 0.7168 \| 3.6094 \| -2064.0 \| -1728.0 \| 4.3125 \| 2.6562 \| \| 0.007 \| 4.3956 \| 4200 \| 1.1905 \| -14.1875 \| -17.75 \| 0.7227 \| 3.6406 \| -2064.0 \| -1736.0 \| 4.3125 \| 2.6719 \| \| 0.0038 \| 4.5003 \| 4300 \| 1.1835 \| -14.0625 \| -17.75 \| 0.7207 \| 3.6406 \| -2064.0 \| -1728.0 \| 4.2812 \| 2.6406 \| \| 0.0093 \| 4.6049 \| 4400 \| 1.1819 \| -14.0625 \| -17.625 \| 0.7207 \| 3.625 \| -2048.0 \| -1720.0 \| 4.2812 \| 2.625 \| \| 0.006 \| 4.7096 \| 4500 \| 1.1817 \| -14.0 \| -17.625 \| 0.7227 \| 3.6406 \| -2048.0 \| -1720.0 \| 4.2812 \| 2.6094 \| \| 0.0037 \| 4.8142 \| 4600 \| 1.1826 \| -14.0 \| -17.625 \| 0.7227 \| 3.6406 \| -2048.0 \| -1720.0 \| 4.25 \| 2.6094 \| \| 0.0059 \| 4.9189 \| 4700 \| 1.1836 \| -14.0 \| -17.625 \| 0.7227 \| 3.625 \| -2048.0 \| -1720.0 \| 4.2812 \| 2.625 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
mradermacher/NeuroCom_4B-GGUF	mradermacher	2024-08-30T16:03:11Z	20	0	transformers	[ "transformers", "gguf", "en", "base_model:FourOhFour/NeuroCom_4B", "base_model:quantized:FourOhFour/NeuroCom_4B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T15:34:36Z	--- base_model: FourOhFour/NeuroCom_4B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/FourOhFour/NeuroCom_4B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q2_K.gguf) \| Q2_K \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ3_XS.gguf) \| IQ3_XS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q3_K_S.gguf) \| Q3_K_S \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ3_S.gguf) \| IQ3_S \| 2.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ3_M.gguf) \| IQ3_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q3_K_M.gguf) \| Q3_K_M \| 2.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q3_K_L.gguf) \| Q3_K_L \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ4_XS.gguf) \| IQ4_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q4_K_S.gguf) \| Q4_K_S \| 2.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q4_K_M.gguf) \| Q4_K_M \| 2.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q5_K_S.gguf) \| Q5_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q5_K_M.gguf) \| Q5_K_M \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q6_K.gguf) \| Q6_K \| 3.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q8_0.gguf) \| Q8_0 \| 4.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
AQ206/models-that-will-literally-shatter-your-right-leg	AQ206	2024-08-30T15:59:26Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-06-30T16:56:10Z	--- license: openrail --- if you use any of these models on rvc... your right leg will absolutely shatter into pieces! if you want to keep your right leg, be cautious.
mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-64-5e	mrm8488	2024-08-30T15:54:21Z	74	2	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "dataset_size:1K<n<10K", "loss:MatryoshkaLoss", "loss:CoSENTLoss", "arxiv:1908.10084", "arxiv:2205.13147", "base_model:intfloat/multilingual-e5-large", "base_model:finetune:intfloat/multilingual-e5-large", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-01T22:13:40Z	--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:1K<n<10K - loss:MatryoshkaLoss - loss:CoSENTLoss base_model: intfloat/multilingual-e5-large metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: El hombre captura una pelota sentences: - Un hombre lanza una pelota en el aire. - Un hombre se encuentra tocando una flauta de madera. - La mujer está maquillándose usando sombra de ojos. - source_sentence: Un hombre está buscando algo. sentences: - En un mercado de granjeros, se encuentra un hombre. - Se acerca a la pista un avión suizo de color blanco. - dos chicas jóvenes se abrazan en la hierba. - source_sentence: El avión está tocando tierra. sentences: - El avión animado se encuentra en proceso de aterrizaje. - La capital de Siria fue golpeada por dos explosiones - Violentos incidentes afectan a estudiantes chinos en Francia - source_sentence: Un hombre saltando la cuerda. sentences: - Un hombre está saltando la cuerda. - Una mujer entrena a su perro para saltar en el aire. - Los gatitos están comiendo de los platos. - source_sentence: tres perros gruñendo entre sí sentences: - Dos perros se aproximan uno al otro en el pasto. - Una mujer sonriente brinda cariño a un pequeño bebé. - Una mujer está montando a caballo en el campo. pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-large results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 768 type: sts-dev-768 metrics: - type: pearson_cosine value: 0.8279951103268512 name: Pearson Cosine - type: spearman_cosine value: 0.8342643795984531 name: Spearman Cosine - type: pearson_manhattan value: 0.8228439538329566 name: Pearson Manhattan - type: spearman_manhattan value: 0.834870903153992 name: Spearman Manhattan - type: pearson_euclidean value: 0.8231076969394738 name: Pearson Euclidean - type: spearman_euclidean value: 0.8349270059177344 name: Spearman Euclidean - type: pearson_dot value: 0.8196281042113861 name: Pearson Dot - type: spearman_dot value: 0.8248683461954115 name: Spearman Dot - type: pearson_max value: 0.8279951103268512 name: Pearson Max - type: spearman_max value: 0.8349270059177344 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 512 type: sts-dev-512 metrics: - type: pearson_cosine value: 0.8236357426336446 name: Pearson Cosine - type: spearman_cosine value: 0.8332692872015282 name: Spearman Cosine - type: pearson_manhattan value: 0.8217552769156274 name: Pearson Manhattan - type: spearman_manhattan value: 0.8331746060276878 name: Spearman Manhattan - type: pearson_euclidean value: 0.8217859136681092 name: Pearson Euclidean - type: spearman_euclidean value: 0.8334069456110773 name: Spearman Euclidean - type: pearson_dot value: 0.8101789790612713 name: Pearson Dot - type: spearman_dot value: 0.8179205607773823 name: Spearman Dot - type: pearson_max value: 0.8236357426336446 name: Pearson Max - type: spearman_max value: 0.8334069456110773 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 256 type: sts-dev-256 metrics: - type: pearson_cosine value: 0.816222860848086 name: Pearson Cosine - type: spearman_cosine value: 0.8303708513421737 name: Spearman Cosine - type: pearson_manhattan value: 0.8178715987143794 name: Pearson Manhattan - type: spearman_manhattan value: 0.8301047046554985 name: Spearman Manhattan - type: pearson_euclidean value: 0.8183826652089494 name: Pearson Euclidean - type: spearman_euclidean value: 0.8301804247624904 name: Spearman Euclidean - type: pearson_dot value: 0.7878741921967743 name: Pearson Dot - type: spearman_dot value: 0.7904844114269662 name: Spearman Dot - type: pearson_max value: 0.8183826652089494 name: Pearson Max - type: spearman_max value: 0.8303708513421737 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 128 type: sts-dev-128 metrics: - type: pearson_cosine value: 0.794202606017138 name: Pearson Cosine - type: spearman_cosine value: 0.8198385906414491 name: Spearman Cosine - type: pearson_manhattan value: 0.8088714046889546 name: Pearson Manhattan - type: spearman_manhattan value: 0.8222921243120748 name: Spearman Manhattan - type: pearson_euclidean value: 0.8092312345267045 name: Pearson Euclidean - type: spearman_euclidean value: 0.8220266161646009 name: Spearman Euclidean - type: pearson_dot value: 0.7341586721030032 name: Pearson Dot - type: spearman_dot value: 0.7351749794310246 name: Spearman Dot - type: pearson_max value: 0.8092312345267045 name: Pearson Max - type: spearman_max value: 0.8222921243120748 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 64 type: sts-dev-64 metrics: - type: pearson_cosine value: 0.7727295051414095 name: Pearson Cosine - type: spearman_cosine value: 0.8076629783565549 name: Spearman Cosine - type: pearson_manhattan value: 0.7976419723073269 name: Pearson Manhattan - type: spearman_manhattan value: 0.8147883308842346 name: Spearman Manhattan - type: pearson_euclidean value: 0.7979124462870892 name: Pearson Euclidean - type: spearman_euclidean value: 0.8123832197697319 name: Spearman Euclidean - type: pearson_dot value: 0.6725844492342726 name: Pearson Dot - type: spearman_dot value: 0.6673162832940408 name: Spearman Dot - type: pearson_max value: 0.7979124462870892 name: Pearson Max - type: spearman_max value: 0.8147883308842346 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: 0.8630482725201897 name: Pearson Cosine - type: spearman_cosine value: 0.8813284718659181 name: Spearman Cosine - type: pearson_manhattan value: 0.8770818288812614 name: Pearson Manhattan - type: spearman_manhattan value: 0.8810971983428288 name: Spearman Manhattan - type: pearson_euclidean value: 0.8770132070253477 name: Pearson Euclidean - type: spearman_euclidean value: 0.8812162173545179 name: Spearman Euclidean - type: pearson_dot value: 0.8581811981775829 name: Pearson Dot - type: spearman_dot value: 0.8707402246720045 name: Spearman Dot - type: pearson_max value: 0.8770818288812614 name: Pearson Max - type: spearman_max value: 0.8813284718659181 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.8589909139210625 name: Pearson Cosine - type: spearman_cosine value: 0.8799604919891442 name: Spearman Cosine - type: pearson_manhattan value: 0.8744468387217347 name: Pearson Manhattan - type: spearman_manhattan value: 0.8791142262015441 name: Spearman Manhattan - type: pearson_euclidean value: 0.8747974723064821 name: Pearson Euclidean - type: spearman_euclidean value: 0.8795698184784307 name: Spearman Euclidean - type: pearson_dot value: 0.8464185524060444 name: Pearson Dot - type: spearman_dot value: 0.8549652098582826 name: Spearman Dot - type: pearson_max value: 0.8747974723064821 name: Pearson Max - type: spearman_max value: 0.8799604919891442 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.8528262537030415 name: Pearson Cosine - type: spearman_cosine value: 0.8762917275750132 name: Spearman Cosine - type: pearson_manhattan value: 0.8715060008387856 name: Pearson Manhattan - type: spearman_manhattan value: 0.8780718380107112 name: Spearman Manhattan - type: pearson_euclidean value: 0.87251419758469 name: Pearson Euclidean - type: spearman_euclidean value: 0.8788770265821976 name: Spearman Euclidean - type: pearson_dot value: 0.801980870958869 name: Pearson Dot - type: spearman_dot value: 0.8007112694661982 name: Spearman Dot - type: pearson_max value: 0.87251419758469 name: Pearson Max - type: spearman_max value: 0.8788770265821976 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.8392066286150661 name: Pearson Cosine - type: spearman_cosine value: 0.8692426944903685 name: Spearman Cosine - type: pearson_manhattan value: 0.8631603748425567 name: Pearson Manhattan - type: spearman_manhattan value: 0.8715673768304316 name: Spearman Manhattan - type: pearson_euclidean value: 0.8643871758114816 name: Pearson Euclidean - type: spearman_euclidean value: 0.8724091426441261 name: Spearman Euclidean - type: pearson_dot value: 0.7461565194503229 name: Pearson Dot - type: spearman_dot value: 0.7403017354497338 name: Spearman Dot - type: pearson_max value: 0.8643871758114816 name: Pearson Max - type: spearman_max value: 0.8724091426441261 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.8213671607347727 name: Pearson Cosine - type: spearman_cosine value: 0.8621003145087452 name: Spearman Cosine - type: pearson_manhattan value: 0.8530869243121955 name: Pearson Manhattan - type: spearman_manhattan value: 0.8631973638935834 name: Spearman Manhattan - type: pearson_euclidean value: 0.854140567169475 name: Pearson Euclidean - type: spearman_euclidean value: 0.8632627342101252 name: Spearman Euclidean - type: pearson_dot value: 0.6853599968011839 name: Pearson Dot - type: spearman_dot value: 0.6726454086764928 name: Spearman Dot - type: pearson_max value: 0.854140567169475 name: Pearson Max - type: spearman_max value: 0.8632627342101252 name: Spearman Max --- # SentenceTransformer based on intfloat/multilingual-e5-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the clibrain/stsb_multi_es_aug_gpt3.5-turbo_2 dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 tokens - Similarity Function: Cosine Similarity - Training Dataset: - stsb_multi_es_aug <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-64-5e") # Run inference sentences = [ 'tres perros gruñendo entre sí', 'Dos perros se aproximan uno al otro en el pasto.', 'Una mujer sonriente brinda cariño a un pequeño bebé.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev-768` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.828 \| \| spearman_cosine \| 0.8343 \| \| pearson_manhattan \| 0.8228 \| \| spearman_manhattan \| 0.8349 \| \| pearson_euclidean \| 0.8231 \| \| spearman_euclidean \| 0.8349 \| \| pearson_dot \| 0.8196 \| \| spearman_dot \| 0.8249 \| \| pearson_max \| 0.828 \| \| spearman_max \| 0.8349 \| #### Semantic Similarity * Dataset: `sts-dev-512` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8236 \| \| spearman_cosine \| 0.8333 \| \| pearson_manhattan \| 0.8218 \| \| spearman_manhattan \| 0.8332 \| \| pearson_euclidean \| 0.8218 \| \| spearman_euclidean \| 0.8334 \| \| pearson_dot \| 0.8102 \| \| spearman_dot \| 0.8179 \| \| pearson_max \| 0.8236 \| \| spearman_max \| 0.8334 \| #### Semantic Similarity * Dataset: `sts-dev-256` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8162 \| \| spearman_cosine \| 0.8304 \| \| pearson_manhattan \| 0.8179 \| \| spearman_manhattan \| 0.8301 \| \| pearson_euclidean \| 0.8184 \| \| spearman_euclidean \| 0.8302 \| \| pearson_dot \| 0.7879 \| \| spearman_dot \| 0.7905 \| \| pearson_max \| 0.8184 \| \| spearman_max \| 0.8304 \| #### Semantic Similarity * Dataset: `sts-dev-128` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.7942 \| \| spearman_cosine \| 0.8198 \| \| pearson_manhattan \| 0.8089 \| \| spearman_manhattan \| 0.8223 \| \| pearson_euclidean \| 0.8092 \| \| spearman_euclidean \| 0.822 \| \| pearson_dot \| 0.7342 \| \| spearman_dot \| 0.7352 \| \| pearson_max \| 0.8092 \| \| spearman_max \| 0.8223 \| #### Semantic Similarity * Dataset: `sts-dev-64` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.7727 \| \| spearman_cosine \| 0.8077 \| \| pearson_manhattan \| 0.7976 \| \| spearman_manhattan \| 0.8148 \| \| pearson_euclidean \| 0.7979 \| \| spearman_euclidean \| 0.8124 \| \| pearson_dot \| 0.6726 \| \| spearman_dot \| 0.6673 \| \| pearson_max \| 0.7979 \| \| spearman_max \| 0.8148 \| #### Semantic Similarity * Dataset: `sts-test-768` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.863 \| \| spearman_cosine \| 0.8813 \| \| pearson_manhattan \| 0.8771 \| \| spearman_manhattan \| 0.8811 \| \| pearson_euclidean \| 0.877 \| \| spearman_euclidean \| 0.8812 \| \| pearson_dot \| 0.8582 \| \| spearman_dot \| 0.8707 \| \| pearson_max \| 0.8771 \| \| spearman_max \| 0.8813 \| #### Semantic Similarity * Dataset: `sts-test-512` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:---------\| \| pearson_cosine \| 0.859 \| \| spearman_cosine \| 0.88 \| \| pearson_manhattan \| 0.8744 \| \| spearman_manhattan \| 0.8791 \| \| pearson_euclidean \| 0.8748 \| \| spearman_euclidean \| 0.8796 \| \| pearson_dot \| 0.8464 \| \| spearman_dot \| 0.855 \| \| pearson_max \| 0.8748 \| \| spearman_max \| 0.88 \| #### Semantic Similarity * Dataset: `sts-test-256` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8528 \| \| spearman_cosine \| 0.8763 \| \| pearson_manhattan \| 0.8715 \| \| spearman_manhattan \| 0.8781 \| \| pearson_euclidean \| 0.8725 \| \| spearman_euclidean \| 0.8789 \| \| pearson_dot \| 0.802 \| \| spearman_dot \| 0.8007 \| \| pearson_max \| 0.8725 \| \| spearman_max \| 0.8789 \| #### Semantic Similarity * Dataset: `sts-test-128` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8392 \| \| spearman_cosine \| 0.8692 \| \| pearson_manhattan \| 0.8632 \| \| spearman_manhattan \| 0.8716 \| \| pearson_euclidean \| 0.8644 \| \| spearman_euclidean \| 0.8724 \| \| pearson_dot \| 0.7462 \| \| spearman_dot \| 0.7403 \| \| pearson_max \| 0.8644 \| \| spearman_max \| 0.8724 \| #### Semantic Similarity * Dataset: `sts-test-64` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8214 \| \| spearman_cosine \| 0.8621 \| \| pearson_manhattan \| 0.8531 \| \| spearman_manhattan \| 0.8632 \| \| pearson_euclidean \| 0.8541 \| \| spearman_euclidean \| 0.8633 \| \| pearson_dot \| 0.6854 \| \| spearman_dot \| 0.6726 \| \| pearson_max \| 0.8541 \| \| spearman_max \| 0.8633 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### stsb_multi_es_aug * Dataset: stsb_multi_es_aug * Size: 2,697 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 8 tokens</li><li>mean: 22.25 tokens</li><li>max: 68 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 22.01 tokens</li><li>max: 79 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 2.67</li><li>max: 5.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------\|:-------------------------------\| \| <code>El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha.</code> \| <code>Un ave de color amarillo descansaba tranquilamente en una rama.</code> \| <code>3.200000047683716</code> \| \| <code>Una chica está tocando la flauta en un parque.</code> \| <code>Un grupo de músicos está tocando en un escenario al aire libre.</code> \| <code>1.286</code> \| \| <code>La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece</code> \| <code>La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere</code> \| <code>4.199999809265137</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### stsb_multi_es_aug * Dataset: stsb_multi_es_aug * Size: 697 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:--------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 8 tokens</li><li>mean: 22.76 tokens</li><li>max: 67 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 22.26 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 2.3</li><li>max: 5.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------\| \| <code>Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas.</code> \| <code>Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos.</code> \| <code>4.199999809265137</code> \| \| <code>"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud"</code> \| <code>"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar."</code> \| <code>3.5</code> \| \| <code>El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario.</code> \| <code>Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida.</code> \| <code>3.691999912261963</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| loss \| sts-dev-128_spearman_cosine \| sts-dev-256_spearman_cosine \| sts-dev-512_spearman_cosine \| sts-dev-64_spearman_cosine \| sts-dev-768_spearman_cosine \| sts-test-128_spearman_cosine \| sts-test-256_spearman_cosine \| sts-test-512_spearman_cosine \| sts-test-64_spearman_cosine \| sts-test-768_spearman_cosine \| \|:------:\|:----:\|:-------------:\|:-------:\|:---------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\| \| 0.5917 \| 100 \| 21.7032 \| 21.7030 \| 0.8030 \| 0.8124 \| 0.8205 \| 0.7839 \| 0.8215 \| - \| - \| - \| - \| - \| \| 1.1834 \| 200 \| 21.4019 \| 24.0898 \| 0.7839 \| 0.7972 \| 0.8038 \| 0.7680 \| 0.8062 \| - \| - \| - \| - \| - \| \| 1.7751 \| 300 \| 21.2168 \| 22.5421 \| 0.7909 \| 0.8027 \| 0.8058 \| 0.7786 \| 0.8068 \| - \| - \| - \| - \| - \| \| 2.3669 \| 400 \| 20.7049 \| 23.6522 \| 0.7938 \| 0.8049 \| 0.8108 \| 0.7873 \| 0.8123 \| - \| - \| - \| - \| - \| \| 2.9586 \| 500 \| 20.5077 \| 23.6100 \| 0.8017 \| 0.8116 \| 0.8155 \| 0.7893 \| 0.8185 \| - \| - \| - \| - \| - \| \| 3.5503 \| 600 \| 19.2725 \| 24.7539 \| 0.8133 \| 0.8254 \| 0.8291 \| 0.8032 \| 0.8314 \| - \| - \| - \| - \| - \| \| 4.1420 \| 700 \| 19.0841 \| 26.5286 \| 0.8210 \| 0.8298 \| 0.8333 \| 0.8102 \| 0.8333 \| - \| - \| - \| - \| - \| \| 4.7337 \| 800 \| 18.6847 \| 26.8158 \| 0.8198 \| 0.8304 \| 0.8333 \| 0.8077 \| 0.8343 \| - \| - \| - \| - \| - \| \| 5.0 \| 845 \| - \| - \| - \| - \| - \| - \| - \| 0.8692 \| 0.8763 \| 0.8800 \| 0.8621 \| 0.8813 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - Transformers: 4.41.1 - PyTorch: 2.3.0+cu121 - Accelerate: 0.30.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mradermacher/Westlake-Scribe-GGUF	mradermacher	2024-08-30T15:54:08Z	46	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:DazzlingXeno/Westlake-Scribe", "base_model:quantized:DazzlingXeno/Westlake-Scribe", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T10:45:30Z	--- base_model: DazzlingXeno/Westlake-Scribe language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DazzlingXeno/Westlake-Scribe <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Westlake-Scribe-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q2_K.gguf) \| Q2_K \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ3_XS.gguf) \| IQ3_XS \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q3_K_S.gguf) \| Q3_K_S \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ3_S.gguf) \| IQ3_S \| 7.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ3_M.gguf) \| IQ3_M \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q3_K_M.gguf) \| Q3_K_M \| 8.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q3_K_L.gguf) \| Q3_K_L \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ4_XS.gguf) \| IQ4_XS \| 9.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q4_K_S.gguf) \| Q4_K_S \| 10.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q4_K_M.gguf) \| Q4_K_M \| 10.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q5_K_S.gguf) \| Q5_K_S \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q5_K_M.gguf) \| Q5_K_M \| 12.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q6_K.gguf) \| Q6_K \| 14.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q8_0.gguf) \| Q8_0 \| 19.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
bif02/eb_t5_base_16	bif02	2024-08-30T15:53:17Z	5	0	null	[ "tensorboard", "safetensors", "t5", "generated_from_trainer", "base_model:bif02/eb_t5_base_16", "base_model:finetune:bif02/eb_t5_base_16", "license:apache-2.0", "region:us" ]	null	2024-08-25T12:11:37Z	--- license: apache-2.0 base_model: bif02/eb_t5_base_16 tags: - generated_from_trainer model-index: - name: eb_t5_base_16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eb_t5_base_16 This model is a fine-tuned version of [bif02/eb_t5_base_16](https://huggingface.co/bif02/eb_t5_base_16) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0058 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0031 \| 1.0 \| 1107 \| 0.0058 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
leap-llm/Meta-Llama-3-8B-Instruct-sft-alfworld-iter2-3e-5	leap-llm	2024-08-30T15:42:35Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T15:36:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BishFish/DialoGPT-small-edward	BishFish	2024-08-30T15:39:26Z	117	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-29T14:08:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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julienkay/stable-diffusion-v1-5	julienkay	2024-08-30T15:39:14Z	6	1	diffusers	[ "diffusers", "onnx", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "diffusers:OnnxStableDiffusionPipeline", "region:us" ]	text-to-image	2024-08-30T12:52:44Z	--- license: creativeml-openrail-m pipeline_tag: text-to-image library_name: diffusers --- This is a reupload of the ONNX version of the stable-diffusion-v1-5 model previously hosted in the runwayml/stable-diffusion-v1-5 repository that is now offline. I'm mostly reuploading this for my own experiments on running diffusion models with Unity Sentis, but the model should still work with any onnx-compatible library of course. See [com.doji.diffusers](https://github.com/julienkay/com.doji.diffusers) for details. Here is the original model card: --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this with the [🧨Diffusers library](https://github.com/huggingface/diffusers). ### Diffusers usage ```py from diffusers import StableDiffusionPipeline import torch pipe = StableDiffusionPipeline.from_pretrained( "benjamin-paine/stable-diffusion-v1-5", torch_dtype=torch.float16 ) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - Resources for more information: [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` after generation of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training Training Data The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) Training Procedure Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/benjamin-paine/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/benjamin-paine/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - Hardware: 32 x 8 x A100 GPUs - Optimizer: AdamW - Gradient Accumulations: 2 - Batch: 32 x 8 x 2 x 4 = 2048 - Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 150000 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).
Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit	Agnuxo	2024-08-30T15:34:12Z	346	1	adapter-transformers	[ "adapter-transformers", "gguf", "llama", "text-generation-inference", "transformers", "unsloth", "mistral", "en", "es", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:codefuse-ai/CodeExercise-Python-27k", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:mlabonne/Evol-Instruct-Python-26k", "base_model:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit", "base_model:adapter:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-24T13:50:42Z	--- base_model: Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit language: - en - es license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca - codefuse-ai/CodeExercise-Python-27k - Vezora/Tested-22k-Python-Alpaca - mlabonne/Evol-Instruct-Python-26k metrics: - code_eval library_name: adapter-transformers --- # Uploaded model [<img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" width="100"/><img src="https://github.githubassets.com/assets/GitHub-Logo-ee398b662d42.png" width="100"/>](https://github.com/Agnuxo1) - Developed by: Agnuxo(https://github.com/Agnuxo1) - License: apache-2.0 - Finetuned from model : Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Agnuxo/Mistral-NeMo-Minitron-8B-Alpaca-CODE-Python-GGUF-16bit	Agnuxo	2024-08-30T15:32:15Z	29	0	adapter-transformers	[ "adapter-transformers", "gguf", "mistral", "text-generation-inference", "transformers", "unsloth", "en", "es", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:codefuse-ai/CodeExercise-Python-27k", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:mlabonne/Evol-Instruct-Python-26k", "base_model:Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal", "base_model:adapter:Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-08-23T12:02:41Z	--- base_model: Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal language: - en - es license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca - codefuse-ai/CodeExercise-Python-27k - Vezora/Tested-22k-Python-Alpaca - mlabonne/Evol-Instruct-Python-26k metrics: - code_eval library_name: adapter-transformers --- # Uploaded model [<img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" width="100"/><img src="https://github.githubassets.com/assets/GitHub-Logo-ee398b662d42.png" width="100"/>](https://github.com/Agnuxo1) - Developed by: Agnuxo(https://github.com/Agnuxo1) - License: apache-2.0 - Finetuned from model : Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
abhinayadutta/deepseek-coder-1.3b-base-int8-plsql-codegen	abhinayadutta	2024-08-30T15:31:57Z	75	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-19T10:12:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
alpacaml/stable-diffusion-v1-5	alpacaml	2024-08-30T15:26:28Z	121	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-08-30T15:26:28Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true extra_gated_prompt: >- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model duplicated_from: runwayml/stable-diffusion-v1-5 --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - Resources for more information: [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` after generation of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training Training Data The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) Training Procedure Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - Hardware: 32 x 8 x A100 GPUs - Optimizer: AdamW - Gradient Accumulations: 2 - Batch: 32 x 8 x 2 x 4 = 2048 - Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 150000 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).
Neuranest/Phi-3.5-mini-instruct-hfc-gguf	Neuranest	2024-08-30T15:21:19Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T15:15:49Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: Neuranest - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_4bit	Agnuxo	2024-08-30T15:14:37Z	18	0	adapter-transformers	[ "adapter-transformers", "gguf", "llama", "text-generation-inference", "transformers", "unsloth", "en", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:codefuse-ai/CodeExercise-Python-27k", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:mlabonne/Evol-Instruct-Python-26k", "base_model:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant_16bit", "base_model:adapter:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant_16bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-24T13:57:37Z	--- base_model: Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant_16bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca - codefuse-ai/CodeExercise-Python-27k - Vezora/Tested-22k-Python-Alpaca - mlabonne/Evol-Instruct-Python-26k metrics: - code_eval library_name: adapter-transformers --- # Uploaded model - Developed by: Agnuxo - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit	Agnuxo	2024-08-30T15:11:44Z	234	3	adapter-transformers	[ "adapter-transformers", "gguf", "mistral", "text-generation-inference", "transformers", "unsloth", "en", "es", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "base_model:Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit", "base_model:adapter:Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-08-24T16:38:03Z	--- base_model: Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit language: - en - es license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca metrics: - code_eval library_name: adapter-transformers --- # Uploaded model - Developed by: Agnuxo - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Neuranest/Phi-3.5-mini-instruct-hfc-16bit	Neuranest	2024-08-30T15:10:40Z	118	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-08-30T15:08:33Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: Neuranest - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX	v000000	2024-08-30T14:57:56Z	125	4	null	[ "gguf", "llama", "merge", "llama-cpp", "base_model:v000000/L3.1-Niitorm-8B-LATCOSx2", "base_model:quantized:v000000/L3.1-Niitorm-8B-LATCOSx2", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-29T21:10:49Z	--- base_model: v000000/L3.1-Niitorm-8B-LATCOSx2 tags: - llama - merge - llama-cpp --- # Llama-3.1-Niitorm-8B-LATCOSx2 ![d48ca23f-9063-4a66-a6b8-0abcbfe26dc5.jpg](https://cdn-uploads.huggingface.co/production/uploads/64f74b6e6389380c77562762/96he_28zzbVZBoh29QlIm.jpeg) # Ordered by quality: * q8_0 imatrix * q8_0 * q6_k imatrix * q6_k * q5_k_m imatrix * q5_k_s imatrix * q4_k_m imatrix * q4_k_s imatrix * iq4_xs imatrix * q4_0_4_8 imatrix arm * q4_0_4_4 imatrix arm This is a test RP model, <b>"v000000/L3.1-Niitorm-8B-t0.0001"</b> but merged one extra time with <b>"akjindal53244/Llama-3.1-Storm-8B"</b>. Using a new merging algorithm I wrote <b>"LATCOS"</b>, which is non linear interpolation and cosine vector similarity between tensors in both magnitude and direction. This attempts to find the smoothest possible interpolation and make them work more seamlessly together by taking into account the vector direction where both models agree. The model seems a lot smarter even though it's just a bit more of storm, but also more compliant which could be a negative since it's less "dynamic". <i>imatrix data randomized bartowski, kalomeze, rp snippets, working gpt4 code, human messaging, story</i>
lmstudio-community/c4ai-command-r-08-2024-GGUF	lmstudio-community	2024-08-30T14:56:25Z	528	22	transformers	[ "transformers", "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "base_model:CohereForAI/c4ai-command-r-08-2024", "base_model:quantized:CohereForAI/c4ai-command-r-08-2024", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-08-30T14:34:31Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox quantized_by: bartowski pipeline_tag: text-generation base_model: CohereForAI/c4ai-command-r-08-2024 lm_studio: param_count: 35b use_case: general release_date: 30-08-2024 model_creator: CohereForAI prompt_template: cohere_command_r base_model: Cohere system_prompt: You are a large language model called Command R built by the company Cohere. You act as a brilliant, sophisticated, AI-assistant chatbot trained to assist human users by providing thorough responses. original_repo: CohereForAI/c4ai-command-r-08-2024 --- ## 💫 Community Model> C4AI Command R 08-2024 by Cohere For AI 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [CohereForAI](https://huggingface.co/CohereForAI)<br> Original model: [c4ai-command-r-08-2024](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3634](https://github.com/ggerganov/llama.cpp/releases/tag/b3634)<br> ## Model Summary: C4AI Command R 08-2024 is an update to the originally released 35B paramater Command R. The original Command R model received sweeping praise for its incredible RAG and multilingual abilities, and this model is no different.<br> Not for commercial use, must adhere to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ## Prompt Template: Choose the `Cohere Command R` preset in your LM Studio. Under the hood, the model will see a prompt that's formatted like so: ``` <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>{prompt}<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> ``` This model also supports tool use and RAG prompt formats. For details on formatting for those use cases, view [tool use here](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024#tool-use--agent-capabilities) and [RAG capabilities here](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024#grounded-generation-and-rag-capabilities) ## Technical Details C4AI Command R 08-2024 has been trained on 23 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian). Due to this multilingual training, it excels in multilingual tasks. Command R 08-2024 supports a context length of 128K. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio. ### Terms of Use (directly from Cohere For AI) We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).
U4R/StructTable-base	U4R	2024-08-30T14:53:12Z	42,463	7	transformers	[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "image-to-text", "en", "zh", "arxiv:2406.11633", "license:apache-2.0", "region:us" ]	image-to-text	2024-07-26T13:34:55Z	--- language: - en - zh pipeline_tag: image-to-text inference: false license: apache-2.0 --- <div align="center"> <h1>StructEqTable-Deploy: A High-efficiency Open-source Toolkit for Table-to-Latex Transformation</h1> [[ Github Repo ]](https://github.com/UniModal4Reasoning/StructEqTable-Deploy) [[ Related Paper ]](https://arxiv.org/abs/2406.11633) [[ Website ]](https://unimodal4reasoning.github.io/DocGenome_page/) [[ Dataset (Google Drive)]](https://drive.google.com/drive/folders/1OIhnuQdIjuSSDc_QL2nP4NwugVDgtItD) [[ Dataset (Hugging Face) ]](https://huggingface.co/datasets/U4R/DocGenome/tree/main) [[Models 🤗(Hugging Face)]](https://huggingface.co/U4R/StructTable-base/tree/main) </div> Welcome to the official repository of StructEqTable-Deploy, a solution that converts images of Table into LaTeX, powered by scalable data from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/). ## Overview Table is an effective way to represent structured data in scientific publications, financial statements, invoices, web pages, and many other scenarios. Extracting tabular data from a visual table image and performing the downstream reasoning tasks according to the extracted data is challenging, mainly due to that tables often present complicated column and row headers with spanning cell operation. To address these challenges, we present TableX, a large-scale multi-modal table benchmark extracted from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/) for table pre-training, comprising more than 2 million high-quality Image-LaTeX pair data covering 156 disciplinary classes. Besides, benefiting from such large-scale data, we train an end-to-end model, StructEqTable, which provides the capability to precisely obtain the corresponding LaTeX description from a visual table image and perform multiple table-related reasoning tasks, including structural extraction and question answering, broadening its application scope and potential. ## Changelog Tips: Current version of StructEqTable is able to process table images from scientific documents such as arXiv, Scihub papers. Times New Roman And Songti(宋体) are main fonts used in table image, other fonts may decrease the accuracy of the model's output. - [2024/8/22] 🔥 We have released our [latest model](https://huggingface.co/U4R/StructTable-base/tree/v0.2), fine-tuned on the DocGenome dataset. This version features improved inference speed and robustness, achieved through data augmentation and reduced image token num. - [2024/8/08] We have released the TensorRT accelerated version, which only takes about 1 second for most images on GPU A100. Please follow the tutorial to install the environment and compile the model weights. - [2024/7/30] We have released the first version of StructEqTable. ## TODO - [x] Release inference code and checkpoints of StructEqTable. - [x] Support Chinese version of StructEqTable. - [x] Accelerated version of StructEqTable using TensorRT-LLM. - [ ] Expand more domains of table image to improve the model's general capabilities. - [ ] Release our table pre-training and fine-tuning code ## Efficient Inference Our model now supports TensorRT-LLM deployment, achieving a 10x or more speedup in during inference. Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) to learn how to depoly. ## Installation ``` bash conda create -n structeqtable python>=3.10 conda activate structeqtable # Install from Source code (Suggested) git clone https://github.com/UniModal4Reasoning/StructEqTable-Deploy.git cd StructEqTable-Deploy python setup develop # or Install from Github repo pip install "git+https://github.com/UniModal4Reasoning/StructEqTable-Deploy.git" # or Install from PyPI pip install struct-eqtable==0.1.0 ``` ## Model Zoo \| Model \| Image Token Num \| Model Size \| Training Data \| Data Augmentation \| TensorRT \| HuggingFace \| \|---------------------\|---------------------\|------------\|------------------\|-------------------\|----------\|-------------------\| \| StructEqTable-base \| 4096 \| ~300M \| DocGenome \| \| ☑️ \| [v0.1](https://huggingface.co/U4R/StructTable-base/tree/v0.1) \| \| StructEqTable-base \| 2048 \| ~300M \| DocGenome \| ☑️ \| ☑️ \| [v0.2](https://huggingface.co/U4R/StructTable-base/tree/v0.2) \| ## Quick Demo - Run the demo/demo.py ```shell script cd tools/demo python demo.py \ --image_path ./demo.png \ --ckpt_path ${CKPT_PATH} \ --output_format latex ``` - HTML or Markdown format output Our model output Latex format code by default. If you want to get other format like HTML or Markdown, `pypandoc` support convert latex format code into HTML and Markdown format for simple table (table has no merge cell ). ```shell script sudo apt install pandoc pip install pypandoc cd tools/demo python demo.py \ --image_path ./demo.png \ --ckpt_path ${CKPT_PATH} \ --output_format html markdown ``` - Visualization Results - The input data are sampled from SciHub domain. ![](docs/demo_1.png) ![](docs/demo_2.png) ## Acknowledgements - [DocGenome](https://github.com/UniModal4Reasoning/DocGenome). An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Models. - [ChartVLM](https://github.com/UniModal4Reasoning/ChartVLM). A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning. - [Pix2Struct](https://github.com/google-research/pix2struct). Screenshot Parsing as Pretraining for Visual Language Understanding. - [UniMERNet](https://github.com/opendatalab/UniMERNet). A Universal Network for Real-World Mathematical Expression Recognition. - [Donut](https://huggingface.co/naver-clova-ix/donut-base). The UniMERNet's Transformer Encoder-Decoder are referenced from Donut. - [Nougat](https://github.com/facebookresearch/nougat). The tokenizer uses Nougat. - [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM). Model inference acceleration uses TensorRT-LLM. ## License StructEqTable is released under the [Apache License 2.0](LICENSE) ## Citation If you find our models / code / papers useful in your research, please consider giving ⭐ and citations 📝, thx :) ```bibtex @article{xia2024docgenome, title={DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models}, author={Xia, Renqiu and Mao, Song and Yan, Xiangchao and Zhou, Hongbin and Zhang, Bo and Peng, Haoyang and Pi, Jiahao and Fu, Daocheng and Wu, Wenjie and Ye, Hancheng and others}, journal={arXiv preprint arXiv:2406.11633}, year={2024} } ``` ## Contact Us If you encounter any issues or have questions, please feel free to contact us via zhouhongbin@pjlab.org.cn.
lamm-mit/mistralai-Mistral-7B-v0.3	lamm-mit	2024-08-30T14:51:53Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-12T22:41:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BeaverAI/Theia-21B-v2d-GGUF	BeaverAI	2024-08-30T14:46:44Z	11	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-08-30T11:45:26Z	chatml seems broken. text completion looks fine
AshtonLKY/Whisper_ASR_ATC_v6	AshtonLKY	2024-08-30T14:45:46Z	5	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "asr-fyp", "generated_from_trainer", "en", "dataset:AshtonLKY/Whisper_ASR_ATC", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-08-23T10:53:01Z	--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-small tags: - asr-fyp - generated_from_trainer datasets: - AshtonLKY/Whisper_ASR_ATC model-index: - name: Whisper_ASR_ATC results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper_ASR_ATC This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AshtonLKY/augmented_audio dataset. It achieves the following results on the evaluation set: - eval_loss: 0.091453 - eval_wer: 6.122661 - step: 11000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 25000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
CharlesLi/OpenELM-1_1B-DPO-full-2-5	CharlesLi	2024-08-30T14:39:22Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "openelm", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2024-08-30T08:25:51Z	--- library_name: transformers tags: - trl - dpo - generated_from_trainer model-index: - name: OpenELM-1_1B-DPO-full-2-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # OpenELM-1_1B-DPO-full-2-5 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1888 - Rewards/chosen: -13.5625 - Rewards/rejected: -17.0 - Rewards/accuracies: 0.7070 - Rewards/margins: 3.4062 - Logps/rejected: -1984.0 - Logps/chosen: -1672.0 - Logits/rejected: 6.2188 - Logits/chosen: 4.5312 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.615 \| 0.1047 \| 100 \| 0.6275 \| -0.7383 \| -0.9961 \| 0.6719 \| 0.2578 \| -386.0 \| -388.0 \| -9.625 \| -9.8125 \| \| 0.5897 \| 0.2093 \| 200 \| 0.6029 \| -1.6641 \| -2.0938 \| 0.6934 \| 0.4336 \| -496.0 \| -480.0 \| -9.375 \| -9.75 \| \| 0.6457 \| 0.3140 \| 300 \| 0.5886 \| -1.3828 \| -1.8281 \| 0.6895 \| 0.4473 \| -470.0 \| -454.0 \| -13.5625 \| -13.75 \| \| 0.6271 \| 0.4186 \| 400 \| 0.5936 \| -1.7031 \| -2.25 \| 0.6992 \| 0.5430 \| -510.0 \| -484.0 \| -8.4375 \| -8.8125 \| \| 0.5746 \| 0.5233 \| 500 \| 0.5886 \| -2.0156 \| -2.5625 \| 0.6816 \| 0.5430 \| -540.0 \| -516.0 \| -6.6562 \| -7.4062 \| \| 0.5484 \| 0.6279 \| 600 \| 0.5710 \| -3.9531 \| -4.6875 \| 0.6973 \| 0.7422 \| -756.0 \| -708.0 \| -5.75 \| -6.4375 \| \| 0.5747 \| 0.7326 \| 700 \| 0.5820 \| -2.75 \| -3.4844 \| 0.6953 \| 0.7227 \| -632.0 \| -592.0 \| -6.5312 \| -7.5938 \| \| 0.5591 \| 0.8373 \| 800 \| 0.5662 \| -2.8594 \| -3.5156 \| 0.7090 \| 0.6523 \| -636.0 \| -600.0 \| -3.375 \| -4.7812 \| \| 0.5892 \| 0.9419 \| 900 \| 0.5821 \| -2.625 \| -3.2344 \| 0.7012 \| 0.5977 \| -608.0 \| -576.0 \| -4.8438 \| -6.125 \| \| 0.261 \| 1.0466 \| 1000 \| 0.5852 \| -3.9375 \| -4.9688 \| 0.7324 \| 1.0078 \| -780.0 \| -708.0 \| -0.8672 \| -2.1094 \| \| 0.2407 \| 1.1512 \| 1100 \| 0.5943 \| -4.0625 \| -5.0 \| 0.6895 \| 0.9336 \| -784.0 \| -720.0 \| -0.3672 \| -1.9688 \| \| 0.2348 \| 1.2559 \| 1200 \| 0.6151 \| -4.9375 \| -5.9688 \| 0.6777 \| 1.0547 \| -884.0 \| -808.0 \| 1.5312 \| 0.2227 \| \| 0.257 \| 1.3605 \| 1300 \| 0.6005 \| -4.4688 \| -5.4688 \| 0.6973 \| 0.9883 \| -832.0 \| -760.0 \| 1.5312 \| -0.1445 \| \| 0.2416 \| 1.4652 \| 1400 \| 0.6023 \| -5.1875 \| -6.125 \| 0.6855 \| 0.9258 \| -900.0 \| -836.0 \| 1.9141 \| 0.2715 \| \| 0.215 \| 1.5699 \| 1500 \| 0.6062 \| -5.5938 \| -6.7188 \| 0.6934 \| 1.1328 \| -960.0 \| -872.0 \| 1.9219 \| 0.2637 \| \| 0.2534 \| 1.6745 \| 1600 \| 0.6013 \| -4.6562 \| -5.7188 \| 0.7129 \| 1.0391 \| -856.0 \| -780.0 \| 2.7969 \| 1.1406 \| \| 0.2463 \| 1.7792 \| 1700 \| 0.6173 \| -5.2812 \| -6.4375 \| 0.6914 \| 1.1484 \| -928.0 \| -844.0 \| 1.9688 \| 0.0977 \| \| 0.23 \| 1.8838 \| 1800 \| 0.6153 \| -5.8438 \| -7.0625 \| 0.7090 \| 1.2266 \| -992.0 \| -896.0 \| 2.9062 \| 1.0156 \| \| 0.2092 \| 1.9885 \| 1900 \| 0.6082 \| -5.5625 \| -6.7188 \| 0.7051 \| 1.1641 \| -956.0 \| -868.0 \| 2.9375 \| 1.0781 \| \| 0.0271 \| 2.0931 \| 2000 \| 0.7202 \| -7.625 \| -9.375 \| 0.7207 \| 1.7734 \| -1224.0 \| -1080.0 \| 3.5781 \| 1.8516 \| \| 0.0367 \| 2.1978 \| 2100 \| 0.8323 \| -9.3125 \| -11.5 \| 0.7168 \| 2.1406 \| -1432.0 \| -1248.0 \| 4.7188 \| 2.9219 \| \| 0.0443 \| 2.3025 \| 2200 \| 0.7840 \| -8.0 \| -10.0625 \| 0.7324 \| 2.0625 \| -1296.0 \| -1112.0 \| 3.9375 \| 2.0312 \| \| 0.0302 \| 2.4071 \| 2300 \| 0.7981 \| -8.375 \| -10.375 \| 0.7070 \| 2.0 \| -1328.0 \| -1152.0 \| 4.625 \| 2.8125 \| \| 0.031 \| 2.5118 \| 2400 \| 0.7786 \| -7.9062 \| -9.875 \| 0.7129 \| 1.9922 \| -1280.0 \| -1104.0 \| 4.875 \| 3.0156 \| \| 0.018 \| 2.6164 \| 2500 \| 0.8584 \| -9.9375 \| -12.125 \| 0.6914 \| 2.2031 \| -1496.0 \| -1312.0 \| 5.4688 \| 3.6719 \| \| 0.0248 \| 2.7211 \| 2600 \| 0.8079 \| -8.625 \| -10.6875 \| 0.7012 \| 2.0469 \| -1352.0 \| -1176.0 \| 5.0312 \| 3.0938 \| \| 0.0263 \| 2.8257 \| 2700 \| 0.8371 \| -9.3125 \| -11.375 \| 0.6914 \| 2.0156 \| -1424.0 \| -1248.0 \| 5.2812 \| 3.4531 \| \| 0.033 \| 2.9304 \| 2800 \| 0.8799 \| -9.8125 \| -12.1875 \| 0.7207 \| 2.4062 \| -1504.0 \| -1296.0 \| 5.2188 \| 3.3281 \| \| 0.0118 \| 3.0351 \| 2900 \| 0.8372 \| -9.625 \| -11.875 \| 0.7246 \| 2.2969 \| -1472.0 \| -1280.0 \| 5.6562 \| 3.7812 \| \| 0.0094 \| 3.1397 \| 3000 \| 0.9555 \| -11.0 \| -13.6875 \| 0.7090 \| 2.6875 \| -1656.0 \| -1416.0 \| 6.0938 \| 4.3125 \| \| 0.0073 \| 3.2444 \| 3100 \| 0.9687 \| -11.375 \| -14.125 \| 0.7129 \| 2.7344 \| -1696.0 \| -1456.0 \| 5.9062 \| 4.1875 \| \| 0.0104 \| 3.3490 \| 3200 \| 1.0111 \| -11.75 \| -14.5625 \| 0.7070 \| 2.8438 \| -1744.0 \| -1488.0 \| 6.1875 \| 4.4688 \| \| 0.01 \| 3.4537 \| 3300 \| 1.0564 \| -12.125 \| -15.0625 \| 0.7051 \| 2.9375 \| -1792.0 \| -1528.0 \| 5.9375 \| 4.2188 \| \| 0.0089 \| 3.5583 \| 3400 \| 0.9822 \| -11.375 \| -14.0625 \| 0.7051 \| 2.7031 \| -1696.0 \| -1448.0 \| 5.875 \| 4.2188 \| \| 0.0106 \| 3.6630 \| 3500 \| 1.0239 \| -11.5625 \| -14.375 \| 0.7070 \| 2.8125 \| -1720.0 \| -1472.0 \| 5.9688 \| 4.25 \| \| 0.0099 \| 3.7677 \| 3600 \| 1.0668 \| -11.9375 \| -14.9375 \| 0.6973 \| 3.0 \| -1784.0 \| -1512.0 \| 6.125 \| 4.375 \| \| 0.0066 \| 3.8723 \| 3700 \| 1.0938 \| -12.75 \| -15.875 \| 0.7070 \| 3.1406 \| -1872.0 \| -1592.0 \| 6.2188 \| 4.5312 \| \| 0.0081 \| 3.9770 \| 3800 \| 1.0255 \| -11.6875 \| -14.5625 \| 0.7129 \| 2.8906 \| -1744.0 \| -1488.0 \| 5.9688 \| 4.2812 \| \| 0.0035 \| 4.0816 \| 3900 \| 1.1112 \| -12.75 \| -15.875 \| 0.7031 \| 3.1406 \| -1872.0 \| -1592.0 \| 6.2188 \| 4.5312 \| \| 0.002 \| 4.1863 \| 4000 \| 1.1127 \| -12.8125 \| -16.0 \| 0.7051 \| 3.1562 \| -1888.0 \| -1600.0 \| 6.1875 \| 4.5 \| \| 0.0036 \| 4.2909 \| 4100 \| 1.1368 \| -13.0 \| -16.25 \| 0.7031 \| 3.25 \| -1912.0 \| -1616.0 \| 6.1875 \| 4.4688 \| \| 0.0069 \| 4.3956 \| 4200 \| 1.1589 \| -13.25 \| -16.625 \| 0.7070 \| 3.3125 \| -1944.0 \| -1640.0 \| 6.2188 \| 4.5312 \| \| 0.0043 \| 4.5003 \| 4300 \| 1.1756 \| -13.4375 \| -16.75 \| 0.7031 \| 3.375 \| -1968.0 \| -1656.0 \| 6.2188 \| 4.5312 \| \| 0.0091 \| 4.6049 \| 4400 \| 1.1842 \| -13.5 \| -16.875 \| 0.7031 \| 3.3906 \| -1976.0 \| -1664.0 \| 6.2188 \| 4.5312 \| \| 0.0058 \| 4.7096 \| 4500 \| 1.1865 \| -13.5 \| -16.875 \| 0.7051 \| 3.3906 \| -1976.0 \| -1664.0 \| 6.2188 \| 4.5312 \| \| 0.0034 \| 4.8142 \| 4600 \| 1.1880 \| -13.5625 \| -17.0 \| 0.7051 \| 3.3906 \| -1984.0 \| -1672.0 \| 6.2188 \| 4.5312 \| \| 0.006 \| 4.9189 \| 4700 \| 1.1888 \| -13.5625 \| -17.0 \| 0.7070 \| 3.4062 \| -1984.0 \| -1672.0 \| 6.2188 \| 4.5312 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
John6666/pony-kanoato-symphony-final-sdxl	John6666	2024-08-30T14:34:56Z	116	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "style", "semi-realistic", "illustration", "paintings", "pony", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-08-30T14:30:14Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - style - semi-realistic - illustration - paintings - pony --- Original model is [here](https://civitai.com/models/645642?modelVersionId=722292). This model created by [Kanoato](https://civitai.com/user/Kanoato).
QuantFactory/Bielik-11B-v2.2-Instruct-GGUF	QuantFactory	2024-08-30T14:26:16Z	18	1	transformers	[ "transformers", "gguf", "finetuned", "pl", "arxiv:2005.01643", "arxiv:2309.11235", "arxiv:2006.09092", "arxiv:2402.13228", "base_model:speakleash/Bielik-11B-v2", "base_model:quantized:speakleash/Bielik-11B-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T13:19:21Z	--- license: apache-2.0 base_model: speakleash/Bielik-11B-v2 language: - pl library_name: transformers tags: - finetuned inference: parameters: temperature: 0.2 widget: - messages: - role: user content: Co przedstawia polskie godło? extra_gated_description: If you want to learn more about how you can use the model, please refer to our <a href="https://bielik.ai/terms/">Terms of Use</a>. --- ![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ) # QuantFactory/Bielik-11B-v2.2-Instruct-GGUF This is quantized version of [speakleash/Bielik-11B-v2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct) created using llama.cpp # Original Model Card <p align="center"> <img src="https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct/raw/main/speakleash_cyfronet.png"> </p> # Bielik-11B-v2.2-Instruct Bielik-11B-v2.2-Instruct is a generative text model featuring 11 billion parameters. It is an instruct fine-tuned version of the [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2). Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH. Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH. The creation and training of the Bielik-11B-v2.2-Instruct was propelled by the support of computational grant number PLG/2024/016951, conducted on the Athena and Helios supercomputer, enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision. 🎥 Demo: https://chat.bielik.ai 🗣️ Chat Arena<span style="color:red;"></span>: https://arena.speakleash.org.pl/ <span style="color:red;"></span>Chat Arena is a platform for testing and comparing different AI language models, allowing users to evaluate their performance and quality. ## Model The [SpeakLeash](https://speakleash.org/) team is working on their own set of instructions in Polish, which is continuously being expanded and refined by annotators. A portion of these instructions, which had been manually verified and corrected, has been utilized for training purposes. Moreover, due to the limited availability of high-quality instructions in Polish, synthetic instructions were generated with [Mixtral 8x22B](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) and used in training. The dataset used for training comprised over 20 million instructions, consisting of more than 10 billion tokens. The instructions varied in quality, leading to a deterioration in the model’s performance. To counteract this while still allowing ourselves to utilize the aforementioned datasets, several improvements were introduced: * Weighted tokens level loss - a strategy inspired by [offline reinforcement learning](https://arxiv.org/abs/2005.01643) and [C-RLFT](https://arxiv.org/abs/2309.11235) * Adaptive learning rate inspired by the study on [Learning Rates as a Function of Batch Size](https://arxiv.org/abs/2006.09092) * Masked prompt tokens To align the model with user preferences we tested many different techniques: DPO, PPO, KTO, SiMPO. Finally the [DPO-Positive](https://arxiv.org/abs/2402.13228) method was employed, utilizing both generated and manually corrected examples, which were scored by a metamodel. A dataset comprising over 66,000 examples of varying lengths to address different aspects of response style. It was filtered and evaluated by the reward model to select instructions with the right level of difference between chosen and rejected. The novelty introduced in DPO-P was multi-turn conversations introduction. Bielik-11B-v2.2-Instruct has been trained with the use of an original open source framework called [ALLaMo](https://github.com/chrisociepa/allamo) implemented by [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/). This framework allows users to train language models with architecture similar to LLaMA and Mistral in fast and efficient way. ### Model description: * Developed by: [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/) * Language: Polish * Model type: causal decoder-only * Finetuned from: [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2) * License: Apache 2.0 and [Terms of Use](https://bielik.ai/terms/) * Model ref: speakleash:0deb975c3780df3a3ae98b619185faa1 ### Quantized models: We know that some people want to explore smaller models or don't have the resources to run a full model. Therefore, we have prepared quantized versions of the Bielik-11B-v2.2-Instruct model in separate repositories: - [GGUF - Q4_K_M, Q5_K_M, Q6_K, Q8_0](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-GGUF) - [GPTQ - 4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-GPTQ) - HQQ - [4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-HQQ-4bit-128gs), [8bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-HQQ-8bit-128gs) - [AWQ - 4bit GEMM](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-AWQ) - EXL2 - [4.5bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-EXL2-4.5bit), [6.5bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-EXL2-6.5bit) - MLX - [4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-MLX-4bit), [8bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-MLX-8bit) - Quanto - [4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-Quanto-4bit), [8bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-Quanto-8bit) - [FP8](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-FP8) (vLLM, SGLang - Ada Lovelace, Hopper optimized) - [INT8 W8A8](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-W8A8) (vLLM INT8 quantization Weights=8bits and Activations=8bits) - [GGUF - experimental - IQ imatrix IQ2_XXS, IQ3_XXS, IQ4_XS and calibrated Q4_K_M, Q5_K_M, Q6_K, Q8_0](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-GGUF-IQ-Imatrix) Please note that quantized models may offer lower quality of generated answers compared to full sized variatns. ### Chat template Bielik-11B-v2.2-Instruct uses [ChatML](https://github.com/cognitivecomputations/OpenChatML) as the prompt format. E.g. ``` prompt = "<s><\|im_start\|> user\nJakie mamy pory roku?<\|im_end\|> \n<\|im_start\|> assistant\n" completion = "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.<\|im_end\|> \n" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model_name = "speakleash/Bielik-11B-v2.2-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16) messages = [ {"role": "system", "content": "Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim."}, {"role": "user", "content": "Jakie mamy pory roku w Polsce?"}, {"role": "assistant", "content": "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima."}, {"role": "user", "content": "Która jest najcieplejsza?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = input_ids.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` Fully formated input conversation by apply_chat_template from previous example: ``` <s><\|im_start\|> system Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim.<\|im_end\|> <\|im_start\|> user Jakie mamy pory roku w Polsce?<\|im_end\|> <\|im_start\|> assistant W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.<\|im_end\|> <\|im_start\|> user Która jest najcieplejsza?<\|im_end\|> ``` ## Evaluation Bielik-11B-v2.2-Instruct has been evaluated on several benchmarks to assess its performance across various tasks and languages. These benchmarks include: 1. Open PL LLM Leaderboard 2. Open LLM Leaderboard 3. Polish MT-Bench 4. Polish EQ-Bench (Emotional Intelligence Benchmark) 5. MixEval The following sections provide detailed results for each of these benchmarks, demonstrating the model's capabilities in both Polish and English language tasks. ### Open PL LLM Leaderboard Models have been evaluated on [Open PL LLM Leaderboard](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) 5-shot. The benchmark evaluates models in NLP tasks like sentiment analysis, categorization, text classification but does not test chatting skills. Average column is an average score among all tasks normalized by baseline scores. \| Model \| Parameters (B)\| Average \| \|---------------------------------\|------------\|---------\| \| Meta-Llama-3.1-405B-Instruct-FP8,API \| 405 \| 69.44 \| \| Mistral-Large-Instruct-2407 \| 123 \| 69.11 \| \| Qwen2-72B-Instruct \| 72 \| 65.87 \| \| Bielik-11B-v2.2-Instruct \| 11 \| 65.57 \| \| Meta-Llama-3.1-70B-Instruct \| 70 \| 65.49 \| \| Bielik-11B-v2.1-Instruct \| 11 \| 65.45 \| \| Mixtral-8x22B-Instruct-v0.1 \| 141 \| 65.23 \| \| Bielik-11B-v2.0-Instruct \| 11 \| 64.98 \| \| Meta-Llama-3-70B-Instruct \| 70 \| 64.45 \| \| Athene-70B \| 70 \| 63.65 \| \| WizardLM-2-8x22B \| 141 \| 62.35 \| \| Qwen1.5-72B-Chat \| 72 \| 58.67 \| \| Qwen2-57B-A14B-Instruct \| 57 \| 56.89 \| \| glm-4-9b-chat \| 9 \| 56.61 \| \| aya-23-35B \| 35 \| 56.37 \| \| Phi-3.5-MoE-instruct \| 41.9 \| 56.34 \| \| openchat-3.5-0106-gemma \| 7 \| 55.69 \| \| Mistral-Nemo-Instruct-2407 \| 12 \| 55.27 \| \| SOLAR-10.7B-Instruct-v1.0 \| 10.7 \| 55.24 \| \| Mixtral-8x7B-Instruct-v0.1 \| 46.7 \| 55.07 \| \| Bielik-7B-Instruct-v0.1 \| 7 \| 44.70 \| \| trurl-2-13b-academic \| 13 \| 36.28 \| \| trurl-2-7b \| 7 \| 26.93 \| The results from the Open PL LLM Leaderboard demonstrate the exceptional performance of Bielik-11B-v2.2-Instruct: 1. Superior performance in its class: Bielik-11B-v2.2-Instruct outperforms all other models with less than 70B parameters. This is a significant achievement, showcasing its efficiency and effectiveness despite having fewer parameters than many competitors. 2. Competitive with larger models: with a score of 65.57, Bielik-11B-v2.2-Instruct performs on par with models in the 70B parameter range. This indicates that it achieves comparable results to much larger models, demonstrating its advanced architecture and training methodology. 3. Substantial improvement over previous version: the model shows a marked improvement over its predecessor, Bielik-7B-Instruct-v0.1, which scored 43.64. This leap in performance highlights the successful enhancements and optimizations implemented in this newer version. 4. Leading position for Polish language models: in the context of Polish language models, Bielik-11B-v2.2-Instruct stands out as a leader. There are no other competitive models specifically tailored for the Polish language that match its performance, making it a crucial resource for Polish NLP tasks. These results underscore Bielik-11B-v2.2-Instruct's position as a state-of-the-art model for Polish language processing, offering high performance with relatively modest computational requirements. #### Open PL LLM Leaderboard - Generative Tasks Performance This section presents a focused comparison of generative Polish language task performance between Bielik models and GPT-3.5. The evaluation is limited to generative tasks due to the constraints of assessing OpenAI models. The comprehensive nature and associated costs of the benchmark explain the limited number of models evaluated. \| Model \| Parameters (B) \| Average g \| \|-------------------------------\|----------------\|---------------\| \| Bielik-11B-v2.1-Instruct \| 11 \| 66.58 \| \| Bielik-11B-v2.2-Instruct \| 11 \| 66.11 \| \| Bielik-11B-v2.0-Instruct \| 11 \| 65.58 \| \| gpt-3.5-turbo-instruct \| Unknown \| 55.65 \| The performance variation among Bielik versions is minimal, indicating consistent quality across iterations. Bielik-11B-v2.2-Instruct demonstrates an impressive 18.8% performance advantage over GPT-3.5. ### Open LLM Leaderboard The Open LLM Leaderboard evaluates models on various English language tasks, providing insights into the model's performance across different linguistic challenges. \| Model \| AVG \| arc_challenge \| hellaswag \| truthfulqa_mc2 \| mmlu \| winogrande \| gsm8k \| \|--------------------------\|-------\|---------------\|-----------\|----------------\|-------\|------------\|-------\| \| Bielik-11B-v2.2-Instruct \| 69.86 \| 59.90 \| 80.16 \| 58.34 \| 64.34 \| 75.30 \| 81.12 \| \| Bielik-11B-v2.1-Instruct \| 69.82 \| 59.56 \| 80.20 \| 59.35 \| 64.18 \| 75.06 \| 80.59 \| \| Bielik-11B-v2.0-Instruct \| 68.04 \| 58.62 \| 78.65 \| 54.65 \| 63.71 \| 76.32 \| 76.27 \| \| Bielik-11B-v2 \| 65.87 \| 60.58 \| 79.84 \| 46.13 \| 63.06 \| 77.82 \| 67.78 \| \| Mistral-7B-Instruct-v0.2 \| 65.71 \| 63.14 \| 84.88 \| 68.26 \| 60.78 \| 77.19 \| 40.03 \| \| Bielik-7B-Instruct-v0.1 \| 51.26 \| 47.53 \| 68.91 \| 49.47 \| 46.18 \| 65.51 \| 29.95 \| Bielik-11B-v2.2-Instruct shows impressive performance on English language tasks: 1. Significant improvement over its base model (4-point increase). 2. Substantial 18-point improvement over Bielik-7B-Instruct-v0.1. These results demonstrate Bielik-11B-v2.2-Instruct's versatility in both Polish and English, highlighting the effectiveness of its instruction tuning process. ### Polish MT-Bench The Bielik-11B-v.2.2-Instruct (16 bit) model was also evaluated using the MT-Bench benchmark. The quality of the model was evaluated using the English version (original version without modifications) and the Polish version created by Speakleash (tasks and evaluation in Polish, the content of the tasks was also changed to take into account the context of the Polish language). #### MT-Bench English \| Model \| Score \| \|-----------------\|----------\| \| Bielik-11B-v2.1 \| 8.537500 \| \| Bielik-11B-v2.2 \| 8.390625 \| \| Bielik-11B-v2.0 \| 8.159375 \| #### MT-Bench Polish \| Model \| Parameters (B) \| Score \| \|-------------------------------------\|----------------\|----------\| \| Qwen2-72B-Instruct \| 72 \| 8.775000 \| \| Mistral-Large-Instruct-2407 (123B) \| 123 \| 8.662500 \| \| gemma-2-27b-it \| 27 \| 8.618750 \| \| Mixtral-8x22b \| 141 \| 8.231250 \| \| Meta-Llama-3.1-405B-Instruct \| 405 \| 8.168750 \| \| Meta-Llama-3.1-70B-Instruct \| 70 \| 8.150000 \| \| Bielik-11B-v2.2-Instruct \| 11 \| 8.115625 \| \| Bielik-11B-v2.1-Instruct \| 11 \| 7.996875 \| \| gpt-3.5-turbo \| Unknown \| 7.868750 \| \| Mixtral-8x7b \| 46.7 \| 7.637500 \| \| Bielik-11B-v2.0-Instruct \| 11 \| 7.562500 \| \| Mistral-Nemo-Instruct-2407 \| 12 \| 7.368750 \| \| openchat-3.5-0106-gemma \| 7 \| 6.812500 \| \| Mistral-7B-Instruct-v0.2 \| 7 \| 6.556250 \| \| Meta-Llama-3.1-8B-Instruct \| 8 \| 6.556250 \| \| Bielik-7B-Instruct-v0.1 \| 7 \| 6.081250 \| \| Mistral-7B-Instruct-v0.3 \| 7 \| 5.818750 \| \| Polka-Mistral-7B-SFT \| 7 \| 4.518750 \| \| trurl-2-7b \| 7 \| 2.762500 \| Key observations on Bielik-11B-v2.2 performance: 1. Strong performance among mid-sized models: Bielik-11B-v2.2-Instruct scored 8.115625, placing it ahead of several well-known models like GPT-3.5-turbo (7.868750) and Mixtral-8x7b (7.637500). This indicates that Bielik-11B-v2.2 is competitive among mid-sized models, particularly those in the 11B-70B parameter range. 2. Competitive against larger models: Bielik-11B-v2.2-Instruct performs close to Meta-Llama-3.1-70B-Instruct (8.150000), Meta-Llama-3.1-405B-Instruct (8.168750) and even Mixtral-8x22b (8.231250), which have significantly more parameters. This efficiency in performance relative to size could make it an attractive option for tasks where resource constraints are a consideration. Bielik 100% generated answers in Polish, while other models (not typically trained for Polish) can answer Polish questions in English. 3. Significant improvement over previous versions: compared to its predecessor, Bielik-7B-Instruct-v0.1, which scored 6.081250, the Bielik-11B-v2.2-Instruct shows a significant improvement. The score increased by more than 2 points, highlighting substantial advancements in model quality, optimization and training methodology. For more information - answers to test tasks and values in each category, visit the [MT-Bench PL](https://huggingface.co/spaces/speakleash/mt-bench-pl) website. ### Polish EQ-Bench [Polish Emotional Intelligence Benchmark for LLMs](https://huggingface.co/spaces/speakleash/polish_eq-bench) \| Model \| Parameters (B) \| Score \| \|-------------------------------\|--------\|-------\| \| Mistral-Large-Instruct-2407 \| 123 \| 78.07 \| \| Meta-Llama-3.1-405B-Instruct-FP8 \| 405 \| 77.23 \| \| gpt-4o-2024-08-06 \| ? \| 75.15 \| \| gpt-4-turbo-2024-04-09 \| ? \| 74.59 \| \| Meta-Llama-3.1-70B-Instruct \| 70 \| 72.53 \| \| Qwen2-72B-Instruct \| 72 \| 71.23 \| \| Meta-Llama-3-70B-Instruct \| 70 \| 71.21 \| \| gpt-4o-mini-2024-07-18 \| ? \| 71.15 \| \| WizardLM-2-8x22B \| 141 \| 69.56 \| \| Bielik-11B-v2.2-Instruct \| 11 \| 69.05 \| \| Bielik-11B-v2.0-Instruct \| 11 \| 68.24 \| \| Qwen1.5-72B-Chat \| 72 \| 68.03 \| \| Mixtral-8x22B-Instruct-v0.1 \| 141 \| 67.63 \| \| Bielik-11B-v2.1-Instruct \| 11 \| 60.07 \| \| Qwen1.5-32B-Chat \| 32 \| 59.63 \| \| openchat-3.5-0106-gemma \| 7 \| 59.58 \| \| aya-23-35B \| 35 \| 58.41 \| \| gpt-3.5-turbo \| ? \| 57.7 \| \| Qwen2-57B-A14B-Instruct \| 57 \| 57.64 \| \| Mixtral-8x7B-Instruct-v0.1 \| 47 \| 57.61 \| \| SOLAR-10.7B-Instruct-v1.0 \| 10.7 \| 55.21 \| \| Mistral-7B-Instruct-v0.2 \| 7 \| 47.02 \| The results show that Bielik-11B-v2.2-Instruct is the best performing model among those with less than 70B parameters. With a score of 69.05, it outperforms larger models like Qwen1.5-72B-Chat and Mixtral-8x22B-Instruct-v0.1, demonstrating its exceptional efficiency and effectiveness despite its smaller parameter count. ### MixEval MixEval is a ground-truth-based English benchmark designed to evaluate Large Language Models (LLMs) efficiently and effectively. Key features of MixEval include: 1. Derived from off-the-shelf benchmark mixtures 2. Highly capable model ranking with a 0.96 correlation to Chatbot Arena 3. Local and quick execution, requiring only 6% of the time and cost compared to running MMLU This benchmark provides a robust and time-efficient method for assessing LLM performance, making it a valuable tool for ongoing model evaluation and comparison. \| Model \| MixEval \| MixEval-Hard \| \|-------------------------------\|---------\|--------------\| \| Bielik-11B-v2.1-Instruct \| 74.55 \| 45.00 \| \| Bielik-11B-v2.2-Instruct \| 72.35 \| 39.65 \| \| Bielik-11B-v2.0-Instruct \| 72.10 \| 40.20 \| \| Mistral-7B-Instruct-v0.2 \| 70.00 \| 36.20 \| The results show that Bielik-11B-v2.2-Instruct performs well on the MixEval benchmark, achieving a score of 72.35 on the standard MixEval and 39.65 on MixEval-Hard. Notably, Bielik-11B-v2.2-Instruct significantly outperforms Mistral-7B-Instruct-v0.2 on both metrics, demonstrating its improved capabilities despite being based on a similar architecture. ### Chat Arena PL Chat Arena PL is a human-evaluated benchmark that provides a direct comparison of model performance through head-to-head battles. Unlike the automated benchmarks mentioned above, this evaluation relies on human judgment to assess the quality and effectiveness of model responses. The results offer valuable insights into how different models perform in real-world, conversational scenarios as perceived by human evaluators. Results accessed on 2024-08-26. \| # \| Model \| Battles \| Won \| Lost \| Draws \| Win % \| ELO \| \|---\|-------\|-------\|---------\|-----------\|--------\|-------------\|-----\| \| 1 \| Bielik-11B-v2.2-Instruct \| 92 \| 72 \| 14 \| 6 \| 83.72% \| 1234 \| \| 2 \| Bielik-11B-v2.1-Instruct \| 240 \| 171 \| 50 \| 19 \| 77.38% \| 1174 \| \| 3 \| gpt-4o-mini \| 639 \| 402 \| 117 \| 120 \| 77.46% \| 1141 \| \| 4 \| Mistral Large 2 (2024-07) \| 324 \| 188 \| 69 \| 67 \| 73.15% \| 1125 \| \| 5 \| Llama-3.1-405B \| 548 \| 297 \| 144 \| 107 \| 67.35% \| 1090 \| \| 6 \| Bielik-11B-v2.0-Instruct \| 1289 \| 695 \| 352 \| 242 \| 66.38% \| 1059 \| \| 7 \| Llama-3.1-70B \| 498 \| 221 \| 187 \| 90 \| 54.17% \| 1033 \| \| 8 \| Bielik-1-7B \| 2041 \| 1029 \| 638 \| 374 \| 61.73% \| 1020 \| \| 9 \| Mixtral-8x22B-v0.1 \| 432 \| 166 \| 167 \| 99 \| 49.85% \| 1018 \| \| 10 \| Qwen2-72B \| 451 \| 179 \| 177 \| 95 \| 50.28% \| 1011 \| \| 11 \| gpt-3.5-turbo \| 2186 \| 1007 \| 731 \| 448 \| 57.94% \| 1008 \| \| 12 \| Llama-3.1-8B \| 440 \| 155 \| 227 \| 58 \| 40.58% \| 975 \| \| 13 \| Mixtral-8x7B-v0.1 \| 1997 \| 794 \| 804 \| 399 \| 49.69% \| 973 \| \| 14 \| Llama-3-70b \| 2008 \| 733 \| 909 \| 366 \| 44.64% \| 956 \| \| 15 \| Mistral Nemo (2024-07) \| 301 \| 84 \| 164 \| 53 \| 33.87% \| 954 \| \| 16 \| Llama-3-8b \| 1911 \| 473 \| 1091 \| 347 \| 30.24% \| 909 \| \| 17 \| gemma-7b-it \| 1928 \| 418 \| 1221 \| 289 \| 25.5% \| 888 \| The results show that Bielik-11B-v2.2-Instruct outperforms all other models in this benchmark, achieving the highest win percentage (83.72%) and ELO score (1234). This impressive performance demonstrates its effectiveness in real-world conversational scenarios, as judged by human evaluators. ## Limitations and Biases Bielik-11B-v2.2-Instruct is a quick demonstration that the base model can be easily fine-tuned to achieve compelling and promising performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community in ways to make the model respect guardrails, allowing for deployment in environments requiring moderated outputs. Bielik-11B-v2.2-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate data. Bielik-11B-v2.2-Instruct was trained on various public datasets. While great efforts have been taken to clear the training data, it is possible that this model can generate lewd, false, biased or otherwise offensive outputs. ## Citation Please cite this model using the following format: ``` @misc{Bielik11Bv2i, title = {Bielik-11B-v2.2-Instruct model card}, author = {Ociepa, Krzysztof and Flis, Łukasz and Kinas, Remigiusz and Gwoździej, Adrian and Wróbel, Krzysztof and {SpeakLeash Team} and {Cyfronet Team}}, year = {2024}, url = {https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct}, note = {Accessed: 2024-08-28}, % change this date urldate = {2024-08-28} % change this date } @unpublished{Bielik11Bv2a, author = {Ociepa, Krzysztof and Flis, Łukasz and Kinas, Remigiusz and Gwoździej, Adrian and Wróbel, Krzysztof}, title = {Bielik: A Family of Large Language Models for the Polish Language - Development, Insights, and Evaluation}, year = {2024}, } ``` ## Responsible for training the model * [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, data preparation, process optimization and oversight of training * [Łukasz Flis](https://www.linkedin.com/in/lukasz-flis-0a39631/)<sup>Cyfronet AGH</sup> - coordinating and supervising the training * [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - conceptualizing and coordinating DPO training, data preparation * [Adrian Gwoździej](https://www.linkedin.com/in/adrgwo/)<sup>SpeakLeash</sup> - data preparation and ensuring data quality * [Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/)<sup>SpeakLeash</sup> - benchmarks The model could not have been created without the commitment and work of the entire SpeakLeash team, whose contribution is invaluable. Thanks to the hard work of many individuals, it was possible to gather a large amount of content in Polish and establish collaboration between the open-science SpeakLeash project and the HPC center: ACK Cyfronet AGH. Individuals who contributed to the creation of the model: [Sebastian Kondracki](https://www.linkedin.com/in/sebastian-kondracki/), [Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/), [Paweł Kiszczak](https://www.linkedin.com/in/paveu-kiszczak/), [Szymon Baczyński](https://www.linkedin.com/in/szymon-baczynski/), [Jacek Chwiła](https://www.linkedin.com/in/jacek-chwila/), [Maria Filipkowska](https://www.linkedin.com/in/maria-filipkowska/), [Jan Maria Kowalski](https://www.linkedin.com/in/janmariakowalski/), [Karol Jezierski](https://www.linkedin.com/in/karol-jezierski/), [Kacper Milan](https://www.linkedin.com/in/kacper-milan/), [Jan Sowa](https://www.linkedin.com/in/janpiotrsowa/), [Len Krawczyk](https://www.linkedin.com/in/magdalena-krawczyk-7810942ab/), [Marta Seidler](https://www.linkedin.com/in/marta-seidler-751102259/), [Agnieszka Ratajska](https://www.linkedin.com/in/agnieszka-ratajska/), [Krzysztof Koziarek](https://www.linkedin.com/in/krzysztofkoziarek/), [Szymon Pepliński](http://linkedin.com/in/szymonpeplinski/), [Filip Bogacz](https://linkedin.com/in/Fibogacci), [Agnieszka Kosiak](https://www.linkedin.com/in/agn-kosiak), [Izabela Babis](https://www.linkedin.com/in/izabela-babis-2274b8105/), [Nina Babis](https://www.linkedin.com/in/nina-babis-00055a140/). Members of the ACK Cyfronet AGH team providing valuable support and expertise: [Szymon Mazurek](https://www.linkedin.com/in/sz-mazurek-ai/), [Marek Magryś](https://www.linkedin.com/in/magrys/), [Mieszko Cholewa ](https://www.linkedin.com/in/mieszko-cholewa-613726301/). ## Contact Us If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.com/invite/TunEeCTw).
Kanon14/whisper-tiny-dv	Kanon14	2024-08-30T14:25:16Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-08-30T13:58:37Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 31.936245572609206 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-dv This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6532 - Wer Ortho: 32.0173 - Wer: 31.9362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.0012 \| 17.8571 \| 500 \| 0.6532 \| 32.0173 \| 31.9362 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
John6666/nsfw-master-flux-lora-merged-with-flux1-dev-fp16-v10-fp8-flux	John6666	2024-08-30T14:21:44Z	1,166	5	diffusers	[ "diffusers", "safetensors", "text-to-image", "Flux", "fp8", "float8_e4m3fn", "realistic", "photorealistic", "en", "license:other", "endpoints_compatible", "diffusers:FluxPipeline", "region:us" ]	text-to-image	2024-08-30T14:06:55Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE. language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - Flux - fp8 - float8_e4m3fn - realistic - photorealistic --- Original model is [here](https://civitai.com/models/701671/nsfw-master-flux-lora-merged-with-flux1-dev-fp16?modelVersionId=785079). This model created by [Defozo](https://civitai.com/user/Defozo). ## Notice This is an experimental conversion in Spaces using a homebrew script. serverless Inference API does not currently support torch float8_e4m3fn, so it does not work. I have not been able to confirm if the conversion is working properly. Please consider this as a test run only.
TonyStarkD99/CLIP-Crop_Disease	TonyStarkD99	2024-08-30T14:21:11Z	98	0	transformers	[ "transformers", "safetensors", "clip", "zero-shot-image-classification", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	zero-shot-image-classification	2024-08-30T13:26:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf	RichardErkhov	2024-08-30T14:20:34Z	5	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-08-30T13:48:08Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) tinyllama_frankenmerge - GGUF - Model creator: https://huggingface.co/bn22/ - Original model: https://huggingface.co/bn22/tinyllama_frankenmerge/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [tinyllama_frankenmerge.Q2_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q2_K.gguf) \| Q2_K \| 0.55GB \| \| [tinyllama_frankenmerge.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ3_XS.gguf) \| IQ3_XS \| 0.61GB \| \| [tinyllama_frankenmerge.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ3_S.gguf) \| IQ3_S \| 0.64GB \| \| [tinyllama_frankenmerge.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K_S.gguf) \| Q3_K_S \| 0.64GB \| \| [tinyllama_frankenmerge.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ3_M.gguf) \| IQ3_M \| 0.67GB \| \| [tinyllama_frankenmerge.Q3_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K.gguf) \| Q3_K \| 0.71GB \| \| [tinyllama_frankenmerge.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K_M.gguf) \| Q3_K_M \| 0.71GB \| \| [tinyllama_frankenmerge.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K_L.gguf) \| Q3_K_L \| 0.77GB \| \| [tinyllama_frankenmerge.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ4_XS.gguf) \| IQ4_XS \| 0.79GB \| \| [tinyllama_frankenmerge.Q4_0.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_0.gguf) \| Q4_0 \| 0.82GB \| \| [tinyllama_frankenmerge.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ4_NL.gguf) \| IQ4_NL \| 0.83GB \| \| [tinyllama_frankenmerge.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_K_S.gguf) \| Q4_K_S \| 0.83GB \| \| [tinyllama_frankenmerge.Q4_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_K.gguf) \| Q4_K \| 0.87GB \| \| [tinyllama_frankenmerge.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_K_M.gguf) \| Q4_K_M \| 0.87GB \| \| [tinyllama_frankenmerge.Q4_1.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_1.gguf) \| Q4_1 \| 0.91GB \| \| [tinyllama_frankenmerge.Q5_0.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_0.gguf) \| Q5_0 \| 1.0GB \| \| [tinyllama_frankenmerge.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_K_S.gguf) \| Q5_K_S \| 1.0GB \| \| [tinyllama_frankenmerge.Q5_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_K.gguf) \| Q5_K \| 1.02GB \| \| [tinyllama_frankenmerge.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_K_M.gguf) \| Q5_K_M \| 1.02GB \| \| [tinyllama_frankenmerge.Q5_1.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_1.gguf) \| Q5_1 \| 1.08GB \| \| [tinyllama_frankenmerge.Q6_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q6_K.gguf) \| Q6_K \| 1.18GB \| \| [tinyllama_frankenmerge.Q8_0.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q8_0.gguf) \| Q8_0 \| 1.53GB \| Original model description: --- license: apache-2.0 tags: - merge - mergekit --- # tinyllama_frankenmerge This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit): * [TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) * [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) ## 🧩 Configuration ```yml slices: - sources: - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T layer_range: [0, 16] - sources: - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T layer_range: [6, 22] merge_method: passthrough dtype: float16 ```
WESTARBJUNIORSUPERO/qametrik_ai_llm_8b	WESTARBJUNIORSUPERO	2024-08-30T14:13:19Z	11	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T14:10:09Z	--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: WESTARBJUNIORSUPERO - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
williamdeli/image_classification	williamdeli	2024-08-30T14:06:50Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-16T18:06:25Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: image_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5747 - Accuracy: 0.883 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.6533 \| 0.992 \| 62 \| 2.4755 \| 0.832 \| \| 1.7798 \| 2.0 \| 125 \| 1.7368 \| 0.866 \| \| 1.5615 \| 2.976 \| 186 \| 1.5850 \| 0.893 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
mradermacher/Athena-gemma-2-9b-it-i1-GGUF	mradermacher	2024-08-30T14:04:08Z	46	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:EpistemeAI/Athena-gemma-2-9b-it", "base_model:quantized:EpistemeAI/Athena-gemma-2-9b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-08-30T12:38:23Z	--- base_model: EpistemeAI/Athena-gemma-2-9b-it language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - gemma2 - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/EpistemeAI/Athena-gemma-2-9b-it <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q2_K.gguf) \| i1-Q2_K \| 3.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 5.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 5.5 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 5.5 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0.gguf) \| i1-Q4_0 \| 5.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 5.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q6_K.gguf) \| i1-Q6_K \| 7.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Mohamed1213/donut-docvqa-finetuned-by-Captos	Mohamed1213	2024-08-30T13:53:23Z	47	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-08-22T11:03:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jvelja/BERT_gemma2b-instrumentalEmergence-strongerOversight_0	jvelja	2024-08-30T13:52:51Z	117	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-08-29T17:00:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
edumorfosis/tokflux	edumorfosis	2024-08-30T13:32:49Z	5	0	null	[ "license:other", "region:us" ]	null	2024-08-30T12:57:54Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
mezzihoussem/CHatbot2epoch	mezzihoussem	2024-08-30T13:25:12Z	15	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-2-7b-chat-bnb-4bit", "base_model:quantized:unsloth/llama-2-7b-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-30T13:15:17Z	--- base_model: unsloth/llama-2-7b-chat-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: mezzihoussem - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-chat-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Downtown-Case/CohereForAI_c4ai-command-r-08-2024-exl2-3.75bpw

Downtown-Case

2024-08-30T22:15:59Z

26

1

transformers

[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]

text-generation

2024-08-30T21:40:34Z

--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox --- # Model Card for C4AI Command R 08-2024 ## Model Summary  C4AI Command R 08-2024 is a research release of a 35 billion parameter highly performant generative model. Command R 08-2024 is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command R 08-2024 has the capability for multilingual generation, trained on 23 languages and evaluated in 10 languages and highly performant RAG capabilities. Developed by: Cohere and [Cohere For AI](https://cohere.for.ai) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: c4ai-command-r-08-2024 - Model Size: 35 billion parameters - Context length: 128K **Try C4AI Command R** If you want to try Command R before downloading the weights, the model is hosted in a hugging face space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command?model=command-r-08-2024). **Usage** Please use `transformers` version 4.39.1 or higher ```python # pip install 'transformers>=4.39.1' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-08-2024 chat template messages = [{"role": "user", "content": "Hello, how are you?"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ## Model Details **Input**: Models input text only. **Output**: Models generate text only. **Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. We use grouped query attention (GQA) to improve inference speed. **Languages covered**: The model has been trained on 23 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian) and evaluated on 10 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese). **Context length**: Command R 08-2024 supports a context length of 128K. ### Tool use & Agent capabilities: Command R 08-2024 has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance. Command R 08-2024’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R 08-2024 may use one of its supplied tools more than once. The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the `directly_answer` tool, but it can be removed or renamed if required. Comprehensive documentation for working with Command R 08-2024's tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). Command R 08-2024 also supports Hugging Face's [tool use API](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) The code snippet below shows a minimal working example on how to render a prompt. <details> <summary>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND] </summary> ```python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # Define tools available for the model to use: tools = [ { "name": "internet_search", "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet", "parameter_definitions": { "query": { "description": "Query to search the internet with", "type": 'str', "required": True } } }, { 'name': "directly_answer", "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history", 'parameter_definitions': {} } ] # render the tool use prompt as a string: tool_use_prompt = tokenizer.apply_tool_use_template( conversation, tools=tools, tokenize=False, add_generation_prompt=True, ) print(tool_use_prompt) ``` </details> <details> <summary>Usage: Rendering prompts with the Tool Use API [CLICK TO EXPAND] </summary> ```python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # Define tools available for the model to use # Type hints and docstrings from Python functions are automatically extracted def internet_search(query: str): """ Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query: Query to search the internet with """ pass def directly_answer(): """ Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass tools = [internet_search, directly_answer] # render the tool use prompt as a string: tool_use_prompt = tokenizer.apply_chat_template( conversation, tools=tools, tokenize=False, add_generation_prompt=True, ) print(tool_use_prompt) ``` </details> <details> <summary>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</summary> ```` <BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling. ## Available Tools Here is a list of tools that you have available to you: ```python def internet_search(query: str) -> List[Dict]: """Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query (str): Query to search the internet with """ pass ``` ```python def directly_answer() -> List[Dict]: """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass ```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example: ```json [ { "tool_name": title of the tool in the specification, "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters } ]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ```` </details> <details> <summary>Example Rendered Tool Use Completion [CLICK TO EXPAND]</summary> ```` Action: ```json [ { "tool_name": "internet_search", "parameters": { "query": "biggest penguin in the world" } } ] ``` ```` </details> ### Grounded Generation and RAG Capabilities: Command R 08-2024 has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG).This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation. Command R 08-2024’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured. By default, Command R 08-2024 will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation. The model is trained with a number of other answering modes, which can be selected by prompt changes. A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens. Comprehensive documentation for working with Command R 08-2024's grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). The code snippet below shows a minimal working example on how to render a prompt. <details> <summary> Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND] </summary> ````python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-08-2024" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # define documents to ground on: documents = [ { "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." }, { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."} ] # render the tool use prompt as a string: grounded_generation_prompt = tokenizer.apply_grounded_generation_template( conversation, documents=documents, citation_mode="accurate", # or "fast" tokenize=False, add_generation_prompt=True, ) print(grounded_generation_prompt) ```` </details> <details> <summary>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</summary> ```` <BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results> Document: 0 title: Tall penguins text: Emperor penguins are the tallest growing up to 122 cm in height. Document: 1 title: Penguin habitats text: Emperor penguins only live in Antarctica. </results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line. Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'. Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'. Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup. Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ```` </details> <details> <summary>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</summary> ```` Relevant Documents: 0,1 Cited Documents: 0,1 Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres. Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0> ```` </details> ### Code Capabilities: Command R 08-2024 has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions. ### Model Card Contact For errors or additional questions about details in this model card, contact [info@for.ai](mailto:info@for.ai). ### Terms of Use: We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try Chat: You can try Command-R chat in the playground [here](https://dashboard.cohere.com/playground/chat).

mradermacher/Einstein-Replete-7B-GGUF

mradermacher

2024-08-30T22:13:14Z

22

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NotASI/Einstein-Replete-7B", "base_model:quantized:NotASI/Einstein-Replete-7B", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-29T08:06:38Z

--- base_model: NotASI/Einstein-Replete-7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/NotASI/Einstein-Replete-7B  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q2_K.gguf) | Q2_K | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ3_XS.gguf) | IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ3_S.gguf) | IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ3_M.gguf) | IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF/resolve/main/Einstein-Replete-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/Einstein-Replete-7B-i1-GGUF

mradermacher

2024-08-30T22:13:10Z

32

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NotASI/Einstein-Replete-7B", "base_model:quantized:NotASI/Einstein-Replete-7B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-08-29T10:07:35Z

--- base_model: NotASI/Einstein-Replete-7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/NotASI/Einstein-Replete-7B  static quants are available at https://huggingface.co/mradermacher/Einstein-Replete-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.5 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.5 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-Replete-7B-i1-GGUF/resolve/main/Einstein-Replete-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mergekit-community/mergekit-ties-svasnwf

mergekit-community

2024-08-30T22:09:46Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Hastagaras/Jamet-8B-L3-MK.V-Blackroot", "base_model:merge:Hastagaras/Jamet-8B-L3-MK.V-Blackroot", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:Vikhrmodels/Vikhr-7B-instruct_0.4", "base_model:merge:Vikhrmodels/Vikhr-7B-instruct_0.4", "base_model:crestf411/L3.1-8B-sunfall-v0.6.1-dpo", "base_model:merge:crestf411/L3.1-8B-sunfall-v0.6.1-dpo", "base_model:maldv/badger-iota-llama-3-8b", "base_model:merge:maldv/badger-iota-llama-3-8b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T22:05:06Z

--- base_model: - Hastagaras/Jamet-8B-L3-MK.V-Blackroot - Vikhrmodels/Vikhr-7B-instruct_0.4 - crestf411/L3.1-8B-sunfall-v0.6.1-dpo - Sao10K/L3-8B-Stheno-v3.2 - maldv/badger-iota-llama-3-8b library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) as a base. ### Models Merged The following models were included in the merge: * [Hastagaras/Jamet-8B-L3-MK.V-Blackroot](https://huggingface.co/Hastagaras/Jamet-8B-L3-MK.V-Blackroot) * [crestf411/L3.1-8B-sunfall-v0.6.1-dpo](https://huggingface.co/crestf411/L3.1-8B-sunfall-v0.6.1-dpo) * [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) * [maldv/badger-iota-llama-3-8b](https://huggingface.co/maldv/badger-iota-llama-3-8b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Vikhrmodels/Vikhr-7B-instruct_0.4 - model: crestf411/L3.1-8B-sunfall-v0.6.1-dpo # Another RP Model trained on... stuff parameters: density: 0.4 weight: 0.25 - model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot # Another RP / Storytelling Model parameters: density: 0.5 weight: 0.3 - model: maldv/badger-iota-llama-3-8b #Megamerge - Helps with General Knowledge parameters: density: 0.6 weight: 0.35 - model: Sao10K/L3-8B-Stheno-v3.2 # This is Stheno v3.2's Initial Name parameters: density: 0.7 weight: 0.4 merge_method: ties base_model: Vikhrmodels/Vikhr-7B-instruct_0.4 parameters: int8_mask: true rescale: true normalize: false dtype: bfloat16 ```

shashank970613/lora-flan-t5-large-chat

shashank970613

2024-08-30T22:08:32Z

105

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-08-30T20:52:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

gglabs/Mistral-Nemo-12B-FC-Chat-0830-2-epoch

gglabs

2024-08-30T22:06:30Z

5

0

transformers

[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T21:31:36Z

--- base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - **Developed by:** gglabs - **License:** apache-2.0 - **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ellarose/test-trainer-alternate

ellarose

2024-08-30T22:06:04Z

5

0

setfit

[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]

text-classification

2024-08-28T21:54:34Z

--- base_model: sentence-transformers/paraphrase-mpnet-base-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: ballades (formes fixes) - text: prison fiction - text: gregorian chants - text: argentina--buenos aires, port of - text: passepieds (music) inference: true model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9555555555555556 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 2 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:-----------|:-----------------------------------------------------------------------------------------------------------------| | subject | <ul><li>'vidourle river (france)'</li><li>'knockout kings 2000 (game)'</li><li>'social practice (art)'</li></ul> | | genre/form | <ul><li>'hadith stories'</li><li>'discographies'</li><li>'dance drama'</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.9556 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("ellarose/test-trainer-alternate") # Run inference preds = model("prison fiction") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 1 | 2.415 | 10 | | Label | Training Sample Count | |:-----------|:----------------------| | subject | 500 | | genre/form | 500 | ### Training Hyperparameters - batch_size: (40, 40) - num_epochs: (10, 10) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:---------:|:-------------:|:---------------:| | 0.0001 | 1 | 0.3379 | - | | 0.0040 | 50 | 0.3311 | - | | 0.0080 | 100 | 0.3642 | - | | 0.0120 | 150 | 0.3077 | - | | 0.0160 | 200 | 0.2542 | - | | 0.0200 | 250 | 0.233 | - | | 0.0240 | 300 | 0.23 | - | | 0.0279 | 350 | 0.2247 | - | | 0.0319 | 400 | 0.2009 | - | | 0.0359 | 450 | 0.2354 | - | | 0.0399 | 500 | 0.1823 | - | | 0.0439 | 550 | 0.2048 | - | | 0.0479 | 600 | 0.1546 | - | | 0.0519 | 650 | 0.1363 | - | | 0.0559 | 700 | 0.1031 | - | | 0.0599 | 750 | 0.0668 | - | | 0.0639 | 800 | 0.1156 | - | | 0.0679 | 850 | 0.0435 | - | | 0.0719 | 900 | 0.0495 | - | | 0.0758 | 950 | 0.046 | - | | 0.0798 | 1000 | 0.0424 | - | | 0.0838 | 1050 | 0.1312 | - | | 0.0878 | 1100 | 0.0246 | - | | 0.0918 | 1150 | 0.0273 | - | | 0.0958 | 1200 | 0.0075 | - | | 0.0998 | 1250 | 0.0203 | - | | 0.1038 | 1300 | 0.0073 | - | | 0.1078 | 1350 | 0.0328 | - | | 0.1118 | 1400 | 0.0274 | - | | 0.1158 | 1450 | 0.0042 | - | | 0.1198 | 1500 | 0.0494 | - | | 0.1238 | 1550 | 0.0413 | - | | 0.1277 | 1600 | 0.0036 | - | | 0.1317 | 1650 | 0.0329 | - | | 0.1357 | 1700 | 0.0168 | - | | 0.1397 | 1750 | 0.0028 | - | | 0.1437 | 1800 | 0.0227 | - | | 0.1477 | 1850 | 0.002 | - | | 0.1517 | 1900 | 0.0121 | - | | 0.1557 | 1950 | 0.0018 | - | | 0.1597 | 2000 | 0.0019 | - | | 0.1637 | 2050 | 0.001 | - | | 0.1677 | 2100 | 0.0009 | - | | 0.1717 | 2150 | 0.0012 | - | | 0.1756 | 2200 | 0.0007 | - | | 0.1796 | 2250 | 0.001 | - | | 0.1836 | 2300 | 0.0008 | - | | 0.1876 | 2350 | 0.0009 | - | | 0.1916 | 2400 | 0.001 | - | | 0.1956 | 2450 | 0.0009 | - | | 0.1996 | 2500 | 0.0247 | - | | 0.2036 | 2550 | 0.0007 | - | | 0.2076 | 2600 | 0.0008 | - | | 0.2116 | 2650 | 0.0008 | - | | 0.2156 | 2700 | 0.0006 | - | | 0.2196 | 2750 | 0.0023 | - | | 0.2236 | 2800 | 0.0007 | - | | 0.2275 | 2850 | 0.0004 | - | | 0.2315 | 2900 | 0.0054 | - | | 0.2355 | 2950 | 0.0007 | - | | 0.2395 | 3000 | 0.0004 | - | | 0.2435 | 3050 | 0.0007 | - | | 0.2475 | 3100 | 0.0244 | - | | 0.2515 | 3150 | 0.0243 | - | | 0.2555 | 3200 | 0.0005 | - | | 0.2595 | 3250 | 0.0004 | - | | 0.2635 | 3300 | 0.0004 | - | | 0.2675 | 3350 | 0.0004 | - | | 0.2715 | 3400 | 0.0004 | - | | 0.2754 | 3450 | 0.0482 | - | | 0.2794 | 3500 | 0.0004 | - | | 0.2834 | 3550 | 0.0005 | - | | 0.2874 | 3600 | 0.0005 | - | | 0.2914 | 3650 | 0.0007 | - | | 0.2954 | 3700 | 0.0063 | - | | 0.2994 | 3750 | 0.0043 | - | | 0.3034 | 3800 | 0.0005 | - | | 0.3074 | 3850 | 0.0366 | - | | 0.3114 | 3900 | 0.0245 | - | | 0.3154 | 3950 | 0.0242 | - | | 0.3194 | 4000 | 0.0003 | - | | 0.3234 | 4050 | 0.0007 | - | | 0.3273 | 4100 | 0.0123 | - | | 0.3313 | 4150 | 0.0004 | - | | 0.3353 | 4200 | 0.0007 | - | | 0.3393 | 4250 | 0.0238 | - | | 0.3433 | 4300 | 0.0002 | - | | 0.3473 | 4350 | 0.0238 | - | | 0.3513 | 4400 | 0.0003 | - | | 0.3553 | 4450 | 0.0224 | - | | 0.3593 | 4500 | 0.0006 | - | | 0.3633 | 4550 | 0.0005 | - | | 0.3673 | 4600 | 0.0004 | - | | 0.3713 | 4650 | 0.0025 | - | | 0.3752 | 4700 | 0.0003 | - | | 0.3792 | 4750 | 0.0218 | - | | 0.3832 | 4800 | 0.001 | - | | 0.3872 | 4850 | 0.0004 | - | | 0.3912 | 4900 | 0.0004 | - | | 0.3952 | 4950 | 0.0161 | - | | 0.3992 | 5000 | 0.0008 | - | | 0.4032 | 5050 | 0.0024 | - | | 0.4072 | 5100 | 0.0003 | - | | 0.4112 | 5150 | 0.0002 | - | | 0.4152 | 5200 | 0.0005 | - | | 0.4192 | 5250 | 0.0021 | - | | 0.4232 | 5300 | 0.0235 | - | | 0.4271 | 5350 | 0.0035 | - | | 0.4311 | 5400 | 0.0007 | - | | 0.4351 | 5450 | 0.0007 | - | | 0.4391 | 5500 | 0.0217 | - | | 0.4431 | 5550 | 0.0006 | - | | 0.4471 | 5600 | 0.0054 | - | | 0.4511 | 5650 | 0.002 | - | | 0.4551 | 5700 | 0.0013 | - | | 0.4591 | 5750 | 0.0026 | - | | 0.4631 | 5800 | 0.0051 | - | | 0.4671 | 5850 | 0.0003 | - | | 0.4711 | 5900 | 0.0003 | - | | 0.4750 | 5950 | 0.0119 | - | | 0.4790 | 6000 | 0.0011 | - | | 0.4830 | 6050 | 0.0253 | - | | 0.4870 | 6100 | 0.0244 | - | | 0.4910 | 6150 | 0.0002 | - | | 0.4950 | 6200 | 0.0002 | - | | 0.4990 | 6250 | 0.0002 | - | | 0.5030 | 6300 | 0.0167 | - | | 0.5070 | 6350 | 0.0002 | - | | 0.5110 | 6400 | 0.0003 | - | | 0.5150 | 6450 | 0.0012 | - | | 0.5190 | 6500 | 0.003 | - | | 0.5230 | 6550 | 0.0003 | - | | 0.5269 | 6600 | 0.0003 | - | | 0.5309 | 6650 | 0.0006 | - | | 0.5349 | 6700 | 0.0026 | - | | 0.5389 | 6750 | 0.0004 | - | | 0.5429 | 6800 | 0.0001 | - | | 0.5469 | 6850 | 0.0002 | - | | 0.5509 | 6900 | 0.0003 | - | | 0.5549 | 6950 | 0.0028 | - | | 0.5589 | 7000 | 0.0022 | - | | 0.5629 | 7050 | 0.0007 | - | | 0.5669 | 7100 | 0.0004 | - | | 0.5709 | 7150 | 0.0002 | - | | 0.5749 | 7200 | 0.0001 | - | | 0.5788 | 7250 | 0.0122 | - | | 0.5828 | 7300 | 0.0017 | - | | 0.5868 | 7350 | 0.0001 | - | | 0.5908 | 7400 | 0.0002 | - | | 0.5948 | 7450 | 0.0001 | - | | 0.5988 | 7500 | 0.0003 | - | | 0.6028 | 7550 | 0.0011 | - | | 0.6068 | 7600 | 0.0002 | - | | 0.6108 | 7650 | 0.0003 | - | | 0.6148 | 7700 | 0.0001 | - | | 0.6188 | 7750 | 0.0001 | - | | 0.6228 | 7800 | 0.0001 | - | | 0.6267 | 7850 | 0.0002 | - | | 0.6307 | 7900 | 0.0149 | - | | 0.6347 | 7950 | 0.0106 | - | | 0.6387 | 8000 | 0.0015 | - | | 0.6427 | 8050 | 0.0001 | - | | 0.6467 | 8100 | 0.0009 | - | | 0.6507 | 8150 | 0.0015 | - | | 0.6547 | 8200 | 0.0306 | - | | 0.6587 | 8250 | 0.0054 | - | | 0.6627 | 8300 | 0.0011 | - | | 0.6667 | 8350 | 0.0003 | - | | 0.6707 | 8400 | 0.0001 | - | | 0.6747 | 8450 | 0.0024 | - | | 0.6786 | 8500 | 0.0001 | - | | 0.6826 | 8550 | 0.0001 | - | | 0.6866 | 8600 | 0.0001 | - | | 0.6906 | 8650 | 0.0072 | - | | 0.6946 | 8700 | 0.0001 | - | | 0.6986 | 8750 | 0.0002 | - | | 0.7026 | 8800 | 0.0001 | - | | 0.7066 | 8850 | 0.0243 | - | | 0.7106 | 8900 | 0.0001 | - | | 0.7146 | 8950 | 0.0001 | - | | 0.7186 | 9000 | 0.0001 | - | | 0.7226 | 9050 | 0.0001 | - | | 0.7265 | 9100 | 0.0001 | - | | 0.7305 | 9150 | 0.0001 | - | | 0.7345 | 9200 | 0.0008 | - | | 0.7385 | 9250 | 0.021 | - | | 0.7425 | 9300 | 0.0229 | - | | 0.7465 | 9350 | 0.0001 | - | | 0.7505 | 9400 | 0.002 | - | | 0.7545 | 9450 | 0.0008 | - | | 0.7585 | 9500 | 0.0225 | - | | 0.7625 | 9550 | 0.0001 | - | | 0.7665 | 9600 | 0.0041 | - | | 0.7705 | 9650 | 0.0012 | - | | 0.7745 | 9700 | 0.0034 | - | | 0.7784 | 9750 | 0.0011 | - | | 0.7824 | 9800 | 0.0008 | - | | 0.7864 | 9850 | 0.0101 | - | | 0.7904 | 9900 | 0.0039 | - | | 0.7944 | 9950 | 0.0001 | - | | 0.7984 | 10000 | 0.0005 | - | | 0.8024 | 10050 | 0.0011 | - | | 0.8064 | 10100 | 0.0025 | - | | 0.8104 | 10150 | 0.0001 | - | | 0.8144 | 10200 | 0.0003 | - | | 0.8184 | 10250 | 0.0002 | - | | 0.8224 | 10300 | 0.0002 | - | | 0.8263 | 10350 | 0.0001 | - | | 0.8303 | 10400 | 0.0007 | - | | 0.8343 | 10450 | 0.0005 | - | | 0.8383 | 10500 | 0.0005 | - | | 0.8423 | 10550 | 0.0001 | - | | 0.8463 | 10600 | 0.0206 | - | | 0.8503 | 10650 | 0.0023 | - | | 0.8543 | 10700 | 0.0001 | - | | 0.8583 | 10750 | 0.0001 | - | | 0.8623 | 10800 | 0.0001 | - | | 0.8663 | 10850 | 0.0001 | - | | 0.8703 | 10900 | 0.0001 | - | | 0.8743 | 10950 | 0.0002 | - | | 0.8782 | 11000 | 0.0007 | - | | 0.8822 | 11050 | 0.0025 | - | | 0.8862 | 11100 | 0.0001 | - | | 0.8902 | 11150 | 0.0001 | - | | 0.8942 | 11200 | 0.0001 | - | | 0.8982 | 11250 | 0.0001 | - | | 0.9022 | 11300 | 0.0047 | - | | 0.9062 | 11350 | 0.0001 | - | | 0.9102 | 11400 | 0.0002 | - | | 0.9142 | 11450 | 0.0001 | - | | 0.9182 | 11500 | 0.0013 | - | | 0.9222 | 11550 | 0.0011 | - | | 0.9261 | 11600 | 0.0001 | - | | 0.9301 | 11650 | 0.001 | - | | 0.9341 | 11700 | 0.0145 | - | | 0.9381 | 11750 | 0.0001 | - | | 0.9421 | 11800 | 0.0156 | - | | 0.9461 | 11850 | 0.0001 | - | | 0.9501 | 11900 | 0.0016 | - | | 0.9541 | 11950 | 0.0001 | - | | 0.9581 | 12000 | 0.0011 | - | | 0.9621 | 12050 | 0.002 | - | | 0.9661 | 12100 | 0.0001 | - | | 0.9701 | 12150 | 0.0004 | - | | 0.9741 | 12200 | 0.0007 | - | | 0.9780 | 12250 | 0.0014 | - | | 0.9820 | 12300 | 0.0255 | - | | 0.9860 | 12350 | 0.004 | - | | 0.9900 | 12400 | 0.002 | - | | 0.9940 | 12450 | 0.0146 | - | | 0.9980 | 12500 | 0.0008 | - | | 1.0 | 12525 | - | 0.0588 | | 1.0020 | 12550 | 0.0175 | - | | 1.0060 | 12600 | 0.0001 | - | | 1.0100 | 12650 | 0.0006 | - | | 1.0140 | 12700 | 0.0002 | - | | 1.0180 | 12750 | 0.0149 | - | | 1.0220 | 12800 | 0.0001 | - | | 1.0259 | 12850 | 0.0001 | - | | 1.0299 | 12900 | 0.0001 | - | | 1.0339 | 12950 | 0.003 | - | | 1.0379 | 13000 | 0.0003 | - | | 1.0419 | 13050 | 0.0254 | - | | 1.0459 | 13100 | 0.0001 | - | | 1.0499 | 13150 | 0.0001 | - | | 1.0539 | 13200 | 0.0001 | - | | 1.0579 | 13250 | 0.0001 | - | | 1.0619 | 13300 | 0.0003 | - | | 1.0659 | 13350 | 0.0244 | - | | 1.0699 | 13400 | 0.0001 | - | | 1.0739 | 13450 | 0.0001 | - | | 1.0778 | 13500 | 0.0175 | - | | 1.0818 | 13550 | 0.0002 | - | | 1.0858 | 13600 | 0.0002 | - | | 1.0898 | 13650 | 0.0001 | - | | 1.0938 | 13700 | 0.0001 | - | | 1.0978 | 13750 | 0.0002 | - | | 1.1018 | 13800 | 0.0001 | - | | 1.1058 | 13850 | 0.0001 | - | | 1.1098 | 13900 | 0.0001 | - | | 1.1138 | 13950 | 0.0005 | - | | 1.1178 | 14000 | 0.0001 | - | | 1.1218 | 14050 | 0.0001 | - | | 1.1257 | 14100 | 0.0002 | - | | 1.1297 | 14150 | 0.0001 | - | | 1.1337 | 14200 | 0.0002 | - | | 1.1377 | 14250 | 0.0008 | - | | 1.1417 | 14300 | 0.0001 | - | | 1.1457 | 14350 | 0.0001 | - | | 1.1497 | 14400 | 0.0013 | - | | 1.1537 | 14450 | 0.0001 | - | | 1.1577 | 14500 | 0.0001 | - | | 1.1617 | 14550 | 0.0004 | - | | 1.1657 | 14600 | 0.0001 | - | | 1.1697 | 14650 | 0.0001 | - | | 1.1737 | 14700 | 0.001 | - | | 1.1776 | 14750 | 0.0156 | - | | 1.1816 | 14800 | 0.0001 | - | | 1.1856 | 14850 | 0.0003 | - | | 1.1896 | 14900 | 0.0045 | - | | 1.1936 | 14950 | 0.0011 | - | | 1.1976 | 15000 | 0.0015 | - | | 1.2016 | 15050 | 0.0017 | - | | 1.2056 | 15100 | 0.017 | - | | 1.2096 | 15150 | 0.0001 | - | | 1.2136 | 15200 | 0.0011 | - | | 1.2176 | 15250 | 0.0003 | - | | 1.2216 | 15300 | 0.0001 | - | | 1.2255 | 15350 | 0.0001 | - | | 1.2295 | 15400 | 0.0005 | - | | 1.2335 | 15450 | 0.0144 | - | | 1.2375 | 15500 | 0.0001 | - | | 1.2415 | 15550 | 0.0001 | - | | 1.2455 | 15600 | 0.0191 | - | | 1.2495 | 15650 | 0.0001 | - | | 1.2535 | 15700 | 0.0001 | - | | 1.2575 | 15750 | 0.0001 | - | | 1.2615 | 15800 | 0.0001 | - | | 1.2655 | 15850 | 0.0008 | - | | 1.2695 | 15900 | 0.0005 | - | | 1.2735 | 15950 | 0.013 | - | | 1.2774 | 16000 | 0.0001 | - | | 1.2814 | 16050 | 0.0201 | - | | 1.2854 | 16100 | 0.0008 | - | | 1.2894 | 16150 | 0.0001 | - | | 1.2934 | 16200 | 0.0001 | - | | 1.2974 | 16250 | 0.0001 | - | | 1.3014 | 16300 | 0.0001 | - | | 1.3054 | 16350 | 0.0 | - | | 1.3094 | 16400 | 0.0118 | - | | 1.3134 | 16450 | 0.0253 | - | | 1.3174 | 16500 | 0.0001 | - | | 1.3214 | 16550 | 0.0012 | - | | 1.3253 | 16600 | 0.0017 | - | | 1.3293 | 16650 | 0.0001 | - | | 1.3333 | 16700 | 0.0094 | - | | 1.3373 | 16750 | 0.0001 | - | | 1.3413 | 16800 | 0.0243 | - | | 1.3453 | 16850 | 0.049 | - | | 1.3493 | 16900 | 0.0001 | - | | 1.3533 | 16950 | 0.0247 | - | | 1.3573 | 17000 | 0.0001 | - | | 1.3613 | 17050 | 0.0001 | - | | 1.3653 | 17100 | 0.0001 | - | | 1.3693 | 17150 | 0.0246 | - | | 1.3733 | 17200 | 0.0001 | - | | 1.3772 | 17250 | 0.0245 | - | | 1.3812 | 17300 | 0.0012 | - | | 1.3852 | 17350 | 0.0001 | - | | 1.3892 | 17400 | 0.0001 | - | | 1.3932 | 17450 | 0.0001 | - | | 1.3972 | 17500 | 0.0001 | - | | 1.4012 | 17550 | 0.0001 | - | | 1.4052 | 17600 | 0.0002 | - | | 1.4092 | 17650 | 0.0001 | - | | 1.4132 | 17700 | 0.0001 | - | | 1.4172 | 17750 | 0.0039 | - | | 1.4212 | 17800 | 0.0012 | - | | 1.4251 | 17850 | 0.0043 | - | | 1.4291 | 17900 | 0.0001 | - | | 1.4331 | 17950 | 0.0001 | - | | 1.4371 | 18000 | 0.0001 | - | | 1.4411 | 18050 | 0.0001 | - | | 1.4451 | 18100 | 0.0043 | - | | 1.4491 | 18150 | 0.0023 | - | | 1.4531 | 18200 | 0.0001 | - | | 1.4571 | 18250 | 0.0002 | - | | 1.4611 | 18300 | 0.0001 | - | | 1.4651 | 18350 | 0.0001 | - | | 1.4691 | 18400 | 0.0001 | - | | 1.4731 | 18450 | 0.0099 | - | | 1.4770 | 18500 | 0.0001 | - | | 1.4810 | 18550 | 0.0001 | - | | 1.4850 | 18600 | 0.03 | - | | 1.4890 | 18650 | 0.0001 | - | | 1.4930 | 18700 | 0.0014 | - | | 1.4970 | 18750 | 0.0 | - | | 1.5010 | 18800 | 0.0246 | - | | 1.5050 | 18850 | 0.0001 | - | | 1.5090 | 18900 | 0.0001 | - | | 1.5130 | 18950 | 0.0001 | - | | 1.5170 | 19000 | 0.0001 | - | | 1.5210 | 19050 | 0.0001 | - | | 1.5250 | 19100 | 0.0001 | - | | 1.5289 | 19150 | 0.0001 | - | | 1.5329 | 19200 | 0.0032 | - | | 1.5369 | 19250 | 0.0001 | - | | 1.5409 | 19300 | 0.0001 | - | | 1.5449 | 19350 | 0.0001 | - | | 1.5489 | 19400 | 0.025 | - | | 1.5529 | 19450 | 0.0028 | - | | 1.5569 | 19500 | 0.0028 | - | | 1.5609 | 19550 | 0.0001 | - | | 1.5649 | 19600 | 0.005 | - | | 1.5689 | 19650 | 0.0024 | - | | 1.5729 | 19700 | 0.0001 | - | | 1.5768 | 19750 | 0.0 | - | | 1.5808 | 19800 | 0.0001 | - | | 1.5848 | 19850 | 0.0001 | - | | 1.5888 | 19900 | 0.0001 | - | | 1.5928 | 19950 | 0.0001 | - | | 1.5968 | 20000 | 0.0001 | - | | 1.6008 | 20050 | 0.0001 | - | | 1.6048 | 20100 | 0.0001 | - | | 1.6088 | 20150 | 0.0001 | - | | 1.6128 | 20200 | 0.0 | - | | 1.6168 | 20250 | 0.0001 | - | | 1.6208 | 20300 | 0.0001 | - | | 1.6248 | 20350 | 0.0028 | - | | 1.6287 | 20400 | 0.0019 | - | | 1.6327 | 20450 | 0.0115 | - | | 1.6367 | 20500 | 0.0029 | - | | 1.6407 | 20550 | 0.0035 | - | | 1.6447 | 20600 | 0.0005 | - | | 1.6487 | 20650 | 0.0007 | - | | 1.6527 | 20700 | 0.0374 | - | | 1.6567 | 20750 | 0.0064 | - | | 1.6607 | 20800 | 0.004 | - | | 1.6647 | 20850 | 0.0009 | - | | 1.6687 | 20900 | 0.0 | - | | 1.6727 | 20950 | 0.0017 | - | | 1.6766 | 21000 | 0.0001 | - | | 1.6806 | 21050 | 0.0001 | - | | 1.6846 | 21100 | 0.0001 | - | | 1.6886 | 21150 | 0.0083 | - | | 1.6926 | 21200 | 0.0001 | - | | 1.6966 | 21250 | 0.0001 | - | | 1.7006 | 21300 | 0.0001 | - | | 1.7046 | 21350 | 0.0009 | - | | 1.7086 | 21400 | 0.0001 | - | | 1.7126 | 21450 | 0.0001 | - | | 1.7166 | 21500 | 0.0015 | - | | 1.7206 | 21550 | 0.0001 | - | | 1.7246 | 21600 | 0.0001 | - | | 1.7285 | 21650 | 0.0001 | - | | 1.7325 | 21700 | 0.0007 | - | | 1.7365 | 21750 | 0.0001 | - | | 1.7405 | 21800 | 0.0213 | - | | 1.7445 | 21850 | 0.0007 | - | | 1.7485 | 21900 | 0.0029 | - | | 1.7525 | 21950 | 0.0007 | - | | 1.7565 | 22000 | 0.0025 | - | | 1.7605 | 22050 | 0.0002 | - | | 1.7645 | 22100 | 0.0173 | - | | 1.7685 | 22150 | 0.0012 | - | | 1.7725 | 22200 | 0.0027 | - | | 1.7764 | 22250 | 0.0009 | - | | 1.7804 | 22300 | 0.0013 | - | | 1.7844 | 22350 | 0.024 | - | | 1.7884 | 22400 | 0.0043 | - | | 1.7924 | 22450 | 0.0001 | - | | 1.7964 | 22500 | 0.0001 | - | | 1.8004 | 22550 | 0.0109 | - | | 1.8044 | 22600 | 0.0001 | - | | 1.8084 | 22650 | 0.0002 | - | | 1.8124 | 22700 | 0.0246 | - | | 1.8164 | 22750 | 0.0001 | - | | 1.8204 | 22800 | 0.0002 | - | | 1.8244 | 22850 | 0.0248 | - | | 1.8283 | 22900 | 0.0001 | - | | 1.8323 | 22950 | 0.0119 | - | | 1.8363 | 23000 | 0.0254 | - | | 1.8403 | 23050 | 0.1233 | - | | 1.8443 | 23100 | 0.0003 | - | | 1.8483 | 23150 | 0.0001 | - | | 1.8523 | 23200 | 0.0 | - | | 1.8563 | 23250 | 0.0 | - | | 1.8603 | 23300 | 0.0001 | - | | 1.8643 | 23350 | 0.0001 | - | | 1.8683 | 23400 | 0.0001 | - | | 1.8723 | 23450 | 0.0001 | - | | 1.8762 | 23500 | 0.0001 | - | | 1.8802 | 23550 | 0.0017 | - | | 1.8842 | 23600 | 0.0 | - | | 1.8882 | 23650 | 0.0001 | - | | 1.8922 | 23700 | 0.0 | - | | 1.8962 | 23750 | 0.0001 | - | | 1.9002 | 23800 | 0.0055 | - | | 1.9042 | 23850 | 0.0001 | - | | 1.9082 | 23900 | 0.0003 | - | | 1.9122 | 23950 | 0.0002 | - | | 1.9162 | 24000 | 0.0007 | - | | 1.9202 | 24050 | 0.0011 | - | | 1.9242 | 24100 | 0.0001 | - | | 1.9281 | 24150 | 0.0001 | - | | 1.9321 | 24200 | 0.0001 | - | | 1.9361 | 24250 | 0.0001 | - | | 1.9401 | 24300 | 0.0192 | - | | 1.9441 | 24350 | 0.0001 | - | | 1.9481 | 24400 | 0.0008 | - | | 1.9521 | 24450 | 0.0 | - | | 1.9561 | 24500 | 0.0004 | - | | 1.9601 | 24550 | 0.0001 | - | | 1.9641 | 24600 | 0.0001 | - | | 1.9681 | 24650 | 0.0001 | - | | 1.9721 | 24700 | 0.0001 | - | | 1.9760 | 24750 | 0.0013 | - | | 1.9800 | 24800 | 0.0129 | - | | 1.9840 | 24850 | 0.0024 | - | | 1.9880 | 24900 | 0.0015 | - | | 1.9920 | 24950 | 0.0196 | - | | 1.9960 | 25000 | 0.0164 | - | | 2.0 | 25050 | 0.002 | 0.0769 | | 2.0040 | 25100 | 0.0001 | - | | 2.0080 | 25150 | 0.0156 | - | | 2.0120 | 25200 | 0.0 | - | | 2.0160 | 25250 | 0.0169 | - | | 2.0200 | 25300 | 0.0023 | - | | 2.0240 | 25350 | 0.0001 | - | | 2.0279 | 25400 | 0.0 | - | | 2.0319 | 25450 | 0.001 | - | | 2.0359 | 25500 | 0.0152 | - | | 2.0399 | 25550 | 0.0001 | - | | 2.0439 | 25600 | 0.001 | - | | 2.0479 | 25650 | 0.0001 | - | | 2.0519 | 25700 | 0.002 | - | | 2.0559 | 25750 | 0.0006 | - | | 2.0599 | 25800 | 0.0031 | - | | 2.0639 | 25850 | 0.0016 | - | | 2.0679 | 25900 | 0.0 | - | | 2.0719 | 25950 | 0.0001 | - | | 2.0758 | 26000 | 0.0001 | - | | 2.0798 | 26050 | 0.0159 | - | | 2.0838 | 26100 | 0.0005 | - | | 2.0878 | 26150 | 0.0001 | - | | 2.0918 | 26200 | 0.0001 | - | | 2.0958 | 26250 | 0.0006 | - | | 2.0998 | 26300 | 0.0008 | - | | 2.1038 | 26350 | 0.0 | - | | 2.1078 | 26400 | 0.0 | - | | 2.1118 | 26450 | 0.0183 | - | | 2.1158 | 26500 | 0.0001 | - | | 2.1198 | 26550 | 0.0167 | - | | 2.1238 | 26600 | 0.0001 | - | | 2.1277 | 26650 | 0.0014 | - | | 2.1317 | 26700 | 0.0003 | - | | 2.1357 | 26750 | 0.0014 | - | | 2.1397 | 26800 | 0.0001 | - | | 2.1437 | 26850 | 0.0001 | - | | 2.1477 | 26900 | 0.0011 | - | | 2.1517 | 26950 | 0.0 | - | | 2.1557 | 27000 | 0.0001 | - | | 2.1597 | 27050 | 0.0001 | - | | 2.1637 | 27100 | 0.0007 | - | | 2.1677 | 27150 | 0.0001 | - | | 2.1717 | 27200 | 0.0 | - | | 2.1756 | 27250 | 0.0001 | - | | 2.1796 | 27300 | 0.0005 | - | | 2.1836 | 27350 | 0.0 | - | | 2.1876 | 27400 | 0.0002 | - | | 2.1916 | 27450 | 0.0001 | - | | 2.1956 | 27500 | 0.0001 | - | | 2.1996 | 27550 | 0.025 | - | | 2.2036 | 27600 | 0.0001 | - | | 2.2076 | 27650 | 0.0001 | - | | 2.2116 | 27700 | 0.0001 | - | | 2.2156 | 27750 | 0.0001 | - | | 2.2196 | 27800 | 0.0001 | - | | 2.2236 | 27850 | 0.0001 | - | | 2.2275 | 27900 | 0.0001 | - | | 2.2315 | 27950 | 0.0 | - | | 2.2355 | 28000 | 0.0001 | - | | 2.2395 | 28050 | 0.0001 | - | | 2.2435 | 28100 | 0.0245 | - | | 2.2475 | 28150 | 0.0001 | - | | 2.2515 | 28200 | 0.0251 | - | | 2.2555 | 28250 | 0.0001 | - | | 2.2595 | 28300 | 0.0497 | - | | 2.2635 | 28350 | 0.0002 | - | | 2.2675 | 28400 | 0.0487 | - | | 2.2715 | 28450 | 0.0703 | - | | 2.2754 | 28500 | 0.0248 | - | | 2.2794 | 28550 | 0.0001 | - | | 2.2834 | 28600 | 0.0245 | - | | 2.2874 | 28650 | 0.0004 | - | | 2.2914 | 28700 | 0.0001 | - | | 2.2954 | 28750 | 0.0001 | - | | 2.2994 | 28800 | 0.0 | - | | 2.3034 | 28850 | 0.0002 | - | | 2.3074 | 28900 | 0.0489 | - | | 2.3114 | 28950 | 0.0245 | - | | 2.3154 | 29000 | 0.0002 | - | | 2.3194 | 29050 | 0.0001 | - | | 2.3234 | 29100 | 0.0001 | - | | 2.3273 | 29150 | 0.0247 | - | | 2.3313 | 29200 | 0.0003 | - | | 2.3353 | 29250 | 0.0001 | - | | 2.3393 | 29300 | 0.0001 | - | | 2.3433 | 29350 | 0.0246 | - | | 2.3473 | 29400 | 0.0246 | - | | 2.3513 | 29450 | 0.0246 | - | | 2.3553 | 29500 | 0.0001 | - | | 2.3593 | 29550 | 0.0001 | - | | 2.3633 | 29600 | 0.0001 | - | | 2.3673 | 29650 | 0.0246 | - | | 2.3713 | 29700 | 0.0 | - | | 2.3752 | 29750 | 0.0246 | - | | 2.3792 | 29800 | 0.0001 | - | | 2.3832 | 29850 | 0.0001 | - | | 2.3872 | 29900 | 0.0001 | - | | 2.3912 | 29950 | 0.0002 | - | | 2.3952 | 30000 | 0.0248 | - | | 2.3992 | 30050 | 0.0002 | - | | 2.4032 | 30100 | 0.0001 | - | | 2.4072 | 30150 | 0.0001 | - | | 2.4112 | 30200 | 0.0001 | - | | 2.4152 | 30250 | 0.0001 | - | | 2.4192 | 30300 | 0.0001 | - | | 2.4232 | 30350 | 0.0245 | - | | 2.4271 | 30400 | 0.0001 | - | | 2.4311 | 30450 | 0.0001 | - | | 2.4351 | 30500 | 0.0001 | - | | 2.4391 | 30550 | 0.0 | - | | 2.4431 | 30600 | 0.0001 | - | | 2.4471 | 30650 | 0.0001 | - | | 2.4511 | 30700 | 0.0001 | - | | 2.4551 | 30750 | 0.0001 | - | | 2.4591 | 30800 | 0.0001 | - | | 2.4631 | 30850 | 0.0002 | - | | 2.4671 | 30900 | 0.0001 | - | | 2.4711 | 30950 | 0.0245 | - | | 2.4750 | 31000 | 0.0001 | - | | 2.4790 | 31050 | 0.0249 | - | | 2.4830 | 31100 | 0.0246 | - | | 2.4870 | 31150 | 0.0001 | - | | 2.4910 | 31200 | 0.0246 | - | | 2.4950 | 31250 | 0.0001 | - | | 2.4990 | 31300 | 0.0247 | - | | 2.5030 | 31350 | 0.0001 | - | | 2.5070 | 31400 | 0.0001 | - | | 2.5110 | 31450 | 0.0003 | - | | 2.5150 | 31500 | 0.0002 | - | | 2.5190 | 31550 | 0.0002 | - | | 2.5230 | 31600 | 0.0001 | - | | 2.5269 | 31650 | 0.0001 | - | | 2.5309 | 31700 | 0.0003 | - | | 2.5349 | 31750 | 0.0001 | - | | 2.5389 | 31800 | 0.0001 | - | | 2.5429 | 31850 | 0.0001 | - | | 2.5469 | 31900 | 0.0001 | - | | 2.5509 | 31950 | 0.0493 | - | | 2.5549 | 32000 | 0.0001 | - | | 2.5589 | 32050 | 0.0001 | - | | 2.5629 | 32100 | 0.0003 | - | | 2.5669 | 32150 | 0.0001 | - | | 2.5709 | 32200 | 0.025 | - | | 2.5749 | 32250 | 0.0001 | - | | 2.5788 | 32300 | 0.0249 | - | | 2.5828 | 32350 | 0.0001 | - | | 2.5868 | 32400 | 0.0001 | - | | 2.5908 | 32450 | 0.0001 | - | | 2.5948 | 32500 | 0.0001 | - | | 2.5988 | 32550 | 0.0004 | - | | 2.6028 | 32600 | 0.0001 | - | | 2.6068 | 32650 | 0.0001 | - | | 2.6108 | 32700 | 0.0001 | - | | 2.6148 | 32750 | 0.0001 | - | | 2.6188 | 32800 | 0.0001 | - | | 2.6228 | 32850 | 0.0001 | - | | 2.6267 | 32900 | 0.0001 | - | | 2.6307 | 32950 | 0.0492 | - | | 2.6347 | 33000 | 0.0001 | - | | 2.6387 | 33050 | 0.0001 | - | | 2.6427 | 33100 | 0.0 | - | | 2.6467 | 33150 | 0.0001 | - | | 2.6507 | 33200 | 0.0247 | - | | 2.6547 | 33250 | 0.0001 | - | | 2.6587 | 33300 | 0.0001 | - | | 2.6627 | 33350 | 0.0001 | - | | 2.6667 | 33400 | 0.0001 | - | | 2.6707 | 33450 | 0.0001 | - | | 2.6747 | 33500 | 0.0001 | - | | 2.6786 | 33550 | 0.0001 | - | | 2.6826 | 33600 | 0.0001 | - | | 2.6866 | 33650 | 0.0002 | - | | 2.6906 | 33700 | 0.0001 | - | | 2.6946 | 33750 | 0.0001 | - | | 2.6986 | 33800 | 0.0001 | - | | 2.7026 | 33850 | 0.0001 | - | | 2.7066 | 33900 | 0.0254 | - | | 2.7106 | 33950 | 0.0001 | - | | 2.7146 | 34000 | 0.0001 | - | | 2.7186 | 34050 | 0.0001 | - | | 2.7226 | 34100 | 0.0001 | - | | 2.7265 | 34150 | 0.0001 | - | | 2.7305 | 34200 | 0.0001 | - | | 2.7345 | 34250 | 0.0002 | - | | 2.7385 | 34300 | 0.0498 | - | | 2.7425 | 34350 | 0.0001 | - | | 2.7465 | 34400 | 0.0001 | - | | 2.7505 | 34450 | 0.0001 | - | | 2.7545 | 34500 | 0.0001 | - | | 2.7585 | 34550 | 0.0248 | - | | 2.7625 | 34600 | 0.0 | - | | 2.7665 | 34650 | 0.0001 | - | | 2.7705 | 34700 | 0.0001 | - | | 2.7745 | 34750 | 0.0001 | - | | 2.7784 | 34800 | 0.0001 | - | | 2.7824 | 34850 | 0.0247 | - | | 2.7864 | 34900 | 0.0001 | - | | 2.7904 | 34950 | 0.0001 | - | | 2.7944 | 35000 | 0.0001 | - | | 2.7984 | 35050 | 0.0001 | - | | 2.8024 | 35100 | 0.0001 | - | | 2.8064 | 35150 | 0.0001 | - | | 2.8104 | 35200 | 0.0001 | - | | 2.8144 | 35250 | 0.0001 | - | | 2.8184 | 35300 | 0.0001 | - | | 2.8224 | 35350 | 0.0001 | - | | 2.8263 | 35400 | 0.0 | - | | 2.8303 | 35450 | 0.0001 | - | | 2.8343 | 35500 | 0.0 | - | | 2.8383 | 35550 | 0.0 | - | | 2.8423 | 35600 | 0.0001 | - | | 2.8463 | 35650 | 0.0254 | - | | 2.8503 | 35700 | 0.0001 | - | | 2.8543 | 35750 | 0.0001 | - | | 2.8583 | 35800 | 0.0001 | - | | 2.8623 | 35850 | 0.0 | - | | 2.8663 | 35900 | 0.0001 | - | | 2.8703 | 35950 | 0.0001 | - | | 2.8743 | 36000 | 0.0002 | - | | 2.8782 | 36050 | 0.0001 | - | | 2.8822 | 36100 | 0.0001 | - | | 2.8862 | 36150 | 0.0 | - | | 2.8902 | 36200 | 0.0001 | - | | 2.8942 | 36250 | 0.0001 | - | | 2.8982 | 36300 | 0.0001 | - | | 2.9022 | 36350 | 0.0001 | - | | 2.9062 | 36400 | 0.0001 | - | | 2.9102 | 36450 | 0.0001 | - | | 2.9142 | 36500 | 0.0001 | - | | 2.9182 | 36550 | 0.0001 | - | | 2.9222 | 36600 | 0.0001 | - | | 2.9261 | 36650 | 0.0002 | - | | 2.9301 | 36700 | 0.0001 | - | | 2.9341 | 36750 | 0.0248 | - | | 2.9381 | 36800 | 0.0245 | - | | 2.9421 | 36850 | 0.0001 | - | | 2.9461 | 36900 | 0.0 | - | | 2.9501 | 36950 | 0.0001 | - | | 2.9541 | 37000 | 0.0001 | - | | 2.9581 | 37050 | 0.0001 | - | | 2.9621 | 37100 | 0.0001 | - | | 2.9661 | 37150 | 0.0001 | - | | 2.9701 | 37200 | 0.0001 | - | | 2.9741 | 37250 | 0.0001 | - | | 2.9780 | 37300 | 0.0 | - | | 2.9820 | 37350 | 0.0503 | - | | 2.9860 | 37400 | 0.0001 | - | | 2.9900 | 37450 | 0.0246 | - | | 2.9940 | 37500 | 0.0001 | - | | 2.9980 | 37550 | 0.0001 | - | | 3.0 | 37575 | - | 0.0396 | | 3.0020 | 37600 | 0.0248 | - | | 3.0060 | 37650 | 0.0001 | - | | 3.0100 | 37700 | 0.0001 | - | | 3.0140 | 37750 | 0.0245 | - | | 3.0180 | 37800 | 0.0002 | - | | 3.0220 | 37850 | 0.0 | - | | 3.0259 | 37900 | 0.0001 | - | | 3.0299 | 37950 | 0.0001 | - | | 3.0339 | 38000 | 0.0003 | - | | 3.0379 | 38050 | 0.0001 | - | | 3.0419 | 38100 | 0.0001 | - | | 3.0459 | 38150 | 0.0001 | - | | 3.0499 | 38200 | 0.0001 | - | | 3.0539 | 38250 | 0.0001 | - | | 3.0579 | 38300 | 0.0001 | - | | 3.0619 | 38350 | 0.0002 | - | | 3.0659 | 38400 | 0.0251 | - | | 3.0699 | 38450 | 0.0001 | - | | 3.0739 | 38500 | 0.0001 | - | | 3.0778 | 38550 | 0.0001 | - | | 3.0818 | 38600 | 0.0001 | - | | 3.0858 | 38650 | 0.0001 | - | | 3.0898 | 38700 | 0.0001 | - | | 3.0938 | 38750 | 0.0001 | - | | 3.0978 | 38800 | 0.0001 | - | | 3.1018 | 38850 | 0.0001 | - | | 3.1058 | 38900 | 0.0001 | - | | 3.1098 | 38950 | 0.0 | - | | 3.1138 | 39000 | 0.0001 | - | | 3.1178 | 39050 | 0.0001 | - | | 3.1218 | 39100 | 0.0001 | - | | 3.1257 | 39150 | 0.0001 | - | | 3.1297 | 39200 | 0.0001 | - | | 3.1337 | 39250 | 0.0001 | - | | 3.1377 | 39300 | 0.0 | - | | 3.1417 | 39350 | 0.0001 | - | | 3.1457 | 39400 | 0.0002 | - | | 3.1497 | 39450 | 0.0001 | - | | 3.1537 | 39500 | 0.0002 | - | | 3.1577 | 39550 | 0.0001 | - | | 3.1617 | 39600 | 0.0717 | - | | 3.1657 | 39650 | 0.0001 | - | | 3.1697 | 39700 | 0.0001 | - | | 3.1737 | 39750 | 0.0004 | - | | 3.1776 | 39800 | 0.0244 | - | | 3.1816 | 39850 | 0.0001 | - | | 3.1856 | 39900 | 0.0239 | - | | 3.1896 | 39950 | 0.0245 | - | | 3.1936 | 40000 | 0.0245 | - | | 3.1976 | 40050 | 0.0001 | - | | 3.2016 | 40100 | 0.0184 | - | | 3.2056 | 40150 | 0.0246 | - | | 3.2096 | 40200 | 0.0001 | - | | 3.2136 | 40250 | 0.0001 | - | | 3.2176 | 40300 | 0.0001 | - | | 3.2216 | 40350 | 0.0001 | - | | 3.2255 | 40400 | 0.0001 | - | | 3.2295 | 40450 | 0.0002 | - | | 3.2335 | 40500 | 0.0248 | - | | 3.2375 | 40550 | 0.0001 | - | | 3.2415 | 40600 | 0.0244 | - | | 3.2455 | 40650 | 0.0002 | - | | 3.2495 | 40700 | 0.0001 | - | | 3.2535 | 40750 | 0.0001 | - | | 3.2575 | 40800 | 0.0 | - | | 3.2615 | 40850 | 0.0 | - | | 3.2655 | 40900 | 0.0001 | - | | 3.2695 | 40950 | 0.0247 | - | | 3.2735 | 41000 | 0.0001 | - | | 3.2774 | 41050 | 0.0001 | - | | 3.2814 | 41100 | 0.0246 | - | | 3.2854 | 41150 | 0.0001 | - | | 3.2894 | 41200 | 0.0001 | - | | 3.2934 | 41250 | 0.0001 | - | | 3.2974 | 41300 | 0.0001 | - | | 3.3014 | 41350 | 0.0001 | - | | 3.3054 | 41400 | 0.0246 | - | | 3.3094 | 41450 | 0.0246 | - | | 3.3134 | 41500 | 0.0246 | - | | 3.3174 | 41550 | 0.0001 | - | | 3.3214 | 41600 | 0.0003 | - | | 3.3253 | 41650 | 0.0001 | - | | 3.3293 | 41700 | 0.0001 | - | | 3.3333 | 41750 | 0.025 | - | | 3.3373 | 41800 | 0.0 | - | | 3.3413 | 41850 | 0.0245 | - | | 3.3453 | 41900 | 0.0001 | - | | 3.3493 | 41950 | 0.0246 | - | | 3.3533 | 42000 | 0.0001 | - | | 3.3573 | 42050 | 0.0001 | - | | 3.3613 | 42100 | 0.0001 | - | | 3.3653 | 42150 | 0.0001 | - | | 3.3693 | 42200 | 0.0248 | - | | 3.3733 | 42250 | 0.0245 | - | | 3.3772 | 42300 | 0.0001 | - | | 3.3812 | 42350 | 0.0 | - | | 3.3852 | 42400 | 0.0001 | - | | 3.3892 | 42450 | 0.0001 | - | | 3.3932 | 42500 | 0.0001 | - | | 3.3972 | 42550 | 0.0001 | - | | 3.4012 | 42600 | 0.0001 | - | | 3.4052 | 42650 | 0.0001 | - | | 3.4092 | 42700 | 0.0001 | - | | 3.4132 | 42750 | 0.0001 | - | | 3.4172 | 42800 | 0.0001 | - | | 3.4212 | 42850 | 0.0 | - | | 3.4251 | 42900 | 0.0 | - | | 3.4291 | 42950 | 0.0001 | - | | 3.4331 | 43000 | 0.0001 | - | | 3.4371 | 43050 | 0.0001 | - | | 3.4411 | 43100 | 0.0001 | - | | 3.4451 | 43150 | 0.0002 | - | | 3.4491 | 43200 | 0.0001 | - | | 3.4531 | 43250 | 0.0002 | - | | 3.4571 | 43300 | 0.0001 | - | | 3.4611 | 43350 | 0.0 | - | | 3.4651 | 43400 | 0.0001 | - | | 3.4691 | 43450 | 0.0246 | - | | 3.4731 | 43500 | 0.0001 | - | | 3.4770 | 43550 | 0.0001 | - | | 3.4810 | 43600 | 0.0246 | - | | 3.4850 | 43650 | 0.0001 | - | | 3.4890 | 43700 | 0.0001 | - | | 3.4930 | 43750 | 0.0001 | - | | 3.4970 | 43800 | 0.0245 | - | | 3.5010 | 43850 | 0.0001 | - | | 3.5050 | 43900 | 0.0001 | - | | 3.5090 | 43950 | 0.0001 | - | | 3.5130 | 44000 | 0.0001 | - | | 3.5170 | 44050 | 0.0001 | - | | 3.5210 | 44100 | 0.0001 | - | | 3.5250 | 44150 | 0.0001 | - | | 3.5289 | 44200 | 0.0001 | - | | 3.5329 | 44250 | 0.0001 | - | | 3.5369 | 44300 | 0.0 | - | | 3.5409 | 44350 | 0.0001 | - | | 3.5449 | 44400 | 0.0 | - | | 3.5489 | 44450 | 0.0249 | - | | 3.5529 | 44500 | 0.0 | - | | 3.5569 | 44550 | 0.0002 | - | | 3.5609 | 44600 | 0.0001 | - | | 3.5649 | 44650 | 0.0002 | - | | 3.5689 | 44700 | 0.0001 | - | | 3.5729 | 44750 | 0.0 | - | | 3.5768 | 44800 | 0.0 | - | | 3.5808 | 44850 | 0.0 | - | | 3.5848 | 44900 | 0.0 | - | | 3.5888 | 44950 | 0.0 | - | | 3.5928 | 45000 | 0.0001 | - | | 3.5968 | 45050 | 0.0001 | - | | 3.6008 | 45100 | 0.0001 | - | | 3.6048 | 45150 | 0.0001 | - | | 3.6088 | 45200 | 0.0002 | - | | 3.6128 | 45250 | 0.0 | - | | 3.6168 | 45300 | 0.0001 | - | | 3.6208 | 45350 | 0.0001 | - | | 3.6248 | 45400 | 0.0001 | - | | 3.6287 | 45450 | 0.0244 | - | | 3.6327 | 45500 | 0.0 | - | | 3.6367 | 45550 | 0.0001 | - | | 3.6407 | 45600 | 0.0 | - | | 3.6447 | 45650 | 0.0001 | - | | 3.6487 | 45700 | 0.0243 | - | | 3.6527 | 45750 | 0.0252 | - | | 3.6567 | 45800 | 0.0001 | - | | 3.6607 | 45850 | 0.0001 | - | | 3.6647 | 45900 | 0.0001 | - | | 3.6687 | 45950 | 0.0 | - | | 3.6727 | 46000 | 0.0001 | - | | 3.6766 | 46050 | 0.0001 | - | | 3.6806 | 46100 | 0.0002 | - | | 3.6846 | 46150 | 0.0 | - | | 3.6886 | 46200 | 0.0247 | - | | 3.6926 | 46250 | 0.0 | - | | 3.6966 | 46300 | 0.0001 | - | | 3.7006 | 46350 | 0.0 | - | | 3.7046 | 46400 | 0.0001 | - | | 3.7086 | 46450 | 0.0001 | - | | 3.7126 | 46500 | 0.0001 | - | | 3.7166 | 46550 | 0.0 | - | | 3.7206 | 46600 | 0.0001 | - | | 3.7246 | 46650 | 0.0 | - | | 3.7285 | 46700 | 0.0001 | - | | 3.7325 | 46750 | 0.0001 | - | | 3.7365 | 46800 | 0.0246 | - | | 3.7405 | 46850 | 0.0 | - | | 3.7445 | 46900 | 0.0001 | - | | 3.7485 | 46950 | 0.0001 | - | | 3.7525 | 47000 | 0.0001 | - | | 3.7565 | 47050 | 0.0001 | - | | 3.7605 | 47100 | 0.0001 | - | | 3.7645 | 47150 | 0.025 | - | | 3.7685 | 47200 | 0.0001 | - | | 3.7725 | 47250 | 0.0002 | - | | 3.7764 | 47300 | 0.0001 | - | | 3.7804 | 47350 | 0.0247 | - | | 3.7844 | 47400 | 0.0248 | - | | 3.7884 | 47450 | 0.0001 | - | | 3.7924 | 47500 | 0.0 | - | | 3.7964 | 47550 | 0.0001 | - | | 3.8004 | 47600 | 0.025 | - | | 3.8044 | 47650 | 0.0001 | - | | 3.8084 | 47700 | 0.0001 | - | | 3.8124 | 47750 | 0.0002 | - | | 3.8164 | 47800 | 0.0001 | - | | 3.8204 | 47850 | 0.0001 | - | | 3.8244 | 47900 | 0.0252 | - | | 3.8283 | 47950 | 0.0001 | - | | 3.8323 | 48000 | 0.0254 | - | | 3.8363 | 48050 | 0.0249 | - | | 3.8403 | 48100 | 0.0001 | - | | 3.8443 | 48150 | 0.0001 | - | | 3.8483 | 48200 | 0.0001 | - | | 3.8523 | 48250 | 0.0 | - | | 3.8563 | 48300 | 0.0001 | - | | 3.8603 | 48350 | 0.0001 | - | | 3.8643 | 48400 | 0.0001 | - | | 3.8683 | 48450 | 0.0001 | - | | 3.8723 | 48500 | 0.0001 | - | | 3.8762 | 48550 | 0.0006 | - | | 3.8802 | 48600 | 0.0003 | - | | 3.8842 | 48650 | 0.0 | - | | 3.8882 | 48700 | 0.0003 | - | | 3.8922 | 48750 | 0.0001 | - | | 3.8962 | 48800 | 0.0001 | - | | 3.9002 | 48850 | 0.0001 | - | | 3.9042 | 48900 | 0.0001 | - | | 3.9082 | 48950 | 0.0001 | - | | 3.9122 | 49000 | 0.0001 | - | | 3.9162 | 49050 | 0.0246 | - | | 3.9202 | 49100 | 0.0 | - | | 3.9242 | 49150 | 0.0001 | - | | 3.9281 | 49200 | 0.0001 | - | | 3.9321 | 49250 | 0.0001 | - | | 3.9361 | 49300 | 0.0246 | - | | 3.9401 | 49350 | 0.0 | - | | 3.9441 | 49400 | 0.0001 | - | | 3.9481 | 49450 | 0.0002 | - | | 3.9521 | 49500 | 0.0 | - | | 3.9561 | 49550 | 0.0 | - | | 3.9601 | 49600 | 0.0002 | - | | 3.9641 | 49650 | 0.0248 | - | | 3.9681 | 49700 | 0.0001 | - | | 3.9721 | 49750 | 0.0001 | - | | 3.9760 | 49800 | 0.0001 | - | | 3.9800 | 49850 | 0.0248 | - | | 3.9840 | 49900 | 0.0001 | - | | 3.9880 | 49950 | 0.0245 | - | | 3.9920 | 50000 | 0.0001 | - | | 3.9960 | 50050 | 0.0487 | - | | 4.0 | 50100 | 0.0002 | 0.0927 | | 4.0040 | 50150 | 0.0001 | - | | 4.0080 | 50200 | 0.0251 | - | | 4.0120 | 50250 | 0.0245 | - | | 4.0160 | 50300 | 0.0001 | - | | 4.0200 | 50350 | 0.0001 | - | | 4.0240 | 50400 | 0.0001 | - | | 4.0279 | 50450 | 0.0 | - | | 4.0319 | 50500 | 0.0001 | - | | 4.0359 | 50550 | 0.0255 | - | | 4.0399 | 50600 | 0.0001 | - | | 4.0439 | 50650 | 0.0 | - | | 4.0479 | 50700 | 0.0001 | - | | 4.0519 | 50750 | 0.0001 | - | | 4.0559 | 50800 | 0.0 | - | | 4.0599 | 50850 | 0.0 | - | | 4.0639 | 50900 | 0.0001 | - | | 4.0679 | 50950 | 0.0001 | - | | 4.0719 | 51000 | 0.0001 | - | | 4.0758 | 51050 | 0.0001 | - | | 4.0798 | 51100 | 0.0242 | - | | 4.0838 | 51150 | 0.0001 | - | | 4.0878 | 51200 | 0.0001 | - | | 4.0918 | 51250 | 0.0001 | - | | 4.0958 | 51300 | 0.0 | - | | 4.0998 | 51350 | 0.0001 | - | | 4.1038 | 51400 | 0.0 | - | | 4.1078 | 51450 | 0.0 | - | | 4.1118 | 51500 | 0.0246 | - | | 4.1158 | 51550 | 0.0 | - | | 4.1198 | 51600 | 0.0249 | - | | 4.1238 | 51650 | 0.0001 | - | | 4.1277 | 51700 | 0.0001 | - | | 4.1317 | 51750 | 0.0001 | - | | 4.1357 | 51800 | 0.0001 | - | | 4.1397 | 51850 | 0.0 | - | | 4.1437 | 51900 | 0.0001 | - | | 4.1477 | 51950 | 0.0 | - | | 4.1517 | 52000 | 0.0001 | - | | 4.1557 | 52050 | 0.0001 | - | | 4.1597 | 52100 | 0.0001 | - | | 4.1637 | 52150 | 0.0001 | - | | 4.1677 | 52200 | 0.0001 | - | | 4.1717 | 52250 | 0.0001 | - | | 4.1756 | 52300 | 0.0 | - | | 4.1796 | 52350 | 0.0001 | - | | 4.1836 | 52400 | 0.0001 | - | | 4.1876 | 52450 | 0.0 | - | | 4.1916 | 52500 | 0.0001 | - | | 4.1956 | 52550 | 0.0001 | - | | 4.1996 | 52600 | 0.0252 | - | | 4.2036 | 52650 | 0.0001 | - | | 4.2076 | 52700 | 0.0001 | - | | 4.2116 | 52750 | 0.0001 | - | | 4.2156 | 52800 | 0.0001 | - | | 4.2196 | 52850 | 0.0001 | - | | 4.2236 | 52900 | 0.0001 | - | | 4.2275 | 52950 | 0.0 | - | | 4.2315 | 53000 | 0.0 | - | | 4.2355 | 53050 | 0.0001 | - | | 4.2395 | 53100 | 0.0244 | - | | 4.2435 | 53150 | 0.0001 | - | | 4.2475 | 53200 | 0.0001 | - | | 4.2515 | 53250 | 0.0248 | - | | 4.2555 | 53300 | 0.0001 | - | | 4.2595 | 53350 | 0.0 | - | | 4.2635 | 53400 | 0.0 | - | | 4.2675 | 53450 | 0.0245 | - | | 4.2715 | 53500 | 0.0 | - | | 4.2754 | 53550 | 0.0251 | - | | 4.2794 | 53600 | 0.0 | - | | 4.2834 | 53650 | 0.0001 | - | | 4.2874 | 53700 | 0.0001 | - | | 4.2914 | 53750 | 0.0001 | - | | 4.2954 | 53800 | 0.0 | - | | 4.2994 | 53850 | 0.0 | - | | 4.3034 | 53900 | 0.0247 | - | | 4.3074 | 53950 | 0.049 | - | | 4.3114 | 54000 | 0.0 | - | | 4.3154 | 54050 | 0.0001 | - | | 4.3194 | 54100 | 0.0 | - | | 4.3234 | 54150 | 0.0001 | - | | 4.3273 | 54200 | 0.0001 | - | | 4.3313 | 54250 | 0.0001 | - | | 4.3353 | 54300 | 0.0001 | - | | 4.3393 | 54350 | 0.0243 | - | | 4.3433 | 54400 | 0.0001 | - | | 4.3473 | 54450 | 0.0246 | - | | 4.3513 | 54500 | 0.0 | - | | 4.3553 | 54550 | 0.0001 | - | | 4.3593 | 54600 | 0.0001 | - | | 4.3633 | 54650 | 0.0001 | - | | 4.3673 | 54700 | 0.0 | - | | 4.3713 | 54750 | 0.0246 | - | | 4.3752 | 54800 | 0.0 | - | | 4.3792 | 54850 | 0.0 | - | | 4.3832 | 54900 | 0.0001 | - | | 4.3872 | 54950 | 0.0001 | - | | 4.3912 | 55000 | 0.0001 | - | | 4.3952 | 55050 | 0.0001 | - | | 4.3992 | 55100 | 0.0001 | - | | 4.4032 | 55150 | 0.0 | - | | 4.4072 | 55200 | 0.0001 | - | | 4.4112 | 55250 | 0.0 | - | | 4.4152 | 55300 | 0.0 | - | | 4.4192 | 55350 | 0.0001 | - | | 4.4232 | 55400 | 0.0244 | - | | 4.4271 | 55450 | 0.0 | - | | 4.4311 | 55500 | 0.0 | - | | 4.4351 | 55550 | 0.0001 | - | | 4.4391 | 55600 | 0.0 | - | | 4.4431 | 55650 | 0.0001 | - | | 4.4471 | 55700 | 0.0001 | - | | 4.4511 | 55750 | 0.0959 | - | | 4.4551 | 55800 | 0.0002 | - | | 4.4591 | 55850 | 0.0001 | - | | 4.4631 | 55900 | 0.0001 | - | | 4.4671 | 55950 | 0.0246 | - | | 4.4711 | 56000 | 0.0001 | - | | 4.4750 | 56050 | 0.0001 | - | | 4.4790 | 56100 | 0.0246 | - | | 4.4830 | 56150 | 0.024 | - | | 4.4870 | 56200 | 0.0001 | - | | 4.4910 | 56250 | 0.0001 | - | | 4.4950 | 56300 | 0.0245 | - | | 4.4990 | 56350 | 0.0001 | - | | 4.5030 | 56400 | 0.0001 | - | | 4.5070 | 56450 | 0.0001 | - | | 4.5110 | 56500 | 0.0001 | - | | 4.5150 | 56550 | 0.0001 | - | | 4.5190 | 56600 | 0.0001 | - | | 4.5230 | 56650 | 0.0001 | - | | 4.5269 | 56700 | 0.0 | - | | 4.5309 | 56750 | 0.0002 | - | | 4.5349 | 56800 | 0.0001 | - | | 4.5389 | 56850 | 0.0001 | - | | 4.5429 | 56900 | 0.0001 | - | | 4.5469 | 56950 | 0.0001 | - | | 4.5509 | 57000 | 0.0 | - | | 4.5549 | 57050 | 0.0001 | - | | 4.5589 | 57100 | 0.0001 | - | | 4.5629 | 57150 | 0.0001 | - | | 4.5669 | 57200 | 0.0 | - | | 4.5709 | 57250 | 0.0001 | - | | 4.5749 | 57300 | 0.0001 | - | | 4.5788 | 57350 | 0.0252 | - | | 4.5828 | 57400 | 0.0 | - | | 4.5868 | 57450 | 0.0 | - | | 4.5908 | 57500 | 0.0001 | - | | 4.5948 | 57550 | 0.0001 | - | | 4.5988 | 57600 | 0.0001 | - | | 4.6028 | 57650 | 0.0001 | - | | 4.6068 | 57700 | 0.0 | - | | 4.6108 | 57750 | 0.0 | - | | 4.6148 | 57800 | 0.0001 | - | | 4.6188 | 57850 | 0.0001 | - | | 4.6228 | 57900 | 0.0 | - | | 4.6267 | 57950 | 0.0244 | - | | 4.6307 | 58000 | 0.0416 | - | | 4.6347 | 58050 | 0.0001 | - | | 4.6387 | 58100 | 0.0 | - | | 4.6427 | 58150 | 0.0 | - | | 4.6467 | 58200 | 0.0245 | - | | 4.6507 | 58250 | 0.0001 | - | | 4.6547 | 58300 | 0.0001 | - | | 4.6587 | 58350 | 0.0 | - | | 4.6627 | 58400 | 0.0001 | - | | 4.6667 | 58450 | 0.0001 | - | | 4.6707 | 58500 | 0.0001 | - | | 4.6747 | 58550 | 0.0001 | - | | 4.6786 | 58600 | 0.0001 | - | | 4.6826 | 58650 | 0.0001 | - | | 4.6866 | 58700 | 0.0002 | - | | 4.6906 | 58750 | 0.0 | - | | 4.6946 | 58800 | 0.0001 | - | | 4.6986 | 58850 | 0.0001 | - | | 4.7026 | 58900 | 0.0001 | - | | 4.7066 | 58950 | 0.0253 | - | | 4.7106 | 59000 | 0.0001 | - | | 4.7146 | 59050 | 0.0 | - | | 4.7186 | 59100 | 0.0001 | - | | 4.7226 | 59150 | 0.0 | - | | 4.7265 | 59200 | 0.0001 | - | | 4.7305 | 59250 | 0.0001 | - | | 4.7345 | 59300 | 0.0246 | - | | 4.7385 | 59350 | 0.0252 | - | | 4.7425 | 59400 | 0.0001 | - | | 4.7465 | 59450 | 0.1531 | - | | 4.7505 | 59500 | 0.0001 | - | | 4.7545 | 59550 | 0.0001 | - | | 4.7585 | 59600 | 0.025 | - | | 4.7625 | 59650 | 0.0 | - | | 4.7665 | 59700 | 0.0001 | - | | 4.7705 | 59750 | 0.0001 | - | | 4.7745 | 59800 | 0.0001 | - | | 4.7784 | 59850 | 0.0244 | - | | 4.7824 | 59900 | 0.0009 | - | | 4.7864 | 59950 | 0.0001 | - | | 4.7904 | 60000 | 0.0009 | - | | 4.7944 | 60050 | 0.0015 | - | | 4.7984 | 60100 | 0.0252 | - | | 4.8024 | 60150 | 0.0001 | - | | 4.8064 | 60200 | 0.0245 | - | | 4.8104 | 60250 | 0.0003 | - | | 4.8144 | 60300 | 0.0002 | - | | 4.8184 | 60350 | 0.0001 | - | | 4.8224 | 60400 | 0.0001 | - | | 4.8263 | 60450 | 0.0249 | - | | 4.8303 | 60500 | 0.0002 | - | | 4.8343 | 60550 | 0.0001 | - | | 4.8383 | 60600 | 0.0001 | - | | 4.8423 | 60650 | 0.0001 | - | | 4.8463 | 60700 | 0.0242 | - | | 4.8503 | 60750 | 0.0001 | - | | 4.8543 | 60800 | 0.0002 | - | | 4.8583 | 60850 | 0.0001 | - | | 4.8623 | 60900 | 0.0001 | - | | 4.8663 | 60950 | 0.0005 | - | | 4.8703 | 61000 | 0.0001 | - | | 4.8743 | 61050 | 0.0247 | - | | 4.8782 | 61100 | 0.0001 | - | | 4.8822 | 61150 | 0.0001 | - | | 4.8862 | 61200 | 0.0001 | - | | 4.8902 | 61250 | 0.0001 | - | | 4.8942 | 61300 | 0.0247 | - | | 4.8982 | 61350 | 0.0245 | - | | 4.9022 | 61400 | 0.0001 | - | | 4.9062 | 61450 | 0.0001 | - | | 4.9102 | 61500 | 0.0249 | - | | 4.9142 | 61550 | 0.2375 | - | | 4.9182 | 61600 | 0.0001 | - | | 4.9222 | 61650 | 0.0001 | - | | 4.9261 | 61700 | 0.0002 | - | | 4.9301 | 61750 | 0.0001 | - | | 4.9341 | 61800 | 0.0494 | - | | 4.9381 | 61850 | 0.0001 | - | | 4.9421 | 61900 | 0.0001 | - | | 4.9461 | 61950 | 0.0001 | - | | 4.9501 | 62000 | 0.0001 | - | | 4.9541 | 62050 | 0.0001 | - | | 4.9581 | 62100 | 0.024 | - | | 4.9621 | 62150 | 0.0001 | - | | 4.9661 | 62200 | 0.0001 | - | | 4.9701 | 62250 | 0.0001 | - | | 4.9741 | 62300 | 0.0002 | - | | 4.9780 | 62350 | 0.0001 | - | | 4.9820 | 62400 | 0.0502 | - | | 4.9860 | 62450 | 0.0244 | - | | 4.9900 | 62500 | 0.0001 | - | | 4.9940 | 62550 | 0.0001 | - | | 4.9980 | 62600 | 0.0001 | - | | 5.0 | 62625 | - | 0.1298 | | 5.0020 | 62650 | 0.025 | - | | 5.0060 | 62700 | 0.0001 | - | | 5.0100 | 62750 | 0.0244 | - | | 5.0140 | 62800 | 0.0001 | - | | 5.0180 | 62850 | 0.0001 | - | | 5.0220 | 62900 | 0.0001 | - | | 5.0259 | 62950 | 0.0001 | - | | 5.0299 | 63000 | 0.0001 | - | | 5.0339 | 63050 | 0.0002 | - | | 5.0379 | 63100 | 0.0001 | - | | 5.0419 | 63150 | 0.0001 | - | | 5.0459 | 63200 | 0.0001 | - | | 5.0499 | 63250 | 0.0001 | - | | 5.0539 | 63300 | 0.0001 | - | | 5.0579 | 63350 | 0.0001 | - | | 5.0619 | 63400 | 0.0001 | - | | 5.0659 | 63450 | 0.0249 | - | | 5.0699 | 63500 | 0.0001 | - | | 5.0739 | 63550 | 0.0001 | - | | 5.0778 | 63600 | 0.0002 | - | | 5.0818 | 63650 | 0.0001 | - | | 5.0858 | 63700 | 0.0001 | - | | 5.0898 | 63750 | 0.0001 | - | | 5.0938 | 63800 | 0.0001 | - | | 5.0978 | 63850 | 0.0001 | - | | 5.1018 | 63900 | 0.0001 | - | | 5.1058 | 63950 | 0.0001 | - | | 5.1098 | 64000 | 0.0001 | - | | 5.1138 | 64050 | 0.0001 | - | | 5.1178 | 64100 | 0.0001 | - | | 5.1218 | 64150 | 0.0001 | - | | 5.1257 | 64200 | 0.0001 | - | | 5.1297 | 64250 | 0.0001 | - | | 5.1337 | 64300 | 0.0002 | - | | 5.1377 | 64350 | 0.0001 | - | | 5.1417 | 64400 | 0.0001 | - | | 5.1457 | 64450 | 0.0002 | - | | 5.1497 | 64500 | 0.0001 | - | | 5.1537 | 64550 | 0.0001 | - | | 5.1577 | 64600 | 0.0001 | - | | 5.1617 | 64650 | 0.0003 | - | | 5.1657 | 64700 | 0.0001 | - | | 5.1697 | 64750 | 0.0001 | - | | 5.1737 | 64800 | 0.0001 | - | | 5.1776 | 64850 | 0.0243 | - | | 5.1816 | 64900 | 0.0001 | - | | 5.1856 | 64950 | 0.0003 | - | | 5.1896 | 65000 | 0.0001 | - | | 5.1936 | 65050 | 0.0001 | - | | 5.1976 | 65100 | 0.0001 | - | | 5.2016 | 65150 | 0.0001 | - | | 5.2056 | 65200 | 0.0245 | - | | 5.2096 | 65250 | 0.0001 | - | | 5.2136 | 65300 | 0.0001 | - | | 5.2176 | 65350 | 0.0001 | - | | 5.2216 | 65400 | 0.0001 | - | | 5.2255 | 65450 | 0.0001 | - | | 5.2295 | 65500 | 0.0002 | - | | 5.2335 | 65550 | 0.0248 | - | | 5.2375 | 65600 | 0.0243 | - | | 5.2415 | 65650 | 0.0001 | - | | 5.2455 | 65700 | 0.0002 | - | | 5.2495 | 65750 | 0.0001 | - | | 5.2535 | 65800 | 0.0001 | - | | 5.2575 | 65850 | 0.0 | - | | 5.2615 | 65900 | 0.0001 | - | | 5.2655 | 65950 | 0.0245 | - | | 5.2695 | 66000 | 0.0001 | - | | 5.2735 | 66050 | 0.0001 | - | | 5.2774 | 66100 | 0.0001 | - | | 5.2814 | 66150 | 0.0244 | - | | 5.2854 | 66200 | 0.0001 | - | | 5.2894 | 66250 | 0.0001 | - | | 5.2934 | 66300 | 0.0001 | - | | 5.2974 | 66350 | 0.0001 | - | | 5.3014 | 66400 | 0.0247 | - | | 5.3054 | 66450 | 0.0244 | - | | 5.3094 | 66500 | 0.0001 | - | | 5.3134 | 66550 | 0.0248 | - | | 5.3174 | 66600 | 0.0001 | - | | 5.3214 | 66650 | 0.0003 | - | | 5.3253 | 66700 | 0.0001 | - | | 5.3293 | 66750 | 0.0001 | - | | 5.3333 | 66800 | 0.0249 | - | | 5.3373 | 66850 | 0.0244 | - | | 5.3413 | 66900 | 0.0001 | - | | 5.3453 | 66950 | 0.0246 | - | | 5.3493 | 67000 | 0.0 | - | | 5.3533 | 67050 | 0.0001 | - | | 5.3573 | 67100 | 0.0001 | - | | 5.3613 | 67150 | 0.0001 | - | | 5.3653 | 67200 | 0.0001 | - | | 5.3693 | 67250 | 0.0494 | - | | 5.3733 | 67300 | 0.0001 | - | | 5.3772 | 67350 | 0.0001 | - | | 5.3812 | 67400 | 0.0001 | - | | 5.3852 | 67450 | 0.0001 | - | | 5.3892 | 67500 | 0.0001 | - | | 5.3932 | 67550 | 0.0001 | - | | 5.3972 | 67600 | 0.0001 | - | | 5.4012 | 67650 | 0.0001 | - | | 5.4052 | 67700 | 0.0001 | - | | 5.4092 | 67750 | 0.0001 | - | | 5.4132 | 67800 | 0.0001 | - | | 5.4172 | 67850 | 0.0001 | - | | 5.4212 | 67900 | 0.0 | - | | 5.4251 | 67950 | 0.0 | - | | 5.4291 | 68000 | 0.0001 | - | | 5.4331 | 68050 | 0.0001 | - | | 5.4371 | 68100 | 0.0001 | - | | 5.4411 | 68150 | 0.0001 | - | | 5.4451 | 68200 | 0.0002 | - | | 5.4491 | 68250 | 0.0001 | - | | 5.4531 | 68300 | 0.0001 | - | | 5.4571 | 68350 | 0.0001 | - | | 5.4611 | 68400 | 0.0001 | - | | 5.4651 | 68450 | 0.0245 | - | | 5.4691 | 68500 | 0.0001 | - | | 5.4731 | 68550 | 0.0001 | - | | 5.4770 | 68600 | 0.0245 | - | | 5.4810 | 68650 | 0.0001 | - | | 5.4850 | 68700 | 0.0001 | - | | 5.4890 | 68750 | 0.0001 | - | | 5.4930 | 68800 | 0.0246 | - | | 5.4970 | 68850 | 0.0 | - | | 5.5010 | 68900 | 0.0001 | - | | 5.5050 | 68950 | 0.0001 | - | | 5.5090 | 69000 | 0.0001 | - | | 5.5130 | 69050 | 0.0001 | - | | 5.5170 | 69100 | 0.0001 | - | | 5.5210 | 69150 | 0.0001 | - | | 5.5250 | 69200 | 0.0001 | - | | 5.5289 | 69250 | 0.0001 | - | | 5.5329 | 69300 | 0.0001 | - | | 5.5369 | 69350 | 0.0 | - | | 5.5409 | 69400 | 0.0001 | - | | 5.5449 | 69450 | 0.0001 | - | | 5.5489 | 69500 | 0.0249 | - | | 5.5529 | 69550 | 0.0 | - | | 5.5569 | 69600 | 0.0001 | - | | 5.5609 | 69650 | 0.0001 | - | | 5.5649 | 69700 | 0.0016 | - | | 5.5689 | 69750 | 0.0001 | - | | 5.5729 | 69800 | 0.0 | - | | 5.5768 | 69850 | 0.0 | - | | 5.5808 | 69900 | 0.0 | - | | 5.5848 | 69950 | 0.0 | - | | 5.5888 | 70000 | 0.0 | - | | 5.5928 | 70050 | 0.0001 | - | | 5.5968 | 70100 | 0.0001 | - | | 5.6008 | 70150 | 0.0001 | - | | 5.6048 | 70200 | 0.0001 | - | | 5.6088 | 70250 | 0.0001 | - | | 5.6128 | 70300 | 0.0 | - | | 5.6168 | 70350 | 0.0 | - | | 5.6208 | 70400 | 0.0043 | - | | 5.6248 | 70450 | 0.0151 | - | | 5.6287 | 70500 | 0.0 | - | | 5.6327 | 70550 | 0.0 | - | | 5.6367 | 70600 | 0.0 | - | | 5.6407 | 70650 | 0.0033 | - | | 5.6447 | 70700 | 0.0274 | - | | 5.6487 | 70750 | 0.0031 | - | | 5.6527 | 70800 | 0.0248 | - | | 5.6567 | 70850 | 0.0 | - | | 5.6607 | 70900 | 0.0245 | - | | 5.6647 | 70950 | 0.0248 | - | | 5.6687 | 71000 | 0.0 | - | | 5.6727 | 71050 | 0.0001 | - | | 5.6766 | 71100 | 0.0001 | - | | 5.6806 | 71150 | 0.0001 | - | | 5.6846 | 71200 | 0.0001 | - | | 5.6886 | 71250 | 0.0002 | - | | 5.6926 | 71300 | 0.0 | - | | 5.6966 | 71350 | 0.0001 | - | | 5.7006 | 71400 | 0.0001 | - | | 5.7046 | 71450 | 0.0001 | - | | 5.7086 | 71500 | 0.0001 | - | | 5.7126 | 71550 | 0.0001 | - | | 5.7166 | 71600 | 0.0247 | - | | 5.7206 | 71650 | 0.0001 | - | | 5.7246 | 71700 | 0.0001 | - | | 5.7285 | 71750 | 0.0001 | - | | 5.7325 | 71800 | 0.0001 | - | | 5.7365 | 71850 | 0.0001 | - | | 5.7405 | 71900 | 0.0245 | - | | 5.7445 | 71950 | 0.0001 | - | | 5.7485 | 72000 | 0.0247 | - | | 5.7525 | 72050 | 0.0001 | - | | 5.7565 | 72100 | 0.0 | - | | 5.7605 | 72150 | 0.0001 | - | | 5.7645 | 72200 | 0.05 | - | | 5.7685 | 72250 | 0.0001 | - | | 5.7725 | 72300 | 0.0247 | - | | 5.7764 | 72350 | 0.0002 | - | | 5.7804 | 72400 | 0.0244 | - | | 5.7844 | 72450 | 0.0 | - | | 5.7884 | 72500 | 0.0001 | - | | 5.7924 | 72550 | 0.0001 | - | | 5.7964 | 72600 | 0.0 | - | | 5.8004 | 72650 | 0.0001 | - | | 5.8044 | 72700 | 0.0001 | - | | 5.8084 | 72750 | 0.0001 | - | | 5.8124 | 72800 | 0.0 | - | | 5.8164 | 72850 | 0.0001 | - | | 5.8204 | 72900 | 0.0001 | - | | 5.8244 | 72950 | 0.025 | - | | 5.8283 | 73000 | 0.0001 | - | | 5.8323 | 73050 | 0.0 | - | | 5.8363 | 73100 | 0.0247 | - | | 5.8403 | 73150 | 0.0 | - | | 5.8443 | 73200 | 0.0 | - | | 5.8483 | 73250 | 0.0001 | - | | 5.8523 | 73300 | 0.0 | - | | 5.8563 | 73350 | 0.0 | - | | 5.8603 | 73400 | 0.0001 | - | | 5.8643 | 73450 | 0.0001 | - | | 5.8683 | 73500 | 0.0 | - | | 5.8723 | 73550 | 0.0248 | - | | 5.8762 | 73600 | 0.0001 | - | | 5.8802 | 73650 | 0.0001 | - | | 5.8842 | 73700 | 0.0001 | - | | 5.8882 | 73750 | 0.0001 | - | | 5.8922 | 73800 | 0.0248 | - | | 5.8962 | 73850 | 0.0001 | - | | 5.9002 | 73900 | 0.0001 | - | | 5.9042 | 73950 | 0.0001 | - | | 5.9082 | 74000 | 0.0247 | - | | 5.9122 | 74050 | 0.0246 | - | | 5.9162 | 74100 | 0.0001 | - | | 5.9202 | 74150 | 0.0001 | - | | 5.9242 | 74200 | 0.0 | - | | 5.9281 | 74250 | 0.0001 | - | | 5.9321 | 74300 | 0.0 | - | | 5.9361 | 74350 | 0.0 | - | | 5.9401 | 74400 | 0.0 | - | | 5.9441 | 74450 | 0.0001 | - | | 5.9481 | 74500 | 0.0249 | - | | 5.9521 | 74550 | 0.0 | - | | 5.9561 | 74600 | 0.0002 | - | | 5.9601 | 74650 | 0.0001 | - | | 5.9641 | 74700 | 0.0001 | - | | 5.9681 | 74750 | 0.0 | - | | 5.9721 | 74800 | 0.0 | - | | 5.9760 | 74850 | 0.0246 | - | | 5.9800 | 74900 | 0.0001 | - | | 5.9840 | 74950 | 0.0 | - | | 5.9880 | 75000 | 0.0001 | - | | 5.9920 | 75050 | 0.0 | - | | 5.9960 | 75100 | 0.0001 | - | | 6.0 | 75150 | 0.0001 | 0.0594 | | 6.0040 | 75200 | 0.0001 | - | | 6.0080 | 75250 | 0.0497 | - | | 6.0120 | 75300 | 0.0248 | - | | 6.0160 | 75350 | 0.0495 | - | | 6.0200 | 75400 | 0.0 | - | | 6.0240 | 75450 | 0.0001 | - | | 6.0279 | 75500 | 0.0001 | - | | 6.0319 | 75550 | 0.0001 | - | | 6.0359 | 75600 | 0.0252 | - | | 6.0399 | 75650 | 0.0001 | - | | 6.0439 | 75700 | 0.0001 | - | | 6.0479 | 75750 | 0.0001 | - | | 6.0519 | 75800 | 0.0741 | - | | 6.0559 | 75850 | 0.0 | - | | 6.0599 | 75900 | 0.0 | - | | 6.0639 | 75950 | 0.0001 | - | | 6.0679 | 76000 | 0.0001 | - | | 6.0719 | 76050 | 0.0 | - | | 6.0758 | 76100 | 0.0001 | - | | 6.0798 | 76150 | 0.0001 | - | | 6.0838 | 76200 | 0.0247 | - | | 6.0878 | 76250 | 0.0001 | - | | 6.0918 | 76300 | 0.0001 | - | | 6.0958 | 76350 | 0.0244 | - | | 6.0998 | 76400 | 0.0 | - | | 6.1038 | 76450 | 0.0001 | - | | 6.1078 | 76500 | 0.0001 | - | | 6.1118 | 76550 | 0.0001 | - | | 6.1158 | 76600 | 0.0 | - | | 6.1198 | 76650 | 0.0001 | - | | 6.1238 | 76700 | 0.0 | - | | 6.1277 | 76750 | 0.0245 | - | | 6.1317 | 76800 | 0.0001 | - | | 6.1357 | 76850 | 0.0001 | - | | 6.1397 | 76900 | 0.0001 | - | | 6.1437 | 76950 | 0.0001 | - | | 6.1477 | 77000 | 0.0 | - | | 6.1517 | 77050 | 0.0001 | - | | 6.1557 | 77100 | 0.0001 | - | | 6.1597 | 77150 | 0.0001 | - | | 6.1637 | 77200 | 0.0001 | - | | 6.1677 | 77250 | 0.0 | - | | 6.1717 | 77300 | 0.0001 | - | | 6.1756 | 77350 | 0.0001 | - | | 6.1796 | 77400 | 0.0245 | - | | 6.1836 | 77450 | 0.0 | - | | 6.1876 | 77500 | 0.0496 | - | | 6.1916 | 77550 | 0.0246 | - | | 6.1956 | 77600 | 0.0001 | - | | 6.1996 | 77650 | 0.025 | - | | 6.2036 | 77700 | 0.0001 | - | | 6.2076 | 77750 | 0.0 | - | | 6.2116 | 77800 | 0.0001 | - | | 6.2156 | 77850 | 0.0001 | - | | 6.2196 | 77900 | 0.0248 | - | | 6.2236 | 77950 | 0.0247 | - | | 6.2275 | 78000 | 0.0002 | - | | 6.2315 | 78050 | 0.0001 | - | | 6.2355 | 78100 | 0.0246 | - | | 6.2395 | 78150 | 0.0001 | - | | 6.2435 | 78200 | 0.0001 | - | | 6.2475 | 78250 | 0.0001 | - | | 6.2515 | 78300 | 0.0249 | - | | 6.2555 | 78350 | 0.0001 | - | | 6.2595 | 78400 | 0.0251 | - | | 6.2635 | 78450 | 0.0 | - | | 6.2675 | 78500 | 0.0 | - | | 6.2715 | 78550 | 0.0001 | - | | 6.2754 | 78600 | 0.05 | - | | 6.2794 | 78650 | 0.0001 | - | | 6.2834 | 78700 | 0.0001 | - | | 6.2874 | 78750 | 0.0 | - | | 6.2914 | 78800 | 0.0001 | - | | 6.2954 | 78850 | 0.0001 | - | | 6.2994 | 78900 | 0.0 | - | | 6.3034 | 78950 | 0.0246 | - | | 6.3074 | 79000 | 0.0245 | - | | 6.3114 | 79050 | 0.0001 | - | | 6.3154 | 79100 | 0.0 | - | | 6.3194 | 79150 | 0.0 | - | | 6.3234 | 79200 | 0.0002 | - | | 6.3273 | 79250 | 0.0001 | - | | 6.3313 | 79300 | 0.0001 | - | | 6.3353 | 79350 | 0.0 | - | | 6.3393 | 79400 | 0.0001 | - | | 6.3433 | 79450 | 0.0249 | - | | 6.3473 | 79500 | 0.0001 | - | | 6.3513 | 79550 | 0.0001 | - | | 6.3553 | 79600 | 0.0001 | - | | 6.3593 | 79650 | 0.0001 | - | | 6.3633 | 79700 | 0.0 | - | | 6.3673 | 79750 | 0.0247 | - | | 6.3713 | 79800 | 0.0243 | - | | 6.3752 | 79850 | 0.0 | - | | 6.3792 | 79900 | 0.0001 | - | | 6.3832 | 79950 | 0.0001 | - | | 6.3872 | 80000 | 0.0001 | - | | 6.3912 | 80050 | 0.0 | - | | 6.3952 | 80100 | 0.0001 | - | | 6.3992 | 80150 | 0.0 | - | | 6.4032 | 80200 | 0.0249 | - | | 6.4072 | 80250 | 0.0247 | - | | 6.4112 | 80300 | 0.0 | - | | 6.4152 | 80350 | 0.0248 | - | | 6.4192 | 80400 | 0.0001 | - | | 6.4232 | 80450 | 0.0247 | - | | 6.4271 | 80500 | 0.0 | - | | 6.4311 | 80550 | 0.0 | - | | 6.4351 | 80600 | 0.0247 | - | | 6.4391 | 80650 | 0.0246 | - | | 6.4431 | 80700 | 0.0001 | - | | 6.4471 | 80750 | 0.0242 | - | | 6.4511 | 80800 | 0.0 | - | | 6.4551 | 80850 | 0.0001 | - | | 6.4591 | 80900 | 0.0001 | - | | 6.4631 | 80950 | 0.0243 | - | | 6.4671 | 81000 | 0.0001 | - | | 6.4711 | 81050 | 0.0001 | - | | 6.4750 | 81100 | 0.0491 | - | | 6.4790 | 81150 | 0.0 | - | | 6.4830 | 81200 | 0.0001 | - | | 6.4870 | 81250 | 0.0001 | - | | 6.4910 | 81300 | 0.0247 | - | | 6.4950 | 81350 | 0.0 | - | | 6.4990 | 81400 | 0.0001 | - | | 6.5030 | 81450 | 0.0001 | - | | 6.5070 | 81500 | 0.0001 | - | | 6.5110 | 81550 | 0.0001 | - | | 6.5150 | 81600 | 0.0001 | - | | 6.5190 | 81650 | 0.0246 | - | | 6.5230 | 81700 | 0.0246 | - | | 6.5269 | 81750 | 0.0001 | - | | 6.5309 | 81800 | 0.0001 | - | | 6.5349 | 81850 | 0.0 | - | | 6.5389 | 81900 | 0.0 | - | | 6.5429 | 81950 | 0.0247 | - | | 6.5469 | 82000 | 0.0248 | - | | 6.5509 | 82050 | 0.0001 | - | | 6.5549 | 82100 | 0.0 | - | | 6.5589 | 82150 | 0.0001 | - | | 6.5629 | 82200 | 0.0001 | - | | 6.5669 | 82250 | 0.0001 | - | | 6.5709 | 82300 | 0.0 | - | | 6.5749 | 82350 | 0.0001 | - | | 6.5788 | 82400 | 0.0001 | - | | 6.5828 | 82450 | 0.0 | - | | 6.5868 | 82500 | 0.0001 | - | | 6.5908 | 82550 | 0.0001 | - | | 6.5948 | 82600 | 0.0001 | - | | 6.5988 | 82650 | 0.0001 | - | | 6.6028 | 82700 | 0.0001 | - | | 6.6068 | 82750 | 0.0001 | - | | 6.6108 | 82800 | 0.0 | - | | 6.6148 | 82850 | 0.0 | - | | 6.6188 | 82900 | 0.0001 | - | | 6.6228 | 82950 | 0.0244 | - | | 6.6267 | 83000 | 0.0001 | - | | 6.6307 | 83050 | 0.0247 | - | | 6.6347 | 83100 | 0.0 | - | | 6.6387 | 83150 | 0.0001 | - | | 6.6427 | 83200 | 0.0246 | - | | 6.6467 | 83250 | 0.0001 | - | | 6.6507 | 83300 | 0.0001 | - | | 6.6547 | 83350 | 0.0 | - | | 6.6587 | 83400 | 0.0 | - | | 6.6627 | 83450 | 0.0 | - | | 6.6667 | 83500 | 0.0 | - | | 6.6707 | 83550 | 0.0 | - | | 6.6747 | 83600 | 0.0001 | - | | 6.6786 | 83650 | 0.0001 | - | | 6.6826 | 83700 | 0.0 | - | | 6.6866 | 83750 | 0.0001 | - | | 6.6906 | 83800 | 0.0 | - | | 6.6946 | 83850 | 0.0001 | - | | 6.6986 | 83900 | 0.0001 | - | | 6.7026 | 83950 | 0.0001 | - | | 6.7066 | 84000 | 0.025 | - | | 6.7106 | 84050 | 0.0001 | - | | 6.7146 | 84100 | 0.0 | - | | 6.7186 | 84150 | 0.0 | - | | 6.7226 | 84200 | 0.0 | - | | 6.7265 | 84250 | 0.0001 | - | | 6.7305 | 84300 | 0.0246 | - | | 6.7345 | 84350 | 0.0001 | - | | 6.7385 | 84400 | 0.0251 | - | | 6.7425 | 84450 | 0.0 | - | | 6.7465 | 84500 | 0.0 | - | | 6.7505 | 84550 | 0.0001 | - | | 6.7545 | 84600 | 0.0 | - | | 6.7585 | 84650 | 0.0248 | - | | 6.7625 | 84700 | 0.0 | - | | 6.7665 | 84750 | 0.0001 | - | | 6.7705 | 84800 | 0.0 | - | | 6.7745 | 84850 | 0.0247 | - | | 6.7784 | 84900 | 0.0 | - | | 6.7824 | 84950 | 0.0001 | - | | 6.7864 | 85000 | 0.0 | - | | 6.7904 | 85050 | 0.0 | - | | 6.7944 | 85100 | 0.0 | - | | 6.7984 | 85150 | 0.0002 | - | | 6.8024 | 85200 | 0.0 | - | | 6.8064 | 85250 | 0.0001 | - | | 6.8104 | 85300 | 0.0001 | - | | 6.8144 | 85350 | 0.0 | - | | 6.8184 | 85400 | 0.0001 | - | | 6.8224 | 85450 | 0.0001 | - | | 6.8263 | 85500 | 0.0 | - | | 6.8303 | 85550 | 0.0001 | - | | 6.8343 | 85600 | 0.0 | - | | 6.8383 | 85650 | 0.0 | - | | 6.8423 | 85700 | 0.0 | - | | 6.8463 | 85750 | 0.0248 | - | | 6.8503 | 85800 | 0.0 | - | | 6.8543 | 85850 | 0.0001 | - | | 6.8583 | 85900 | 0.0 | - | | 6.8623 | 85950 | 0.0 | - | | 6.8663 | 86000 | 0.0001 | - | | 6.8703 | 86050 | 0.0 | - | | 6.8743 | 86100 | 0.0001 | - | | 6.8782 | 86150 | 0.0001 | - | | 6.8822 | 86200 | 0.0 | - | | 6.8862 | 86250 | 0.0 | - | | 6.8902 | 86300 | 0.0 | - | | 6.8942 | 86350 | 0.0 | - | | 6.8982 | 86400 | 0.0 | - | | 6.9022 | 86450 | 0.0001 | - | | 6.9062 | 86500 | 0.0 | - | | 6.9102 | 86550 | 0.0 | - | | 6.9142 | 86600 | 0.0 | - | | 6.9182 | 86650 | 0.0 | - | | 6.9222 | 86700 | 0.0 | - | | 6.9261 | 86750 | 0.0001 | - | | 6.9301 | 86800 | 0.0246 | - | | 6.9341 | 86850 | 0.025 | - | | 6.9381 | 86900 | 0.0001 | - | | 6.9421 | 86950 | 0.0 | - | | 6.9461 | 87000 | 0.0 | - | | 6.9501 | 87050 | 0.0 | - | | 6.9541 | 87100 | 0.0001 | - | | 6.9581 | 87150 | 0.0001 | - | | 6.9621 | 87200 | 0.0 | - | | 6.9661 | 87250 | 0.0 | - | | 6.9701 | 87300 | 0.0001 | - | | 6.9741 | 87350 | 0.0081 | - | | 6.9780 | 87400 | 0.0 | - | | 6.9820 | 87450 | 0.0469 | - | | 6.9860 | 87500 | 0.0 | - | | 6.9900 | 87550 | 0.0 | - | | 6.9940 | 87600 | 0.0 | - | | 6.9980 | 87650 | 0.0 | - | | **7.0** | **87675** | **-** | **0.039** | | 7.0020 | 87700 | 0.0248 | - | | 7.0060 | 87750 | 0.0246 | - | | 7.0100 | 87800 | 0.0 | - | | 7.0140 | 87850 | 0.0001 | - | | 7.0180 | 87900 | 0.0001 | - | | 7.0220 | 87950 | 0.0 | - | | 7.0259 | 88000 | 0.0 | - | | 7.0299 | 88050 | 0.0001 | - | | 7.0339 | 88100 | 0.0001 | - | | 7.0379 | 88150 | 0.0 | - | | 7.0419 | 88200 | 0.0034 | - | | 7.0459 | 88250 | 0.0001 | - | | 7.0499 | 88300 | 0.004 | - | | 7.0539 | 88350 | 0.0 | - | | 7.0579 | 88400 | 0.0 | - | | 7.0619 | 88450 | 0.0001 | - | | 7.0659 | 88500 | 0.0249 | - | | 7.0699 | 88550 | 0.0 | - | | 7.0739 | 88600 | 0.0 | - | | 7.0778 | 88650 | 0.0001 | - | | 7.0818 | 88700 | 0.0 | - | | 7.0858 | 88750 | 0.0 | - | | 7.0898 | 88800 | 0.0 | - | | 7.0938 | 88850 | 0.0 | - | | 7.0978 | 88900 | 0.0 | - | | 7.1018 | 88950 | 0.0 | - | | 7.1058 | 89000 | 0.0 | - | | 7.1098 | 89050 | 0.0 | - | | 7.1138 | 89100 | 0.0 | - | | 7.1178 | 89150 | 0.0 | - | | 7.1218 | 89200 | 0.0001 | - | | 7.1257 | 89250 | 0.0024 | - | | 7.1297 | 89300 | 0.0 | - | | 7.1337 | 89350 | 0.0001 | - | | 7.1377 | 89400 | 0.0 | - | | 7.1417 | 89450 | 0.0 | - | | 7.1457 | 89500 | 0.0001 | - | | 7.1497 | 89550 | 0.0 | - | | 7.1537 | 89600 | 0.0 | - | | 7.1577 | 89650 | 0.0 | - | | 7.1617 | 89700 | 0.0001 | - | | 7.1657 | 89750 | 0.0001 | - | | 7.1697 | 89800 | 0.0 | - | | 7.1737 | 89850 | 0.0 | - | | 7.1776 | 89900 | 0.0135 | - | | 7.1816 | 89950 | 0.0001 | - | | 7.1856 | 90000 | 0.0043 | - | | 7.1896 | 90050 | 0.0072 | - | | 7.1936 | 90100 | 0.0 | - | | 7.1976 | 90150 | 0.0 | - | | 7.2016 | 90200 | 0.0 | - | | 7.2056 | 90250 | 0.0247 | - | | 7.2096 | 90300 | 0.0 | - | | 7.2136 | 90350 | 0.0 | - | | 7.2176 | 90400 | 0.0 | - | | 7.2216 | 90450 | 0.0 | - | | 7.2255 | 90500 | 0.0001 | - | | 7.2295 | 90550 | 0.0 | - | | 7.2335 | 90600 | 0.0363 | - | | 7.2375 | 90650 | 0.0 | - | | 7.2415 | 90700 | 0.0 | - | | 7.2455 | 90750 | 0.0 | - | | 7.2495 | 90800 | 0.0 | - | | 7.2535 | 90850 | 0.0 | - | | 7.2575 | 90900 | 0.0 | - | | 7.2615 | 90950 | 0.0138 | - | | 7.2655 | 91000 | 0.0 | - | | 7.2695 | 91050 | 0.0 | - | | 7.2735 | 91100 | 0.0 | - | | 7.2774 | 91150 | 0.0 | - | | 7.2814 | 91200 | 0.0252 | - | | 7.2854 | 91250 | 0.0 | - | | 7.2894 | 91300 | 0.0 | - | | 7.2934 | 91350 | 0.0 | - | | 7.2974 | 91400 | 0.0105 | - | | 7.3014 | 91450 | 0.0244 | - | | 7.3054 | 91500 | 0.0 | - | | 7.3094 | 91550 | 0.0 | - | | 7.3134 | 91600 | 0.0247 | - | | 7.3174 | 91650 | 0.0 | - | | 7.3214 | 91700 | 0.0001 | - | | 7.3253 | 91750 | 0.0 | - | | 7.3293 | 91800 | 0.0 | - | | 7.3333 | 91850 | 0.0189 | - | | 7.3373 | 91900 | 0.0 | - | | 7.3413 | 91950 | 0.0246 | - | | 7.3453 | 92000 | 0.0 | - | | 7.3493 | 92050 | 0.0 | - | | 7.3533 | 92100 | 0.0 | - | | 7.3573 | 92150 | 0.0 | - | | 7.3613 | 92200 | 0.0 | - | | 7.3653 | 92250 | 0.0247 | - | | 7.3693 | 92300 | 0.0122 | - | | 7.3733 | 92350 | 0.0 | - | | 7.3772 | 92400 | 0.0 | - | | 7.3812 | 92450 | 0.0022 | - | | 7.3852 | 92500 | 0.0 | - | | 7.3892 | 92550 | 0.0001 | - | | 7.3932 | 92600 | 0.0 | - | | 7.3972 | 92650 | 0.0 | - | | 7.4012 | 92700 | 0.0 | - | | 7.4052 | 92750 | 0.0032 | - | | 7.4092 | 92800 | 0.0001 | - | | 7.4132 | 92850 | 0.0037 | - | | 7.4172 | 92900 | 0.0001 | - | | 7.4212 | 92950 | 0.0028 | - | | 7.4251 | 93000 | 0.0001 | - | | 7.4291 | 93050 | 0.0 | - | | 7.4331 | 93100 | 0.0039 | - | | 7.4371 | 93150 | 0.0036 | - | | 7.4411 | 93200 | 0.0 | - | | 7.4451 | 93250 | 0.0 | - | | 7.4491 | 93300 | 0.0 | - | | 7.4531 | 93350 | 0.0 | - | | 7.4571 | 93400 | 0.0001 | - | | 7.4611 | 93450 | 0.0091 | - | | 7.4651 | 93500 | 0.0 | - | | 7.4691 | 93550 | 0.0 | - | | 7.4731 | 93600 | 0.0275 | - | | 7.4770 | 93650 | 0.0 | - | | 7.4810 | 93700 | 0.0 | - | | 7.4850 | 93750 | 0.0035 | - | | 7.4890 | 93800 | 0.0246 | - | | 7.4930 | 93850 | 0.0025 | - | | 7.4970 | 93900 | 0.0 | - | | 7.5010 | 93950 | 0.0 | - | | 7.5050 | 94000 | 0.0 | - | | 7.5090 | 94050 | 0.0 | - | | 7.5130 | 94100 | 0.0 | - | | 7.5170 | 94150 | 0.0 | - | | 7.5210 | 94200 | 0.0023 | - | | 7.5250 | 94250 | 0.0 | - | | 7.5289 | 94300 | 0.0 | - | | 7.5329 | 94350 | 0.0 | - | | 7.5369 | 94400 | 0.0 | - | | 7.5409 | 94450 | 0.0027 | - | | 7.5449 | 94500 | 0.0028 | - | | 7.5489 | 94550 | 0.0247 | - | | 7.5529 | 94600 | 0.0 | - | | 7.5569 | 94650 | 0.0 | - | | 7.5609 | 94700 | 0.0 | - | | 7.5649 | 94750 | 0.0036 | - | | 7.5689 | 94800 | 0.0 | - | | 7.5729 | 94850 | 0.0 | - | | 7.5768 | 94900 | 0.0 | - | | 7.5808 | 94950 | 0.0 | - | | 7.5848 | 95000 | 0.0 | - | | 7.5888 | 95050 | 0.0 | - | | 7.5928 | 95100 | 0.0001 | - | | 7.5968 | 95150 | 0.0 | - | | 7.6008 | 95200 | 0.0 | - | | 7.6048 | 95250 | 0.0001 | - | | 7.6088 | 95300 | 0.0 | - | | 7.6128 | 95350 | 0.0 | - | | 7.6168 | 95400 | 0.0028 | - | | 7.6208 | 95450 | 0.0119 | - | | 7.6248 | 95500 | 0.0028 | - | | 7.6287 | 95550 | 0.0 | - | | 7.6327 | 95600 | 0.0001 | - | | 7.6367 | 95650 | 0.0 | - | | 7.6407 | 95700 | 0.0318 | - | | 7.6447 | 95750 | 0.0037 | - | | 7.6487 | 95800 | 0.0035 | - | | 7.6527 | 95850 | 0.0089 | - | | 7.6567 | 95900 | 0.0 | - | | 7.6607 | 95950 | 0.006 | - | | 7.6647 | 96000 | 0.0 | - | | 7.6687 | 96050 | 0.0 | - | | 7.6727 | 96100 | 0.0 | - | | 7.6766 | 96150 | 0.0 | - | | 7.6806 | 96200 | 0.0 | - | | 7.6846 | 96250 | 0.0 | - | | 7.6886 | 96300 | 0.0105 | - | | 7.6926 | 96350 | 0.0 | - | | 7.6966 | 96400 | 0.0 | - | | 7.7006 | 96450 | 0.0 | - | | 7.7046 | 96500 | 0.0 | - | | 7.7086 | 96550 | 0.0 | - | | 7.7126 | 96600 | 0.0 | - | | 7.7166 | 96650 | 0.0024 | - | | 7.7206 | 96700 | 0.0001 | - | | 7.7246 | 96750 | 0.0 | - | | 7.7285 | 96800 | 0.0123 | - | | 7.7325 | 96850 | 0.0 | - | | 7.7365 | 96900 | 0.0031 | - | | 7.7405 | 96950 | 0.0 | - | | 7.7445 | 97000 | 0.0025 | - | | 7.7485 | 97050 | 0.0 | - | | 7.7525 | 97100 | 0.0 | - | | 7.7565 | 97150 | 0.0 | - | | 7.7605 | 97200 | 0.0022 | - | | 7.7645 | 97250 | 0.0251 | - | | 7.7685 | 97300 | 0.002 | - | | 7.7725 | 97350 | 0.0118 | - | | 7.7764 | 97400 | 0.0019 | - | | 7.7804 | 97450 | 0.0001 | - | | 7.7844 | 97500 | 0.0123 | - | | 7.7884 | 97550 | 0.0 | - | | 7.7924 | 97600 | 0.0 | - | | 7.7964 | 97650 | 0.0 | - | | 7.8004 | 97700 | 0.0097 | - | | 7.8044 | 97750 | 0.0 | - | | 7.8084 | 97800 | 0.0 | - | | 7.8124 | 97850 | 0.0 | - | | 7.8164 | 97900 | 0.0001 | - | | 7.8204 | 97950 | 0.0001 | - | | 7.8244 | 98000 | 0.0251 | - | | 7.8283 | 98050 | 0.0 | - | | 7.8323 | 98100 | 0.009 | - | | 7.8363 | 98150 | 0.0246 | - | | 7.8403 | 98200 | 0.0 | - | | 7.8443 | 98250 | 0.0 | - | | 7.8483 | 98300 | 0.0 | - | | 7.8523 | 98350 | 0.0 | - | | 7.8563 | 98400 | 0.0 | - | | 7.8603 | 98450 | 0.0001 | - | | 7.8643 | 98500 | 0.0 | - | | 7.8683 | 98550 | 0.0018 | - | | 7.8723 | 98600 | 0.0 | - | | 7.8762 | 98650 | 0.0001 | - | | 7.8802 | 98700 | 0.0 | - | | 7.8842 | 98750 | 0.0 | - | | 7.8882 | 98800 | 0.0024 | - | | 7.8922 | 98850 | 0.0 | - | | 7.8962 | 98900 | 0.0 | - | | 7.9002 | 98950 | 0.0 | - | | 7.9042 | 99000 | 0.0027 | - | | 7.9082 | 99050 | 0.0027 | - | | 7.9122 | 99100 | 0.0 | - | | 7.9162 | 99150 | 0.0 | - | | 7.9202 | 99200 | 0.0 | - | | 7.9242 | 99250 | 0.0 | - | | 7.9281 | 99300 | 0.0138 | - | | 7.9321 | 99350 | 0.0 | - | | 7.9361 | 99400 | 0.0 | - | | 7.9401 | 99450 | 0.0 | - | | 7.9441 | 99500 | 0.0001 | - | | 7.9481 | 99550 | 0.0019 | - | | 7.9521 | 99600 | 0.0 | - | | 7.9561 | 99650 | 0.0 | - | | 7.9601 | 99700 | 0.0001 | - | | 7.9641 | 99750 | 0.0 | - | | 7.9681 | 99800 | 0.0 | - | | 7.9721 | 99850 | 0.0016 | - | | 7.9760 | 99900 | 0.0001 | - | | 7.9800 | 99950 | 0.0265 | - | | 7.9840 | 100000 | 0.0 | - | | 7.9880 | 100050 | 0.0 | - | | 7.9920 | 100100 | 0.0 | - | | 7.9960 | 100150 | 0.0127 | - | | 8.0 | 100200 | 0.0001 | 0.0405 | | 8.0040 | 100250 | 0.0247 | - | | 8.0080 | 100300 | 0.025 | - | | 8.0120 | 100350 | 0.0 | - | | 8.0160 | 100400 | 0.0072 | - | | 8.0200 | 100450 | 0.0 | - | | 8.0240 | 100500 | 0.0 | - | | 8.0279 | 100550 | 0.0 | - | | 8.0319 | 100600 | 0.0 | - | | 8.0359 | 100650 | 0.0251 | - | | 8.0399 | 100700 | 0.0 | - | | 8.0439 | 100750 | 0.0 | - | | 8.0479 | 100800 | 0.0042 | - | | 8.0519 | 100850 | 0.0036 | - | | 8.0559 | 100900 | 0.0 | - | | 8.0599 | 100950 | 0.0 | - | | 8.0639 | 101000 | 0.0 | - | | 8.0679 | 101050 | 0.0001 | - | | 8.0719 | 101100 | 0.0 | - | | 8.0758 | 101150 | 0.0 | - | | 8.0798 | 101200 | 0.0116 | - | | 8.0838 | 101250 | 0.0027 | - | | 8.0878 | 101300 | 0.0 | - | | 8.0918 | 101350 | 0.0 | - | | 8.0958 | 101400 | 0.0032 | - | | 8.0998 | 101450 | 0.0 | - | | 8.1038 | 101500 | 0.0 | - | | 8.1078 | 101550 | 0.0 | - | | 8.1118 | 101600 | 0.0097 | - | | 8.1158 | 101650 | 0.0 | - | | 8.1198 | 101700 | 0.0105 | - | | 8.1238 | 101750 | 0.0 | - | | 8.1277 | 101800 | 0.0026 | - | | 8.1317 | 101850 | 0.0 | - | | 8.1357 | 101900 | 0.0 | - | | 8.1397 | 101950 | 0.0 | - | | 8.1437 | 102000 | 0.0 | - | | 8.1477 | 102050 | 0.0 | - | | 8.1517 | 102100 | 0.0 | - | | 8.1557 | 102150 | 0.0 | - | | 8.1597 | 102200 | 0.0 | - | | 8.1637 | 102250 | 0.0 | - | | 8.1677 | 102300 | 0.0 | - | | 8.1717 | 102350 | 0.0 | - | | 8.1756 | 102400 | 0.0028 | - | | 8.1796 | 102450 | 0.0 | - | | 8.1836 | 102500 | 0.0037 | - | | 8.1876 | 102550 | 0.0065 | - | | 8.1916 | 102600 | 0.0 | - | | 8.1956 | 102650 | 0.0001 | - | | 8.1996 | 102700 | 0.0251 | - | | 8.2036 | 102750 | 0.0 | - | | 8.2076 | 102800 | 0.0 | - | | 8.2116 | 102850 | 0.0 | - | | 8.2156 | 102900 | 0.0 | - | | 8.2196 | 102950 | 0.0023 | - | | 8.2236 | 103000 | 0.0023 | - | | 8.2275 | 103050 | 0.0 | - | | 8.2315 | 103100 | 0.0246 | - | | 8.2355 | 103150 | 0.0 | - | | 8.2395 | 103200 | 0.0 | - | | 8.2435 | 103250 | 0.0 | - | | 8.2475 | 103300 | 0.0 | - | | 8.2515 | 103350 | 0.0253 | - | | 8.2555 | 103400 | 0.0 | - | | 8.2595 | 103450 | 0.0148 | - | | 8.2635 | 103500 | 0.0 | - | | 8.2675 | 103550 | 0.0 | - | | 8.2715 | 103600 | 0.0001 | - | | 8.2754 | 103650 | 0.0271 | - | | 8.2794 | 103700 | 0.0 | - | | 8.2834 | 103750 | 0.0 | - | | 8.2874 | 103800 | 0.0 | - | | 8.2914 | 103850 | 0.0 | - | | 8.2954 | 103900 | 0.0108 | - | | 8.2994 | 103950 | 0.0245 | - | | 8.3034 | 104000 | 0.0 | - | | 8.3074 | 104050 | 0.0248 | - | | 8.3114 | 104100 | 0.0 | - | | 8.3154 | 104150 | 0.0 | - | | 8.3194 | 104200 | 0.0 | - | | 8.3234 | 104250 | 0.0001 | - | | 8.3273 | 104300 | 0.0 | - | | 8.3313 | 104350 | 0.0098 | - | | 8.3353 | 104400 | 0.0 | - | | 8.3393 | 104450 | 0.0247 | - | | 8.3433 | 104500 | 0.0001 | - | | 8.3473 | 104550 | 0.0 | - | | 8.3513 | 104600 | 0.0 | - | | 8.3553 | 104650 | 0.0001 | - | | 8.3593 | 104700 | 0.0 | - | | 8.3633 | 104750 | 0.0247 | - | | 8.3673 | 104800 | 0.0 | - | | 8.3713 | 104850 | 0.0001 | - | | 8.3752 | 104900 | 0.0001 | - | | 8.3792 | 104950 | 0.0 | - | | 8.3832 | 105000 | 0.0 | - | | 8.3872 | 105050 | 0.0 | - | | 8.3912 | 105100 | 0.0 | - | | 8.3952 | 105150 | 0.0 | - | | 8.3992 | 105200 | 0.0001 | - | | 8.4032 | 105250 | 0.0 | - | | 8.4072 | 105300 | 0.0001 | - | | 8.4112 | 105350 | 0.0001 | - | | 8.4152 | 105400 | 0.0 | - | | 8.4192 | 105450 | 0.0 | - | | 8.4232 | 105500 | 0.025 | - | | 8.4271 | 105550 | 0.0 | - | | 8.4311 | 105600 | 0.0 | - | | 8.4351 | 105650 | 0.0 | - | | 8.4391 | 105700 | 0.0 | - | | 8.4431 | 105750 | 0.0001 | - | | 8.4471 | 105800 | 0.0 | - | | 8.4511 | 105850 | 0.0 | - | | 8.4551 | 105900 | 0.0001 | - | | 8.4591 | 105950 | 0.0246 | - | | 8.4631 | 106000 | 0.0 | - | | 8.4671 | 106050 | 0.0 | - | | 8.4711 | 106100 | 0.0246 | - | | 8.4750 | 106150 | 0.0001 | - | | 8.4790 | 106200 | 0.0 | - | | 8.4830 | 106250 | 0.0 | - | | 8.4870 | 106300 | 0.0246 | - | | 8.4910 | 106350 | 0.0 | - | | 8.4950 | 106400 | 0.0 | - | | 8.4990 | 106450 | 0.0001 | - | | 8.5030 | 106500 | 0.0001 | - | | 8.5070 | 106550 | 0.0 | - | | 8.5110 | 106600 | 0.0 | - | | 8.5150 | 106650 | 0.0001 | - | | 8.5190 | 106700 | 0.0 | - | | 8.5230 | 106750 | 0.0 | - | | 8.5269 | 106800 | 0.0 | - | | 8.5309 | 106850 | 0.0001 | - | | 8.5349 | 106900 | 0.0 | - | | 8.5389 | 106950 | 0.0 | - | | 8.5429 | 107000 | 0.0001 | - | | 8.5469 | 107050 | 0.0 | - | | 8.5509 | 107100 | 0.0 | - | | 8.5549 | 107150 | 0.0 | - | | 8.5589 | 107200 | 0.0 | - | | 8.5629 | 107250 | 0.0001 | - | | 8.5669 | 107300 | 0.0 | - | | 8.5709 | 107350 | 0.0 | - | | 8.5749 | 107400 | 0.0001 | - | | 8.5788 | 107450 | 0.0251 | - | | 8.5828 | 107500 | 0.0 | - | | 8.5868 | 107550 | 0.0 | - | | 8.5908 | 107600 | 0.0001 | - | | 8.5948 | 107650 | 0.0 | - | | 8.5988 | 107700 | 0.0 | - | | 8.6028 | 107750 | 0.0 | - | | 8.6068 | 107800 | 0.0001 | - | | 8.6108 | 107850 | 0.0 | - | | 8.6148 | 107900 | 0.0 | - | | 8.6188 | 107950 | 0.0245 | - | | 8.6228 | 108000 | 0.0 | - | | 8.6267 | 108050 | 0.0 | - | | 8.6307 | 108100 | 0.0249 | - | | 8.6347 | 108150 | 0.0 | - | | 8.6387 | 108200 | 0.0246 | - | | 8.6427 | 108250 | 0.0 | - | | 8.6467 | 108300 | 0.0001 | - | | 8.6507 | 108350 | 0.0001 | - | | 8.6547 | 108400 | 0.0001 | - | | 8.6587 | 108450 | 0.0 | - | | 8.6627 | 108500 | 0.0 | - | | 8.6667 | 108550 | 0.0 | - | | 8.6707 | 108600 | 0.0 | - | | 8.6747 | 108650 | 0.0 | - | | 8.6786 | 108700 | 0.0 | - | | 8.6826 | 108750 | 0.0 | - | | 8.6866 | 108800 | 0.0001 | - | | 8.6906 | 108850 | 0.0 | - | | 8.6946 | 108900 | 0.0 | - | | 8.6986 | 108950 | 0.0 | - | | 8.7026 | 109000 | 0.0 | - | | 8.7066 | 109050 | 0.0248 | - | | 8.7106 | 109100 | 0.0001 | - | | 8.7146 | 109150 | 0.0 | - | | 8.7186 | 109200 | 0.0 | - | | 8.7226 | 109250 | 0.0 | - | | 8.7265 | 109300 | 0.0246 | - | | 8.7305 | 109350 | 0.0001 | - | | 8.7345 | 109400 | 0.0 | - | | 8.7385 | 109450 | 0.025 | - | | 8.7425 | 109500 | 0.0 | - | | 8.7465 | 109550 | 0.0 | - | | 8.7505 | 109600 | 0.0 | - | | 8.7545 | 109650 | 0.0 | - | | 8.7585 | 109700 | 0.025 | - | | 8.7625 | 109750 | 0.0001 | - | | 8.7665 | 109800 | 0.0001 | - | | 8.7705 | 109850 | 0.0248 | - | | 8.7745 | 109900 | 0.0001 | - | | 8.7784 | 109950 | 0.0 | - | | 8.7824 | 110000 | 0.0 | - | | 8.7864 | 110050 | 0.0 | - | | 8.7904 | 110100 | 0.0 | - | | 8.7944 | 110150 | 0.0 | - | | 8.7984 | 110200 | 0.0001 | - | | 8.8024 | 110250 | 0.0 | - | | 8.8064 | 110300 | 0.0 | - | | 8.8104 | 110350 | 0.0 | - | | 8.8144 | 110400 | 0.0 | - | | 8.8184 | 110450 | 0.0001 | - | | 8.8224 | 110500 | 0.0001 | - | | 8.8263 | 110550 | 0.0 | - | | 8.8303 | 110600 | 0.0001 | - | | 8.8343 | 110650 | 0.0 | - | | 8.8383 | 110700 | 0.0 | - | | 8.8423 | 110750 | 0.0 | - | | 8.8463 | 110800 | 0.0247 | - | | 8.8503 | 110850 | 0.0 | - | | 8.8543 | 110900 | 0.0 | - | | 8.8583 | 110950 | 0.0 | - | | 8.8623 | 111000 | 0.0 | - | | 8.8663 | 111050 | 0.0001 | - | | 8.8703 | 111100 | 0.0 | - | | 8.8743 | 111150 | 0.0001 | - | | 8.8782 | 111200 | 0.0001 | - | | 8.8822 | 111250 | 0.0 | - | | 8.8862 | 111300 | 0.0 | - | | 8.8902 | 111350 | 0.0001 | - | | 8.8942 | 111400 | 0.0 | - | | 8.8982 | 111450 | 0.0 | - | | 8.9022 | 111500 | 0.0 | - | | 8.9062 | 111550 | 0.0 | - | | 8.9102 | 111600 | 0.0 | - | | 8.9142 | 111650 | 0.0 | - | | 8.9182 | 111700 | 0.0 | - | | 8.9222 | 111750 | 0.0 | - | | 8.9261 | 111800 | 0.0247 | - | | 8.9301 | 111850 | 0.0 | - | | 8.9341 | 111900 | 0.0248 | - | | 8.9381 | 111950 | 0.0 | - | | 8.9421 | 112000 | 0.0 | - | | 8.9461 | 112050 | 0.0 | - | | 8.9501 | 112100 | 0.0 | - | | 8.9541 | 112150 | 0.0 | - | | 8.9581 | 112200 | 0.0 | - | | 8.9621 | 112250 | 0.0001 | - | | 8.9661 | 112300 | 0.0 | - | | 8.9701 | 112350 | 0.0001 | - | | 8.9741 | 112400 | 0.0001 | - | | 8.9780 | 112450 | 0.0247 | - | | 8.9820 | 112500 | 0.0496 | - | | 8.9860 | 112550 | 0.0 | - | | 8.9900 | 112600 | 0.0001 | - | | 8.9940 | 112650 | 0.0 | - | | 8.9980 | 112700 | 0.0 | - | | 9.0 | 112725 | - | 0.0579 | | 9.0020 | 112750 | 0.0493 | - | | 9.0060 | 112800 | 0.0 | - | | 9.0100 | 112850 | 0.0001 | - | | 9.0140 | 112900 | 0.0001 | - | | 9.0180 | 112950 | 0.0 | - | | 9.0220 | 113000 | 0.0 | - | | 9.0259 | 113050 | 0.0 | - | | 9.0299 | 113100 | 0.0 | - | | 9.0339 | 113150 | 0.0001 | - | | 9.0379 | 113200 | 0.0 | - | | 9.0419 | 113250 | 0.0 | - | | 9.0459 | 113300 | 0.0 | - | | 9.0499 | 113350 | 0.0 | - | | 9.0539 | 113400 | 0.0 | - | | 9.0579 | 113450 | 0.0 | - | | 9.0619 | 113500 | 0.0 | - | | 9.0659 | 113550 | 0.0246 | - | | 9.0699 | 113600 | 0.0 | - | | 9.0739 | 113650 | 0.0 | - | | 9.0778 | 113700 | 0.0001 | - | | 9.0818 | 113750 | 0.0001 | - | | 9.0858 | 113800 | 0.0 | - | | 9.0898 | 113850 | 0.0001 | - | | 9.0938 | 113900 | 0.0 | - | | 9.0978 | 113950 | 0.0 | - | | 9.1018 | 114000 | 0.0 | - | | 9.1058 | 114050 | 0.0 | - | | 9.1098 | 114100 | 0.0 | - | | 9.1138 | 114150 | 0.0 | - | | 9.1178 | 114200 | 0.0 | - | | 9.1218 | 114250 | 0.0 | - | | 9.1257 | 114300 | 0.0001 | - | | 9.1297 | 114350 | 0.0 | - | | 9.1337 | 114400 | 0.0001 | - | | 9.1377 | 114450 | 0.0 | - | | 9.1417 | 114500 | 0.0 | - | | 9.1457 | 114550 | 0.0001 | - | | 9.1497 | 114600 | 0.0 | - | | 9.1537 | 114650 | 0.0 | - | | 9.1577 | 114700 | 0.0 | - | | 9.1617 | 114750 | 0.0001 | - | | 9.1657 | 114800 | 0.0 | - | | 9.1697 | 114850 | 0.0 | - | | 9.1737 | 114900 | 0.0 | - | | 9.1776 | 114950 | 0.0247 | - | | 9.1816 | 115000 | 0.0001 | - | | 9.1856 | 115050 | 0.0001 | - | | 9.1896 | 115100 | 0.0001 | - | | 9.1936 | 115150 | 0.0 | - | | 9.1976 | 115200 | 0.0 | - | | 9.2016 | 115250 | 0.0 | - | | 9.2056 | 115300 | 0.0247 | - | | 9.2096 | 115350 | 0.0 | - | | 9.2136 | 115400 | 0.0 | - | | 9.2176 | 115450 | 0.0 | - | | 9.2216 | 115500 | 0.0 | - | | 9.2255 | 115550 | 0.0 | - | | 9.2295 | 115600 | 0.0245 | - | | 9.2335 | 115650 | 0.0248 | - | | 9.2375 | 115700 | 0.0 | - | | 9.2415 | 115750 | 0.0001 | - | | 9.2455 | 115800 | 0.0 | - | | 9.2495 | 115850 | 0.0 | - | | 9.2535 | 115900 | 0.0 | - | | 9.2575 | 115950 | 0.0246 | - | | 9.2615 | 116000 | 0.0 | - | | 9.2655 | 116050 | 0.0 | - | | 9.2695 | 116100 | 0.0 | - | | 9.2735 | 116150 | 0.0 | - | | 9.2774 | 116200 | 0.0 | - | | 9.2814 | 116250 | 0.0246 | - | | 9.2854 | 116300 | 0.0 | - | | 9.2894 | 116350 | 0.0 | - | | 9.2934 | 116400 | 0.0247 | - | | 9.2974 | 116450 | 0.0245 | - | | 9.3014 | 116500 | 0.0 | - | | 9.3054 | 116550 | 0.0 | - | | 9.3094 | 116600 | 0.0 | - | | 9.3134 | 116650 | 0.0244 | - | | 9.3174 | 116700 | 0.0001 | - | | 9.3214 | 116750 | 0.0 | - | | 9.3253 | 116800 | 0.0001 | - | | 9.3293 | 116850 | 0.0232 | - | | 9.3333 | 116900 | 0.0192 | - | | 9.3373 | 116950 | 0.0246 | - | | 9.3413 | 117000 | 0.0 | - | | 9.3453 | 117050 | 0.0005 | - | | 9.3493 | 117100 | 0.0007 | - | | 9.3533 | 117150 | 0.0002 | - | | 9.3573 | 117200 | 0.0001 | - | | 9.3613 | 117250 | 0.0244 | - | | 9.3653 | 117300 | 0.0002 | - | | 9.3693 | 117350 | 0.0188 | - | | 9.3733 | 117400 | 0.0001 | - | | 9.3772 | 117450 | 0.0003 | - | | 9.3812 | 117500 | 0.001 | - | | 9.3852 | 117550 | 0.0 | - | | 9.3892 | 117600 | 0.0001 | - | | 9.3932 | 117650 | 0.0001 | - | | 9.3972 | 117700 | 0.0003 | - | | 9.4012 | 117750 | 0.0029 | - | | 9.4052 | 117800 | 0.0003 | - | | 9.4092 | 117850 | 0.0026 | - | | 9.4132 | 117900 | 0.0019 | - | | 9.4172 | 117950 | 0.0002 | - | | 9.4212 | 118000 | 0.0007 | - | | 9.4251 | 118050 | 0.0 | - | | 9.4291 | 118100 | 0.0019 | - | | 9.4331 | 118150 | 0.004 | - | | 9.4371 | 118200 | 0.001 | - | | 9.4411 | 118250 | 0.0016 | - | | 9.4451 | 118300 | 0.0028 | - | | 9.4491 | 118350 | 0.0001 | - | | 9.4531 | 118400 | 0.0 | - | | 9.4571 | 118450 | 0.0105 | - | | 9.4611 | 118500 | 0.0013 | - | | 9.4651 | 118550 | 0.0 | - | | 9.4691 | 118600 | 0.0221 | - | | 9.4731 | 118650 | 0.0001 | - | | 9.4770 | 118700 | 0.0008 | - | | 9.4810 | 118750 | 0.0001 | - | | 9.4850 | 118800 | 0.0214 | - | | 9.4890 | 118850 | 0.0001 | - | | 9.4930 | 118900 | 0.0018 | - | | 9.4970 | 118950 | 0.0011 | - | | 9.5010 | 119000 | 0.0001 | - | | 9.5050 | 119050 | 0.0009 | - | | 9.5090 | 119100 | 0.0004 | - | | 9.5130 | 119150 | 0.0004 | - | | 9.5170 | 119200 | 0.0034 | - | | 9.5210 | 119250 | 0.0016 | - | | 9.5250 | 119300 | 0.0006 | - | | 9.5289 | 119350 | 0.0 | - | | 9.5329 | 119400 | 0.0001 | - | | 9.5369 | 119450 | 0.0041 | - | | 9.5409 | 119500 | 0.0029 | - | | 9.5449 | 119550 | 0.0001 | - | | 9.5489 | 119600 | 0.0189 | - | | 9.5529 | 119650 | 0.0001 | - | | 9.5569 | 119700 | 0.0 | - | | 9.5609 | 119750 | 0.0 | - | | 9.5649 | 119800 | 0.0042 | - | | 9.5689 | 119850 | 0.0009 | - | | 9.5729 | 119900 | 0.0 | - | | 9.5768 | 119950 | 0.0 | - | | 9.5808 | 120000 | 0.0 | - | | 9.5848 | 120050 | 0.0007 | - | | 9.5888 | 120100 | 0.0009 | - | | 9.5928 | 120150 | 0.0006 | - | | 9.5968 | 120200 | 0.0001 | - | | 9.6008 | 120250 | 0.0001 | - | | 9.6048 | 120300 | 0.0007 | - | | 9.6088 | 120350 | 0.0001 | - | | 9.6128 | 120400 | 0.0025 | - | | 9.6168 | 120450 | 0.0136 | - | | 9.6208 | 120500 | 0.0011 | - | | 9.6248 | 120550 | 0.002 | - | | 9.6287 | 120600 | 0.001 | - | | 9.6327 | 120650 | 0.0008 | - | | 9.6367 | 120700 | 0.0298 | - | | 9.6407 | 120750 | 0.009 | - | | 9.6447 | 120800 | 0.0042 | - | | 9.6487 | 120850 | 0.0011 | - | | 9.6527 | 120900 | 0.0089 | - | | 9.6567 | 120950 | 0.0054 | - | | 9.6607 | 121000 | 0.0019 | - | | 9.6647 | 121050 | 0.0006 | - | | 9.6687 | 121100 | 0.0 | - | | 9.6727 | 121150 | 0.0 | - | | 9.6766 | 121200 | 0.0001 | - | | 9.6806 | 121250 | 0.0001 | - | | 9.6846 | 121300 | 0.0 | - | | 9.6886 | 121350 | 0.0128 | - | | 9.6926 | 121400 | 0.0 | - | | 9.6966 | 121450 | 0.0001 | - | | 9.7006 | 121500 | 0.0 | - | | 9.7046 | 121550 | 0.0007 | - | | 9.7086 | 121600 | 0.0001 | - | | 9.7126 | 121650 | 0.0001 | - | | 9.7166 | 121700 | 0.0021 | - | | 9.7206 | 121750 | 0.0001 | - | | 9.7246 | 121800 | 0.0207 | - | | 9.7285 | 121850 | 0.0001 | - | | 9.7325 | 121900 | 0.0032 | - | | 9.7365 | 121950 | 0.0008 | - | | 9.7405 | 122000 | 0.0038 | - | | 9.7445 | 122050 | 0.0005 | - | | 9.7485 | 122100 | 0.0002 | - | | 9.7525 | 122150 | 0.0005 | - | | 9.7565 | 122200 | 0.0043 | - | | 9.7605 | 122250 | 0.0003 | - | | 9.7645 | 122300 | 0.021 | - | | 9.7685 | 122350 | 0.0128 | - | | 9.7725 | 122400 | 0.0032 | - | | 9.7764 | 122450 | 0.0001 | - | | 9.7804 | 122500 | 0.0 | - | | 9.7844 | 122550 | 0.0119 | - | | 9.7884 | 122600 | 0.0 | - | | 9.7924 | 122650 | 0.0 | - | | 9.7964 | 122700 | 0.0 | - | | 9.8004 | 122750 | 0.0092 | - | | 9.8044 | 122800 | 0.0001 | - | | 9.8084 | 122850 | 0.0008 | - | | 9.8124 | 122900 | 0.0009 | - | | 9.8164 | 122950 | 0.0021 | - | | 9.8204 | 123000 | 0.0 | - | | 9.8244 | 123050 | 0.0174 | - | | 9.8283 | 123100 | 0.0001 | - | | 9.8323 | 123150 | 0.0095 | - | | 9.8363 | 123200 | 0.0183 | - | | 9.8403 | 123250 | 0.0001 | - | | 9.8443 | 123300 | 0.0002 | - | | 9.8483 | 123350 | 0.0 | - | | 9.8523 | 123400 | 0.0004 | - | | 9.8563 | 123450 | 0.0 | - | | 9.8603 | 123500 | 0.0001 | - | | 9.8643 | 123550 | 0.0028 | - | | 9.8683 | 123600 | 0.0 | - | | 9.8723 | 123650 | 0.0001 | - | | 9.8762 | 123700 | 0.0 | - | | 9.8802 | 123750 | 0.0004 | - | | 9.8842 | 123800 | 0.0035 | - | | 9.8882 | 123850 | 0.0001 | - | | 9.8922 | 123900 | 0.0 | - | | 9.8962 | 123950 | 0.0001 | - | | 9.9002 | 124000 | 0.0038 | - | | 9.9042 | 124050 | 0.0028 | - | | 9.9082 | 124100 | 0.0002 | - | | 9.9122 | 124150 | 0.0001 | - | | 9.9162 | 124200 | 0.0 | - | | 9.9202 | 124250 | 0.0005 | - | | 9.9242 | 124300 | 0.016 | - | | 9.9281 | 124350 | 0.0001 | - | | 9.9321 | 124400 | 0.0001 | - | | 9.9361 | 124450 | 0.0 | - | | 9.9401 | 124500 | 0.0009 | - | | 9.9441 | 124550 | 0.0 | - | | 9.9481 | 124600 | 0.0015 | - | | 9.9521 | 124650 | 0.0 | - | | 9.9561 | 124700 | 0.0 | - | | 9.9601 | 124750 | 0.0002 | - | | 9.9641 | 124800 | 0.0 | - | | 9.9681 | 124850 | 0.0028 | - | | 9.9721 | 124900 | 0.0004 | - | | 9.9760 | 124950 | 0.014 | - | | 9.9800 | 125000 | 0.0138 | - | | 9.9840 | 125050 | 0.0008 | - | | 9.9880 | 125100 | 0.0001 | - | | 9.9920 | 125150 | 0.0 | - | | 9.9960 | 125200 | 0.0136 | - | | 10.0 | 125250 | 0.0182 | 0.0777 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - SetFit: 1.0.3 - Sentence Transformers: 3.0.1 - Transformers: 4.39.0 - PyTorch: 2.4.0 - Datasets: 2.21.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

jeanviet/lora-jeanviet-flux1

jeanviet

2024-08-30T22:03:54Z

5

1

diffusers

[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-08-30T21:57:53Z

--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # lora jeanviet flux1 <Gallery /> ## Model description Flux Lora de Jeanviet ## Trigger words You should use `` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/jeanviet/lora-jeanviet-flux1/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-general-training](https://fal.ai/models/fal-ai/flux-lora-general-training).

nagthgr8/subject-gpt2

nagthgr8

2024-08-30T21:54:51Z

116

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T21:54:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jimmycarter/flux-training-seuss-lora-bsz-4

jimmycarter

2024-08-30T21:45:45Z

5

0

diffusers

[ "diffusers", "flux", "flux-diffusers", "text-to-image", "simpletuner", "lora", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-08-30T21:20:00Z

--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - lora - template:sd-lora inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'Dr. Seuss during a book signing event, seated at a table with an open book and pen in hand, his characteristic white beard, clear-rimmed glasses, and whimsical bow tie complementing his calm, attentive expression, all within the literary setting of a bookstore, reflecting his enduring connection with readers and the joy his work brought to many.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'Anime picture of famed author Dr. Seuss in a Studio Ghibli style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'Dr. Seuss in a leather jacket riding a Harley Davidson Motorcycle' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'Famous author Dr. Seuss holding a chainsaw while riding around on a unicycle, vintage TV still from the Dick Van Dyke show' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'A photograph of Dr. Seuss riding in a horse-drawn carriage' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png --- # flux-training-seuss-lora-bsz-4 This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` A photograph of Dr. Seuss riding in a horse-drawn carriage ``` ## Validation settings - CFG: `3.5` - CFG Rescale: `0.0` - Steps: `15` - Sampler: `None` - Seed: `42` - Resolution: `1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 0 - Training steps: 200 - Learning rate: 0.0008 - Effective batch size: 4 - Micro-batch size: 4 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: bf16 - Quantised: No - Xformers: Not used - LoRA Rank: 16 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### default_dataset_arb - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 3 - Resolution: 1.5 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### default_dataset - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_512 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_576 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.331776 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_640 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.4096 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_704 - Repeats: 100 - Total number of images: 4 - Total number of aspect buckets: 1 - Resolution: 0.495616 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_768 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_832 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.692224 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_896 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.802816 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_960 - Repeats: 100 - Total number of images: 3 - Total number of aspect buckets: 1 - Resolution: 0.9216 megapixels - Cropped: True - Crop style: random - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'jimmycarter/flux-training-seuss-lora-bsz-4' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "A photograph of Dr. Seuss riding in a horse-drawn carriage" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, num_inference_steps=15, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.5, ).images[0] image.save("output.png", format="PNG") ```

Sharan1712/llama2_7B_unnaturalcore_qdora_loftq_4bit_6c

Sharan1712

2024-08-30T21:42:44Z

77

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-08-30T21:40:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Rodrigo1771/BioLinkBERT-base-drugtemist-en-ner

Rodrigo1771

2024-08-30T21:42:27Z

6

0

null

[ "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:Rodrigo1771/drugtemist-en-ner", "base_model:michiyasunaga/BioLinkBERT-base", "base_model:finetune:michiyasunaga/BioLinkBERT-base", "license:apache-2.0", "model-index", "region:us" ]

token-classification

2024-08-30T21:24:16Z

--- license: apache-2.0 base_model: michiyasunaga/BioLinkBERT-base tags: - token-classification - generated_from_trainer datasets: - Rodrigo1771/drugtemist-en-ner metrics: - precision - recall - f1 - accuracy model-index: - name: output results: - task: name: Token Classification type: token-classification dataset: name: Rodrigo1771/drugtemist-en-ner type: Rodrigo1771/drugtemist-en-ner config: DrugTEMIST English NER split: validation args: DrugTEMIST English NER metrics: - name: Precision type: precision value: 0.9327102803738317 - name: Recall type: recall value: 0.9301025163094129 - name: F1 type: f1 value: 0.9314045730284647 - name: Accuracy type: accuracy value: 0.9986953367008066 ---  # output This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the Rodrigo1771/drugtemist-en-ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0056 - Precision: 0.9327 - Recall: 0.9301 - F1: 0.9314 - Accuracy: 0.9987 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 434 | 0.0057 | 0.8938 | 0.8938 | 0.8938 | 0.9981 | | 0.0182 | 2.0 | 868 | 0.0044 | 0.9024 | 0.9301 | 0.9160 | 0.9985 | | 0.0039 | 3.0 | 1302 | 0.0045 | 0.9129 | 0.9282 | 0.9205 | 0.9987 | | 0.0024 | 4.0 | 1736 | 0.0051 | 0.8821 | 0.9348 | 0.9077 | 0.9983 | | 0.0017 | 5.0 | 2170 | 0.0057 | 0.9251 | 0.9320 | 0.9285 | 0.9986 | | 0.0012 | 6.0 | 2604 | 0.0061 | 0.9001 | 0.9236 | 0.9117 | 0.9984 | | 0.0009 | 7.0 | 3038 | 0.0056 | 0.9327 | 0.9301 | 0.9314 | 0.9987 | | 0.0009 | 8.0 | 3472 | 0.0068 | 0.9118 | 0.9348 | 0.9231 | 0.9986 | | 0.0006 | 9.0 | 3906 | 0.0072 | 0.9267 | 0.9310 | 0.9289 | 0.9987 | | 0.0004 | 10.0 | 4340 | 0.0073 | 0.9192 | 0.9329 | 0.9260 | 0.9986 | ### Framework versions - Transformers 4.42.4 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

mradermacher/yaoi-v1-instruct-GGUF

mradermacher

2024-08-30T21:41:09Z

18

1

transformers

[ "transformers", "gguf", "code", "yaoi", "en", "base_model:Ichate/yaoi-v1-instruct", "base_model:quantized:Ichate/yaoi-v1-instruct", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-08-30T12:31:07Z

--- base_model: Ichate/yaoi-v1-instruct language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - code - yaoi --- ## About      static quants of https://huggingface.co/Ichate/yaoi-v1-instruct  weighted/imatrix quants are available at https://huggingface.co/mradermacher/yaoi-v1-instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q2_K.gguf) | Q2_K | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ3_XS.gguf) | IQ3_XS | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ3_S.gguf) | IQ3_S | 3.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q3_K_S.gguf) | Q3_K_S | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ3_M.gguf) | IQ3_M | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q3_K_L.gguf) | Q3_K_L | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.IQ4_XS.gguf) | IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q5_K_S.gguf) | Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q5_K_M.gguf) | Q5_K_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q6_K.gguf) | Q6_K | 5.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/yaoi-v1-instruct-GGUF/resolve/main/yaoi-v1-instruct.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

jeanviet/flux-lora

jeanviet

2024-08-30T21:35:51Z

6

0

diffusers

[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-08-30T21:35:45Z

--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: Portrait Photo license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # flux lora <Gallery /> ## Model description Flux Lora de Jeanviet ## Trigger words You should use `Portrait Photo` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/jeanviet/flux-lora/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-general-training](https://fal.ai/models/fal-ai/flux-lora-general-training).

RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf

RichardErkhov

2024-08-30T21:20:17Z

74

0

null

[ "gguf", "arxiv:2401.06466", "endpoints_compatible", "region:us" ]

null

2024-08-30T19:08:18Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) PersianMind-v1.0 - GGUF - Model creator: https://huggingface.co/universitytehran/ - Original model: https://huggingface.co/universitytehran/PersianMind-v1.0/ | Name | Quant method | Size | | ---- | ---- | ---- | | [PersianMind-v1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q2_K.gguf) | Q2_K | 2.4GB | | [PersianMind-v1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ3_XS.gguf) | IQ3_XS | 2.65GB | | [PersianMind-v1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ3_S.gguf) | IQ3_S | 2.79GB | | [PersianMind-v1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K_S.gguf) | Q3_K_S | 2.79GB | | [PersianMind-v1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ3_M.gguf) | IQ3_M | 2.95GB | | [PersianMind-v1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K.gguf) | Q3_K | 3.12GB | | [PersianMind-v1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K_M.gguf) | Q3_K_M | 3.12GB | | [PersianMind-v1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q3_K_L.gguf) | Q3_K_L | 3.4GB | | [PersianMind-v1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ4_XS.gguf) | IQ4_XS | 3.45GB | | [PersianMind-v1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_0.gguf) | Q4_0 | 3.61GB | | [PersianMind-v1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.IQ4_NL.gguf) | IQ4_NL | 3.63GB | | [PersianMind-v1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_K_S.gguf) | Q4_K_S | 3.64GB | | [PersianMind-v1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_K.gguf) | Q4_K | 3.85GB | | [PersianMind-v1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_K_M.gguf) | Q4_K_M | 3.85GB | | [PersianMind-v1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q4_1.gguf) | Q4_1 | 4.0GB | | [PersianMind-v1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_0.gguf) | Q5_0 | 4.39GB | | [PersianMind-v1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_K_S.gguf) | Q5_K_S | 4.39GB | | [PersianMind-v1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_K.gguf) | Q5_K | 4.51GB | | [PersianMind-v1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_K_M.gguf) | Q5_K_M | 4.51GB | | [PersianMind-v1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q5_1.gguf) | Q5_1 | 4.77GB | | [PersianMind-v1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q6_K.gguf) | Q6_K | 5.21GB | | [PersianMind-v1.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/universitytehran_-_PersianMind-v1.0-gguf/blob/main/PersianMind-v1.0.Q8_0.gguf) | Q8_0 | 6.75GB | Original model description: --- license: cc-by-nc-sa-4.0 language: - multilingual - fa - en library_name: transformers tags: - text-generation-inference inference: false metrics: - bleu - comet - accuracy - perplexity - spearmanr pipeline_tag: text-generation co2_eq_emissions: emissions: 232380 --- <img src="PersianMind.jpg" alt="PersianMind logo" width=200/> # PersianMind PersianMind is a cross-lingual Persian-English large language model. The model achieves state-of-the-art results on Persian subset of the [Belebele](https://github.com/facebookresearch/belebele) benchmark and the [ParsiNLU multiple-choice QA](https://github.com/persiannlp/parsinlu) task. It also attains performance comparable to GPT-3.5-turbo in a Persian reading comprehension task. ## Model Description - **Developed by:** [Pedram Rostami](mailto:pedram.rostami@ut.ac.ir), [Ali Salemi](mailto:alisalemi@ut.ac.ir), and [Mohammad Javad Dousti](mailto:mjdousti@ut.ac.ir) - **Model type:** Language model - **Languages:** English and Persian - **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (non-commercial use only.) ## How to Get Started with the Model Use the code below to get started with the model. Note that you need to install <code>sentencepiece</code> and <code>accelerate</code> libraries along with <code>PyTorch</code> and <code>🤗Transformers</code> to run this code. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained( "universitytehran/PersianMind-v1.0", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map={"": device}, ) tokenizer = AutoTokenizer.from_pretrained( "universitytehran/PersianMind-v1.0", ) TEMPLATE = "{context}\nYou: {prompt}\nPersianMind: " CONTEXT = "This is a conversation with PersianMind. It is an artificial intelligence model designed by a team of " \ "NLP experts at the University of Tehran to help you with various tasks such as answering questions, " \ "providing recommendations, and helping with decision making. You can ask it anything you want and " \ "it will do its best to give you accurate and relevant information." PROMPT = "در مورد هوش مصنوعی توضیح بده." model_input = TEMPLATE.format(context=CONTEXT, prompt=PROMPT) input_tokens = tokenizer(model_input, return_tensors="pt") input_tokens = input_tokens.to(device) generate_ids = model.generate(**input_tokens, max_new_tokens=512, do_sample=False, repetition_penalty=1.1) model_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(model_output[len(model_input):]) ``` ### How to Quantize the Model Quantized models can be run on resource-constrained devices. To quantize the model, you should install the <code>bitsandbytes</code> library. In order to quantize the model in 8-bit (`INT8`), use the code below. ```python model = AutoModelForCausalLM.from_pretrained( "universitytehran/PersianMind-v1.0", device_map="auto", low_cpu_mem_usage=True, load_in_8bit=True ) ``` Alternatively, you can quantize the model in 4-bit (`NormalFloat4`) with the following code. ```python from transformers import BitsAndBytesConfig quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", ) model = AutoModelForCausalLM.from_pretrained( "universitytehran/PersianMind-v1.0", quantization_config=quantization_config, device_map="auto" ) ``` ### Evaluating Quantized Models | Model | Belebele (Persian) | Fa→En Translation (Comet) | En→Fa Translation (Comet) | Model Size | Tokens/sec | | :----------------------------------------------------------------: | :--------------------------------------------------------------: | :------------------------------------------------------------------------: | :------------------------------------------------------------------------: | :--------: | :--------: | | PersianMind (`BF16`) | 73.9 | 83.61 | 79.44 | 13.7G | 25.35 | | PersianMind (`INT8`) | 73.7 | 82.32 | 78.61 | 7.2G | 11.36 | | PersianMind (`NF4`) | 70.2 | 82.07 | 80.36 | 3.9G | 24.36 | We evaluated quantized models in various tasks against the original model. Specifically, we evaluated all models using the reading comprehension multiple-choice question-answering benchmark of [Belebele](https://github.com/facebookresearch/belebele) (Persian subset) and reported the accuracy of each model. Additionally, we evaluated our models for Persian-to-English and English-to-Persian translation tasks. For this, we utilized the Persian-English subset of the [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200) dataset and reported our results using the Comet metric. Furthermore, we calculated the average number of generated tokens per second by each model during running the translation tasks. To understand resource efficiency, we measured the memory usage of each model by employing the `get_memory_footprint()` function. ## License PersianMind is subject to Meta's [LLaMa2 Community License](https://raw.githubusercontent.com/facebookresearch/llama/main/LICENSE). It is further licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/), which allows non-commercial use of the model. Commercial use of this model requires written agreement which must be obtained from the copyright holders who are listed as developers in this page. If you suspect any violations, please reach out to us. ## Citation If you find this model helpful, please ensure to cite the following paper. **BibTeX:** ```bibtex @misc{persianmind, title={{PersianMind: A Cross-Lingual Persian-English Large Language Model}}, author={Rostami, Pedram and Salemi, Ali and Dousti, Mohammad Javad}, year={2024} eprint={2401.06466}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

clydeiii/scott-stapp-flux

clydeiii

2024-08-30T21:18:57Z

81

0

diffusers

[ "diffusers", "text-to-image", "lora", "flux", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-08-23T18:41:24Z

--- tags: - text-to-image - lora - diffusers - flux base_model: black-forest-labs/FLUX.1-dev instance_prompt: p3r5on widget: - text: p3r5on license: other --- ### Scott Stapp #### Flux LoRA --- # Prompt: p3r5on #### Sample pictures: !["" 0](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451563162__000003000_8.jpg) !["" 1](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451446248__000003000_5.jpg) !["" 2](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451368344__000003000_3.jpg) !["" 3](https://huggingface.co/clydeiii/flux/resolve/main/samples/1724451524223__000003000_7.jpg)

mradermacher/MN-12B-Lyra-v3-GGUF

mradermacher

2024-08-30T21:14:13Z

10

3

transformers

[ "transformers", "gguf", "en", "base_model:Sao10K/MN-12B-Lyra-v3", "base_model:quantized:Sao10K/MN-12B-Lyra-v3", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-29T07:52:37Z

--- base_model: Sao10K/MN-12B-Lyra-v3 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/Sao10K/MN-12B-Lyra-v3  weighted/imatrix quants are available at https://huggingface.co/mradermacher/MN-12B-Lyra-v3-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q2_K.gguf) | Q2_K | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ3_XS.gguf) | IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q3_K_S.gguf) | Q3_K_S | 5.6 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ3_M.gguf) | IQ3_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q3_K_L.gguf) | Q3_K_L | 6.7 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.IQ4_XS.gguf) | IQ4_XS | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q5_K_S.gguf) | Q5_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q5_K_M.gguf) | Q5_K_M | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q6_K.gguf) | Q6_K | 10.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/MN-12B-Lyra-v3-GGUF/resolve/main/MN-12B-Lyra-v3.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Xilixmeaty40/vfdfdfdfdfd

Xilixmeaty40

2024-08-30T21:10:18Z

89

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "base_model:vinthony/SadTalker", "base_model:finetune:vinthony/SadTalker", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T20:26:42Z

--- license: apache-2.0 base_model: vinthony/SadTalker library_name: transformers ---

LouisSanna/orpo-model-output

LouisSanna

2024-08-30T20:57:31Z

119

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "trl", "orpo", "generated_from_trainer", "dataset:piqa", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T20:43:47Z

--- library_name: transformers license: mit base_model: openai-community/gpt2 tags: - trl - orpo - generated_from_trainer datasets: - piqa model-index: - name: orpo-model-output results: [] ---  # orpo-model-output This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the piqa dataset. It achieves the following results on the evaluation set: - Loss: 3.2999 - Rewards/chosen: -0.3182 - Rewards/rejected: -0.3369 - Rewards/accuracies: 0.6693 - Rewards/margins: 0.0187 - Logps/rejected: -3.3688 - Logps/chosen: -3.1821 - Logits/rejected: -24.5739 - Logits/chosen: -24.6603 - Nll Loss: 3.2330 - Log Odds Ratio: -0.6695 - Log Odds Chosen: 0.2013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Nll Loss | Log Odds Ratio | Log Odds Chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:| | 3.1799 | 1.0 | 3223 | 3.1575 | -0.2992 | -0.3103 | 0.6472 | 0.0111 | -3.1030 | -2.9917 | -16.2781 | -16.3180 | 3.0900 | -0.6758 | 0.1201 | | 2.7077 | 2.0 | 6446 | 3.1544 | -0.3005 | -0.3160 | 0.6652 | 0.0154 | -3.1595 | -3.0051 | -21.7517 | -21.8387 | 3.0878 | -0.6671 | 0.1676 | | 2.2691 | 3.0 | 9669 | 3.2999 | -0.3182 | -0.3369 | 0.6693 | 0.0187 | -3.3688 | -3.1821 | -24.5739 | -24.6603 | 3.2330 | -0.6695 | 0.2013 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.0.0 - Datasets 2.16.1 - Tokenizers 0.19.1

ambrosfitz/tinyllama-history

ambrosfitz

2024-08-30T20:53:18Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "dataset:ambrosfitz/just_history_v2", "dataset:ambrosfitz/synth_history_sentences", "dataset:ambrosfitz/ps_history_txt", "dataset:ambrosfitz/might-history-merge_v2", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T00:43:28Z

--- library_name: transformers license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: tinyllama-history results: [] datasets: - ambrosfitz/just_history_v2 - ambrosfitz/synth_history_sentences - ambrosfitz/ps_history_txt - ambrosfitz/might-history-merge_v2 ---  # tinyllama-history This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

stablediffusionapi/oxalis-anime-hentai-model

stablediffusionapi

2024-08-30T20:45:20Z

32

0

diffusers

[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-08-30T20:40:06Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Oxalis Anime Hentai Model API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/20484209241725050156.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "oxalis-anime-hentai-model" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/oxalis-anime-hentai-model) Model link: [View model](https://modelslab.com/models/oxalis-anime-hentai-model) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "oxalis-anime-hentai-model", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

stablediffusionapi/falkons-anime-and-hentai

stablediffusionapi

2024-08-30T20:45:20Z

29

0

diffusers

[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-08-30T20:40:53Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Falkons (Anime and Hentai) API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/14969003721725050261.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "falkons-anime-and-hentai" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/falkons-anime-and-hentai) Model link: [View model](https://modelslab.com/models/falkons-anime-and-hentai) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "falkons-anime-and-hentai", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

bartowski/c4ai-command-r-plus-08-2024-GGUF

bartowski

2024-08-30T20:45:08Z

5,335

19

transformers

[ "transformers", "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "base_model:CohereForAI/c4ai-command-r-plus-08-2024", "base_model:quantized:CohereForAI/c4ai-command-r-plus-08-2024", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

text-generation

2024-08-30T14:28:25Z

--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox quantized_by: bartowski pipeline_tag: text-generation base_model: CohereForAI/c4ai-command-r-plus-08-2024 --- ## Llamacpp imatrix Quantizations of c4ai-command-r-plus-08-2024 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3634">b3634</a> for quantization. Original model: https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) ## Prompt format ``` <BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{system_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Split | Description | | -------- | ---------- | --------- | ----- | ----------- | | [c4ai-command-r-plus-08-2024-Q8_0.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q8_0) | Q8_0 | 110.31GB | true | Extremely high quality, generally unneeded but max available quant. | | [c4ai-command-r-plus-08-2024-Q6_K.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q6_K) | Q6_K | 85.17GB | true | Very high quality, near perfect, *recommended*. | | [c4ai-command-r-plus-08-2024-Q5_K_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q5_K_M) | Q5_K_M | 73.62GB | true | High quality, *recommended*. | | [c4ai-command-r-plus-08-2024-Q4_K_L.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_K_L) | Q4_K_L | 63.51GB | true | Uses Q8_0 for embed and output weights. Good quality, *recommended*. | | [c4ai-command-r-plus-08-2024-Q4_K_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_K_M) | Q4_K_M | 62.75GB | true | Good quality, default size for must use cases, *recommended*. | | [c4ai-command-r-plus-08-2024-Q4_K_S.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_K_S) | Q4_K_S | 59.64GB | true | Slightly lower quality with more space savings, *recommended*. | | [c4ai-command-r-plus-08-2024-Q4_0.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_0) | Q4_0 | 59.43GB | true | Legacy format, generally not worth using over similarly sized formats | | [c4ai-command-r-plus-08-2024-Q4_0_4_8.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_0_4_8) | Q4_0_4_8 | 59.22GB | true | Optimized for ARM and CPU inference, much faster than Q4_0 at similar quality. | | [c4ai-command-r-plus-08-2024-Q4_0_4_4.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q4_0_4_4) | Q4_0_4_4 | 59.22GB | true | Optimized for ARM and CPU inference, much faster than Q4_0 at similar quality. | | [c4ai-command-r-plus-08-2024-IQ4_XS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-IQ4_XS) | IQ4_XS | 56.20GB | true | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [c4ai-command-r-plus-08-2024-Q3_K_XL.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q3_K_XL) | Q3_K_XL | 56.16GB | true | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. | | [c4ai-command-r-plus-08-2024-Q3_K_L.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q3_K_L) | Q3_K_L | 55.40GB | true | Lower quality but usable, good for low RAM availability. | | [c4ai-command-r-plus-08-2024-Q3_K_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/tree/main/c4ai-command-r-plus-08-2024-Q3_K_M) | Q3_K_M | 50.98GB | true | Low quality. | | [c4ai-command-r-plus-08-2024-IQ3_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ3_M.gguf) | IQ3_M | 47.68GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [c4ai-command-r-plus-08-2024-Q3_K_S.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-Q3_K_S.gguf) | Q3_K_S | 45.85GB | false | Low quality, not recommended. | | [c4ai-command-r-plus-08-2024-IQ3_XXS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ3_XXS.gguf) | IQ3_XXS | 40.66GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. | | [c4ai-command-r-plus-08-2024-Q2_K_L.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-Q2_K_L.gguf) | Q2_K_L | 40.26GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. | | [c4ai-command-r-plus-08-2024-Q2_K.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-Q2_K.gguf) | Q2_K | 39.50GB | false | Very low quality but surprisingly usable. | | [c4ai-command-r-plus-08-2024-IQ2_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ2_M.gguf) | IQ2_M | 36.04GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. | | [c4ai-command-r-plus-08-2024-IQ2_XS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ2_XS.gguf) | IQ2_XS | 31.63GB | false | Low quality, uses SOTA techniques to be usable. | | [c4ai-command-r-plus-08-2024-IQ2_XXS.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ2_XXS.gguf) | IQ2_XXS | 28.61GB | false | Very low quality, uses SOTA techniques to be usable. | | [c4ai-command-r-plus-08-2024-IQ1_M.gguf](https://huggingface.co/bartowski/c4ai-command-r-plus-08-2024-GGUF/blob/main/c4ai-command-r-plus-08-2024-IQ1_M.gguf) | IQ1_M | 25.22GB | false | Extremely low quality, *not* recommended. | ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. Thanks! ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset Thank you ZeroWw for the inspiration to experiment with embed/output ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/c4ai-command-r-plus-08-2024-GGUF --include "c4ai-command-r-plus-08-2024-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/c4ai-command-r-plus-08-2024-GGUF --include "c4ai-command-r-plus-08-2024-Q8_0/*" --local-dir ./ ``` You can either specify a new local-dir (c4ai-command-r-plus-08-2024-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

stablediffusionapi/kawaii-realistic-anime-mi

stablediffusionapi

2024-08-30T20:33:23Z

30

0

diffusers

[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-08-30T20:29:59Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Kawaii Realistic Anime Mix API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/13606319581725049585.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "kawaii-realistic-anime-mi" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/kawaii-realistic-anime-mi) Model link: [View model](https://modelslab.com/models/kawaii-realistic-anime-mi) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "kawaii-realistic-anime-mi", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

taku-yoshioka/rlhf-line-marcja-0828

taku-yoshioka

2024-08-30T20:19:51Z

48

0

transformers

[ "transformers", "pytorch", "safetensors", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]

reinforcement-learning

2024-08-29T00:52:48Z

--- license: apache-2.0 tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="taku-yoshioka//tmp/tmpxviy4d9k/taku-yoshioka/rlhf-line-marcja-0828") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("taku-yoshioka//tmp/tmpxviy4d9k/taku-yoshioka/rlhf-line-marcja-0828") model = AutoModelForCausalLMWithValueHead.from_pretrained("taku-yoshioka//tmp/tmpxviy4d9k/taku-yoshioka/rlhf-line-marcja-0828") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

Xilixmeaty40/sad

Xilixmeaty40

2024-08-30T20:16:41Z

76

0

transformers

[ "transformers", "safetensors", "bert", "code", "chemistry", "biology", "music", "es", "dataset:fka/awesome-chatgpt-prompts", "dataset:LLLM-Lab/huanhuan_sadtalker", "arxiv:1910.09700", "base_model:vinthony/SadTalker", "base_model:finetune:vinthony/SadTalker", "license:gemma", "endpoints_compatible", "region:us" ]

null

2024-06-19T20:35:23Z

--- license: gemma datasets: - fka/awesome-chatgpt-prompts - LLLM-Lab/huanhuan_sadtalker language: - es metrics: - accuracy base_model: vinthony/SadTalker library_name: transformers tags: - code - chemistry - biology - music extra_gated_heading: Access sad on Hugging Face extra_gated_button_content: Acknowledge license extra_gated_prompt: >- To access sad on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

magnifi/Phi3_intent_v32_3_epoch_7_lr_0.002_r_16_a_16

magnifi

2024-08-30T20:02:16Z

75

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T19:59:50Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** magnifi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

lgk03/WITHINAPPS_NDD-dimeshift_test-tags-CWAdj

lgk03

2024-08-30T20:01:51Z

105

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-08-30T19:39:40Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-dimeshift_test-tags-CWAdj results: [] ---  # WITHINAPPS_NDD-dimeshift_test-tags-CWAdj This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3579 - Accuracy: 0.8865 - F1: 0.9038 - Precision: 0.9379 - Recall: 0.8865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 0.9897 | 72 | 0.4258 | 0.8624 | 0.8858 | 0.9303 | 0.8624 | | No log | 1.9931 | 145 | 0.3954 | 0.8723 | 0.8929 | 0.9321 | 0.8723 | | No log | 2.9966 | 218 | 0.3774 | 0.5370 | 0.6318 | 0.9294 | 0.5370 | | No log | 4.0 | 291 | 0.3647 | 0.8865 | 0.9038 | 0.9379 | 0.8865 | | No log | 4.9485 | 360 | 0.3579 | 0.8865 | 0.9038 | 0.9379 | 0.8865 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2

unclemusclez

2024-08-30T20:00:21Z

111

0

sentence-transformers

[ "sentence-transformers", "safetensors", "gguf", "llama", "sentence-similarity", "feature-extraction", "autotrain", "dataset:skratos115/opendevin_DataDevinator", "base_model:HuggingFaceTB/SmolLM-135M-Instruct", "base_model:quantized:HuggingFaceTB/SmolLM-135M-Instruct", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]

sentence-similarity

2024-08-30T15:44:10Z

--- library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - autotrain base_model: HuggingFaceTB/SmolLM-135M-Instruct widget: - source_sentence: 'search_query: i love autotrain' sentences: - 'search_query: huggingface auto train' - 'search_query: hugging face auto train' - 'search_query: i love autotrain' pipeline_tag: sentence-similarity datasets: - skratos115/opendevin_DataDevinator --- AVAILABLE ON OLLAMA: https://ollama.com/unclemusclez/smollm-135m-instruct-devinator # Model Trained Using AutoTrain - Problem type: Sentence Transformers ## Validation Metrics No validation metrics available ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the Hugging Face Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'search_query: autotrain', 'search_query: auto train', 'search_query: i love autotrain', ] embeddings = model.encode(sentences) print(embeddings.shape) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) ```

unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.1

unclemusclez

2024-08-30T20:00:02Z

112

0

sentence-transformers

[ "sentence-transformers", "safetensors", "gguf", "llama", "sentence-similarity", "feature-extraction", "autotrain", "dataset:skratos115/opendevin_DataDevinator", "base_model:HuggingFaceTB/SmolLM-135M-Instruct", "base_model:quantized:HuggingFaceTB/SmolLM-135M-Instruct", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]

sentence-similarity

2024-08-30T19:10:03Z

--- library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - autotrain base_model: HuggingFaceTB/SmolLM-135M-Instruct widget: - source_sentence: 'search_query: i love autotrain' sentences: - 'search_query: huggingface auto train' - 'search_query: hugging face auto train' - 'search_query: i love autotrain' pipeline_tag: sentence-similarity datasets: - skratos115/opendevin_DataDevinator --- AVAILABLE ON OLLAMA: https://ollama.com/unclemusclez/smollm-135m-instruct-devinator # Model Trained Using AutoTrain - Problem type: Sentence Transformers ## Validation Metrics No validation metrics available ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the Hugging Face Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'search_query: autotrain', 'search_query: auto train', 'search_query: i love autotrain', ] embeddings = model.encode(sentences) print(embeddings.shape) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) ```

jeiku/NeuroNoise_v1

jeiku

2024-08-30T19:44:38Z

8

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml", "base_model:finetune:IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T19:33:10Z

--- library_name: transformers license: other base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml tags: - generated_from_trainer model-index: - name: outputs/out results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/Gryphe-3.5-16k-Subset type: sharegpt conversation: chatml - path: ResplendentAI/bluemoon type: sharegpt conversation: chatml - path: openerotica/freedom-rp type: sharegpt conversation: chatml - path: MinervaAI/Aesir-Preview type: sharegpt conversation: chatml - path: anthracite-org/stheno-filtered-v1.1 type: sharegpt conversation: chatml - path: NewEden/Kalo-Opus-Instruct-22k-Refusal-Murdered type: sharegpt conversation: chatml - path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml - path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml - path: jeiku/jeikutxt type: completion - path: ResplendentAI/Sissification_Hypno_1k type: alpaca - path: ResplendentAI/theory_of_mind_fixed_output type: alpaca - path: ResplendentAI/Synthetic_Soul_1k type: alpaca chat_template: chatml val_set_size: 0.01 output_dir: ./outputs/out adapter: lora_r: lora_alpha: lora_dropout: lora_target_linear: sequence_len: 8192 # sequence_len: 32768 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true wandb_project: Neuro4B wandb_entity: wandb_watch: wandb_name: Neuro4B wandb_log_model: gradient_accumulation_steps: 32 micro_batch_size: 2 num_epochs: 2 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.00001 weight_decay: 0.05 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.1 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 2 debug: deepspeed: deepspeed_configs/zero3.json fsdp: fsdp_config: special_tokens: pad_token: <|finetune_right_pad_id|> ``` </details> # outputs/out This model is a fine-tuned version of [IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml](https://huggingface.co/IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4207 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 19 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.0722 | 0.0094 | 1 | 3.0240 | | 1.5947 | 0.2529 | 27 | 2.8578 | | 1.4579 | 0.5057 | 54 | 2.5541 | | 1.4422 | 0.7586 | 81 | 2.4550 | | 1.4039 | 1.0006 | 108 | 2.4334 | | 1.3428 | 1.2534 | 135 | 2.4217 | | 1.3054 | 1.5063 | 162 | 2.4259 | | 1.3378 | 1.7591 | 189 | 2.4207 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf

RichardErkhov

2024-08-30T19:40:18Z

5

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T13:59:54Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) FusionNet_SOLAR - GGUF - Model creator: https://huggingface.co/TomGrc/ - Original model: https://huggingface.co/TomGrc/FusionNet_SOLAR/ | Name | Quant method | Size | | ---- | ---- | ---- | | [FusionNet_SOLAR.Q2_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q2_K.gguf) | Q2_K | 5.52GB | | [FusionNet_SOLAR.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ3_XS.gguf) | IQ3_XS | 6.13GB | | [FusionNet_SOLAR.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ3_S.gguf) | IQ3_S | 6.48GB | | [FusionNet_SOLAR.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K_S.gguf) | Q3_K_S | 6.44GB | | [FusionNet_SOLAR.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ3_M.gguf) | IQ3_M | 6.69GB | | [FusionNet_SOLAR.Q3_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K.gguf) | Q3_K | 7.18GB | | [FusionNet_SOLAR.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K_M.gguf) | Q3_K_M | 7.18GB | | [FusionNet_SOLAR.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q3_K_L.gguf) | Q3_K_L | 7.82GB | | [FusionNet_SOLAR.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ4_XS.gguf) | IQ4_XS | 8.06GB | | [FusionNet_SOLAR.Q4_0.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_0.gguf) | Q4_0 | 8.4GB | | [FusionNet_SOLAR.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.IQ4_NL.gguf) | IQ4_NL | 8.49GB | | [FusionNet_SOLAR.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_K_S.gguf) | Q4_K_S | 3.76GB | | [FusionNet_SOLAR.Q4_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_K.gguf) | Q4_K | 8.94GB | | [FusionNet_SOLAR.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_K_M.gguf) | Q4_K_M | 8.94GB | | [FusionNet_SOLAR.Q4_1.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q4_1.gguf) | Q4_1 | 9.32GB | | [FusionNet_SOLAR.Q5_0.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_0.gguf) | Q5_0 | 10.24GB | | [FusionNet_SOLAR.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_K_S.gguf) | Q5_K_S | 10.24GB | | [FusionNet_SOLAR.Q5_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_K.gguf) | Q5_K | 10.52GB | | [FusionNet_SOLAR.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_K_M.gguf) | Q5_K_M | 10.52GB | | [FusionNet_SOLAR.Q5_1.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q5_1.gguf) | Q5_1 | 11.16GB | | [FusionNet_SOLAR.Q6_K.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q6_K.gguf) | Q6_K | 12.2GB | | [FusionNet_SOLAR.Q8_0.gguf](https://huggingface.co/RichardErkhov/TomGrc_-_FusionNet_SOLAR-gguf/blob/main/FusionNet_SOLAR.Q8_0.gguf) | Q8_0 | 15.8GB | Original model description: --- language: - en license: mit pipeline_tag: text-generation model-index: - name: FusionNet_SOLAR results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.4 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.29 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 69.21 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 50.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_SOLAR name: Open LLM Leaderboard --- # FusionNet_SOLAR Fine-tuned model on English language using SOLAR Fusion method. ## Model description This is an experiment with the SOLAR Fusion method of FusionNet. This model has 16B parameters, and this model is fine-tuned. Enjoy! # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_SOLAR) | Metric |Value| |---------------------------------|----:| |Avg. |71.08| |AI2 Reasoning Challenge (25-Shot)|71.59| |HellaSwag (10-Shot) |88.40| |MMLU (5-Shot) |65.29| |TruthfulQA (0-shot) |69.21| |Winogrande (5-shot) |81.06| |GSM8k (5-shot) |50.95|

mradermacher/Coder-2B-GGUF

mradermacher

2024-08-30T19:39:11Z

27

0

transformers

[ "transformers", "gguf", "unsloth", "trl", "sft", "en", "base_model:suriya7/Coder-2B", "base_model:quantized:suriya7/Coder-2B", "endpoints_compatible", "region:us" ]

null

2024-08-30T19:30:28Z

--- base_model: suriya7/Coder-2B language: - en library_name: transformers quantized_by: mradermacher tags: - unsloth - trl - sft --- ## About      static quants of https://huggingface.co/suriya7/Coder-2B  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q2_K.gguf) | Q2_K | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ3_XS.gguf) | IQ3_XS | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ3_S.gguf) | IQ3_S | 1.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q3_K_S.gguf) | Q3_K_S | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ3_M.gguf) | IQ3_M | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q3_K_M.gguf) | Q3_K_M | 1.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q3_K_L.gguf) | Q3_K_L | 1.7 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.IQ4_XS.gguf) | IQ4_XS | 1.7 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q4_K_M.gguf) | Q4_K_M | 1.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q5_K_S.gguf) | Q5_K_S | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q5_K_M.gguf) | Q5_K_M | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q6_K.gguf) | Q6_K | 2.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.Q8_0.gguf) | Q8_0 | 2.9 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Coder-2B-GGUF/resolve/main/Coder-2B.f16.gguf) | f16 | 5.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mertgulexe/distributed-model

mertgulexe

2024-08-30T19:37:47Z

104

0

transformers

[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-08-30T19:30:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lmstudio-community/c4ai-command-r-plus-08-2024-GGUF

lmstudio-community

2024-08-30T19:36:22Z

485

5

transformers

[ "transformers", "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "base_model:CohereForAI/c4ai-command-r-plus-08-2024", "base_model:quantized:CohereForAI/c4ai-command-r-plus-08-2024", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-08-30T16:02:51Z

--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox quantized_by: bartowski pipeline_tag: text-generation base_model: CohereForAI/c4ai-command-r-plus-08-2024 lm_studio: param_count: 105b use_case: general release_date: 30-08-2024 model_creator: CohereForAI prompt_template: cohere_command_r base_model: Cohere system_prompt: You are a large language model called Command R built by the company Cohere. You act as a brilliant, sophisticated, AI-assistant chatbot trained to assist human users by providing thorough responses. original_repo: CohereForAI/c4ai-command-r-plus-08-2024 --- ## 💫 Community Model> C4AI Command R Plus 08-2024 by Cohere For AI *👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*. **Model creator:** [CohereForAI](https://huggingface.co/CohereForAI) **Original model**: [c4ai-command-r-plus-08-2024](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024) **GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3634](https://github.com/ggerganov/llama.cpp/releases/tag/b3634) ## Model Summary: C4AI Command R Plus 08-2024 is an update to the originally released 105B paramater Command R. The original Command R model received sweeping praise for its incredible RAG and multilingual abilities, and this model is no different. Not for commercial use, must adhere to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ## Prompt Template: Choose the `Cohere Command R` preset in your LM Studio. Under the hood, the model will see a prompt that's formatted like so: ``` <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ``` This model also supports tool use and RAG prompt formats. For details on formatting for those use cases, view [tool use here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024#tool-use--agent-capabilities) and [RAG capabilities here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024#grounded-generation-and-rag-capabilities) ## Technical Details C4AI Command R Plus 08-2024 has been trained on 23 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian). Due to this multilingual training, it excels in multilingual tasks. Command R Plus 08-2024 supports a context length of 128K. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio. ### Terms of Use (directly from Cohere For AI) We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).

Tawkat/qlora-med42llm3-nclex-august-v7

Tawkat

2024-08-30T19:36:21Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T16:32:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TheNovak/simpletuner-lora-Elbows

TheNovak

2024-08-30T19:20:49Z

14

0

diffusers

[ "diffusers", "flux", "flux-diffusers", "text-to-image", "simpletuner", "lora", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-08-30T07:33:43Z

--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - lora - template:sd-lora inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'Elbows the cat on a box' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # simpletuner-lora-Elbows This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` Elbows the cat on a box ``` ## Validation settings - CFG: `3.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 5 - Training steps: 8200 - Learning rate: 8e-05 - Effective batch size: 1 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: bf16 - Quantised: No - Xformers: Not used - LoRA Rank: 64 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### Elbows - Repeats: 0 - Total number of images: 1392 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'jusscubs/simpletuner-lora-Elbows' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "Elbows the cat on a box" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.0, ).images[0] image.save("output.png", format="PNG") ```

showvikdbz/phi-3.5-it-25k

showvikdbz

2024-08-30T19:18:06Z

75

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T19:11:53Z

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

geshijoker/distilbert_ner_wnut_model

geshijoker

2024-08-30T19:14:00Z

105

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-08-30T19:12:11Z

--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert_ner_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5339578454332553 - name: Recall type: recall value: 0.211306765523633 - name: F1 type: f1 value: 0.30278884462151395 - name: Accuracy type: accuracy value: 0.9365995468342525 ---  # distilbert_ner_wnut_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2970 - Precision: 0.5340 - Recall: 0.2113 - F1: 0.3028 - Accuracy: 0.9366 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 107 | 0.3140 | 0.3744 | 0.0732 | 0.1225 | 0.9306 | | No log | 2.0 | 214 | 0.2970 | 0.5340 | 0.2113 | 0.3028 | 0.9366 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.2.1+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

csikasote/mms-zeroshot-bem-sv-male

csikasote

2024-08-30T19:07:14Z

10

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "BembaSpeech", "mms", "generated_from_trainer", "base_model:mms-meta/mms-zeroshot-300m", "base_model:finetune:mms-meta/mms-zeroshot-300m", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-08-30T13:55:57Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: mms-meta/mms-zeroshot-300m tags: - automatic-speech-recognition - BembaSpeech - mms - generated_from_trainer metrics: - wer model-index: - name: mms-zeroshot-bem-sv-male results: [] ---  # mms-zeroshot-bem-sv-male This model is a fine-tuned version of [mms-meta/mms-zeroshot-300m](https://huggingface.co/mms-meta/mms-zeroshot-300m) on the BEMBASPEECH - BEM dataset. It achieves the following results on the evaluation set: - Loss: 0.1874 - Wer: 0.3949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | No log | 0.2183 | 200 | 2.3822 | 1.0 | | No log | 0.4367 | 400 | 0.2715 | 0.5093 | | 2.7769 | 0.6550 | 600 | 0.2489 | 0.4820 | | 2.7769 | 0.8734 | 800 | 0.2296 | 0.4695 | | 0.6809 | 1.0917 | 1000 | 0.2209 | 0.4638 | | 0.6809 | 1.3100 | 1200 | 0.2163 | 0.4469 | | 0.6809 | 1.5284 | 1400 | 0.2092 | 0.4400 | | 0.6113 | 1.7467 | 1600 | 0.2047 | 0.4346 | | 0.6113 | 1.9651 | 1800 | 0.2074 | 0.4467 | | 0.5974 | 2.1834 | 2000 | 0.2041 | 0.4304 | | 0.5974 | 2.4017 | 2200 | 0.2054 | 0.4317 | | 0.5974 | 2.6201 | 2400 | 0.1987 | 0.4240 | | 0.5636 | 2.8384 | 2600 | 0.2003 | 0.4252 | | 0.5636 | 3.0568 | 2800 | 0.1997 | 0.4287 | | 0.5398 | 3.2751 | 3000 | 0.2097 | 0.4400 | | 0.5398 | 3.4934 | 3200 | 0.1968 | 0.4165 | | 0.5398 | 3.7118 | 3400 | 0.2013 | 0.4218 | | 0.5334 | 3.9301 | 3600 | 0.2003 | 0.4230 | | 0.5334 | 4.1485 | 3800 | 0.1976 | 0.4227 | | 0.5123 | 4.3668 | 4000 | 0.1978 | 0.4198 | | 0.5123 | 4.5852 | 4200 | 0.2019 | 0.4298 | | 0.5123 | 4.8035 | 4400 | 0.1939 | 0.4146 | | 0.5119 | 5.0218 | 4600 | 0.1989 | 0.4161 | | 0.5119 | 5.2402 | 4800 | 0.1902 | 0.4076 | | 0.4929 | 5.4585 | 5000 | 0.1929 | 0.4116 | | 0.4929 | 5.6769 | 5200 | 0.1943 | 0.4144 | | 0.4929 | 5.8952 | 5400 | 0.1922 | 0.4106 | | 0.4878 | 6.1135 | 5600 | 0.1933 | 0.4137 | | 0.4878 | 6.3319 | 5800 | 0.1920 | 0.4058 | | 0.4755 | 6.5502 | 6000 | 0.1927 | 0.4171 | | 0.4755 | 6.7686 | 6200 | 0.1920 | 0.4127 | | 0.4755 | 6.9869 | 6400 | 0.1925 | 0.4061 | | 0.475 | 7.2052 | 6600 | 0.1884 | 0.4058 | | 0.475 | 7.4236 | 6800 | 0.1903 | 0.4070 | | 0.4715 | 7.6419 | 7000 | 0.1882 | 0.3996 | | 0.4715 | 7.8603 | 7200 | 0.1881 | 0.4033 | | 0.4715 | 8.0786 | 7400 | 0.1885 | 0.4007 | | 0.4575 | 8.2969 | 7600 | 0.1885 | 0.4016 | | 0.4575 | 8.5153 | 7800 | 0.1888 | 0.4050 | | 0.4611 | 8.7336 | 8000 | 0.1884 | 0.4046 | | 0.4611 | 8.9520 | 8200 | 0.1881 | 0.3974 | | 0.4611 | 9.1703 | 8400 | 0.1865 | 0.3956 | | 0.4559 | 9.3886 | 8600 | 0.1875 | 0.3974 | | 0.4559 | 9.6070 | 8800 | 0.1872 | 0.3996 | | 0.4536 | 9.8253 | 9000 | 0.1876 | 0.3953 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

GlobalMeltdown/MaidenlessNoMore-7B-GGUF

GlobalMeltdown

2024-08-30T18:55:11Z

12

3

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "Roleplay", "RP", "Chat", "text-generation-inference", "merge ", "text generation", "en", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

null

2024-08-29T21:05:23Z

--- base_model: MaidenlessNoMore-7B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo - Roleplay - RP - Chat - text-generation-inference - 'merge ' - text generation license: cc-by-4.0 language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65bbcee1320702b1043ef8ae/9OPS0wrdkzksmyuM6Nxdu.png) MaidenlessNoMore-7B-GGUF was my first attempt at merging an LLM I decided to use one of the first models I really enjoyed that not many people know of: https://huggingface.co/cookinai/Valkyrie-V1 with my other favorite model that has been my fallback model for a long time: https://huggingface.co/SanjiWatsuki/Kunoichi-7B This was more of an experiment than anything else. Hopefully this will lead to some more interesting merges and who knows what else in the future. I mean we have to start somewhere right? Alpaca or Alpaca roleplay is recommended. # GlobalMeltdown/MaidenlessNoMore-7B-GGUF This model was converted to GGUF format from [`GlobalMeltdown/MaidenlessNoMore-7B`](https://huggingface.co/GlobalMeltdown/MaidenlessNoMore-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/GlobalMeltdown/MaidenlessNoMore-7B) for more details on the model.

csikasote/mms-zeroshot-bem-sv-female

csikasote

2024-08-30T18:42:13Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "BembaSpeech", "mms", "generated_from_trainer", "base_model:mms-meta/mms-zeroshot-300m", "base_model:finetune:mms-meta/mms-zeroshot-300m", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-08-30T14:12:22Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: mms-meta/mms-zeroshot-300m tags: - automatic-speech-recognition - BembaSpeech - mms - generated_from_trainer metrics: - wer model-index: - name: mms-zeroshot-bem-sv-female results: [] ---  # mms-zeroshot-bem-sv-female This model is a fine-tuned version of [mms-meta/mms-zeroshot-300m](https://huggingface.co/mms-meta/mms-zeroshot-300m) on the BEMBASPEECH - BEM dataset. It achieves the following results on the evaluation set: - Loss: 0.2722 - Wer: 0.4375 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | No log | 0.3992 | 200 | 1.9434 | 1.0 | | No log | 0.7984 | 400 | 0.3648 | 0.5411 | | 2.5692 | 1.1976 | 600 | 0.3371 | 0.5174 | | 2.5692 | 1.5968 | 800 | 0.3229 | 0.5213 | | 0.3941 | 1.9960 | 1000 | 0.3183 | 0.4915 | | 0.3941 | 2.3952 | 1200 | 0.3068 | 0.5073 | | 0.3941 | 2.7944 | 1400 | 0.3057 | 0.4688 | | 0.3502 | 3.1936 | 1600 | 0.3017 | 0.4777 | | 0.3502 | 3.5928 | 1800 | 0.2905 | 0.4647 | | 0.3253 | 3.9920 | 2000 | 0.2857 | 0.4686 | | 0.3253 | 4.3912 | 2200 | 0.2892 | 0.4601 | | 0.3253 | 4.7904 | 2400 | 0.2848 | 0.4759 | | 0.3066 | 5.1896 | 2600 | 0.2801 | 0.4444 | | 0.3066 | 5.5888 | 2800 | 0.2752 | 0.4627 | | 0.2988 | 5.9880 | 3000 | 0.2818 | 0.4614 | | 0.2988 | 6.3872 | 3200 | 0.2759 | 0.4444 | | 0.2988 | 6.7864 | 3400 | 0.2751 | 0.4382 | | 0.2877 | 7.1856 | 3600 | 0.2726 | 0.4472 | | 0.2877 | 7.5848 | 3800 | 0.2722 | 0.4484 | | 0.2812 | 7.9840 | 4000 | 0.2710 | 0.4344 | | 0.2812 | 8.3832 | 4200 | 0.2734 | 0.4410 | | 0.2812 | 8.7824 | 4400 | 0.2734 | 0.4360 | | 0.2742 | 9.1816 | 4600 | 0.2759 | 0.4398 | | 0.2742 | 9.5808 | 4800 | 0.2740 | 0.4337 | | 0.2731 | 9.9800 | 5000 | 0.2722 | 0.4382 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

lgk03/WITHINAPPS_NDD-mrbs_test-tags-CWAdj

lgk03

2024-08-30T18:21:47Z

105

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-08-30T18:00:13Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-mrbs_test-tags-CWAdj results: [] ---  # WITHINAPPS_NDD-mrbs_test-tags-CWAdj This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0932 - Accuracy: 0.8764 - F1: 0.8787 - Precision: 0.9031 - Recall: 0.8764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 71 | 0.0974 | 0.8764 | 0.8787 | 0.9031 | 0.8764 | | No log | 2.0 | 142 | 0.0972 | 0.8764 | 0.8787 | 0.9031 | 0.8764 | | No log | 3.0 | 213 | 0.0957 | 0.8764 | 0.8787 | 0.9031 | 0.8764 | | No log | 4.0 | 284 | 0.0947 | 0.8764 | 0.8787 | 0.9031 | 0.8764 | | No log | 5.0 | 355 | 0.0932 | 0.8764 | 0.8787 | 0.9031 | 0.8764 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

arshiakarimian1/spam-student-4-256

arshiakarimian1

2024-08-30T18:16:01Z

173

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-29T23:15:08Z

--- library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - generated_from_trainer model-index: - name: spam-student-4-256 results: [] ---  # spam-student-4-256 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

netcat420/MFANNv0.20.12

netcat420

2024-08-30T18:14:25Z

17

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated", "base_model:merge:mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated", "base_model:netcat420/MFANNv0.19", "base_model:merge:netcat420/MFANNv0.19", "base_model:netcat420/MFANNv0.20", "base_model:merge:netcat420/MFANNv0.20", "base_model:netcat420/MFANNv0.20.11", "base_model:merge:netcat420/MFANNv0.20.11", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T18:09:21Z

--- base_model: - netcat420/MFANNv0.19 - netcat420/MFANNv0.20.11 - netcat420/MFANNv0.20 - mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) as a base. ### Models Merged The following models were included in the merge: * [netcat420/MFANNv0.19](https://huggingface.co/netcat420/MFANNv0.19) * [netcat420/MFANNv0.20.11](https://huggingface.co/netcat420/MFANNv0.20.11) * [netcat420/MFANNv0.20](https://huggingface.co/netcat420/MFANNv0.20) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: netcat420/MFANNv0.20.11 parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: netcat420/MFANNv0.20 parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: netcat420/MFANNv0.19 parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 merge_method: ties base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated parameters: normalize: true int8_mask: true dtype: float16 ```

LearnerSX/847_capstone_tweets_bert_v2

LearnerSX

2024-08-30T17:35:49Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-08-19T01:20:05Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: 847_capstone_tweets_bert_v2 results: [] ---  # 847_capstone_tweets_bert_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6962 - Accuracy: 0.8508 - F1: 0.8487 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1

gglabs/Mistral-Nemo-12B-FC-Chat-0830-1-epoch

gglabs

2024-08-30T17:27:37Z

6

0

transformers

[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T16:57:25Z

--- base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - **Developed by:** gglabs - **License:** apache-2.0 - **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

jacobposel/cc-rl-pattern-2

jacobposel

2024-08-30T17:20:15Z

7

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-08-30T16:12:53Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image instance_prompt: <lora:RL> --- # Cc Rl Pattern 2 Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `<lora:RL>` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jacobposel/cc-rl-pattern-2', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

Huseyin/bert-base-uncased-finetuned-ner

Huseyin

2024-08-30T17:15:38Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-06-07T11:39:18Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased-finetuned-ner results: [] ---  # bert-base-uncased-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3910 - Precision: 0.9616 - Recall: 0.9637 - F1: 0.9627 - Accuracy: 0.9560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3052 | 1.0 | 3334 | 0.2630 | 0.9365 | 0.9367 | 0.9366 | 0.9228 | | 0.2104 | 2.0 | 6668 | 0.2481 | 0.9418 | 0.9537 | 0.9477 | 0.9400 | | 0.163 | 3.0 | 10002 | 0.2390 | 0.9495 | 0.9606 | 0.9550 | 0.9479 | | 0.1151 | 4.0 | 13336 | 0.2516 | 0.9549 | 0.9616 | 0.9583 | 0.9515 | | 0.0809 | 5.0 | 16670 | 0.2887 | 0.9590 | 0.9556 | 0.9573 | 0.9493 | | 0.0625 | 6.0 | 20004 | 0.2912 | 0.9573 | 0.9611 | 0.9592 | 0.9520 | | 0.0516 | 7.0 | 23338 | 0.3139 | 0.9581 | 0.9563 | 0.9572 | 0.9501 | | 0.0388 | 8.0 | 26672 | 0.3070 | 0.9605 | 0.9600 | 0.9602 | 0.9531 | | 0.0273 | 9.0 | 30006 | 0.3344 | 0.9607 | 0.9617 | 0.9612 | 0.9535 | | 0.0252 | 10.0 | 33340 | 0.3547 | 0.9608 | 0.9638 | 0.9623 | 0.9554 | | 0.0242 | 11.0 | 36674 | 0.3726 | 0.9600 | 0.9619 | 0.9610 | 0.9541 | | 0.0119 | 12.0 | 40008 | 0.3727 | 0.9602 | 0.9623 | 0.9612 | 0.9546 | | 0.0078 | 13.0 | 43342 | 0.3772 | 0.9617 | 0.9639 | 0.9628 | 0.9562 | | 0.0078 | 14.0 | 46676 | 0.3904 | 0.9615 | 0.9638 | 0.9627 | 0.9560 | | 0.0026 | 15.0 | 50010 | 0.3910 | 0.9616 | 0.9637 | 0.9627 | 0.9560 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

geshijoker/distilbert_sentiment

geshijoker

2024-08-30T17:08:28Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-08-30T16:49:06Z

--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert_sentiment results: [] ---  # distilbert_sentiment This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Accuracy: 0.9299 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2792 | 1.0 | 782 | 0.1936 | 0.9251 | | 0.1419 | 2.0 | 1564 | 0.1957 | 0.9299 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.2.1+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF

mradermacher

2024-08-30T17:02:15Z

63

1

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos", "base_model:quantized:EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-08-30T11:24:20Z

--- base_model: EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl --- ## About      weighted/imatrix quants of https://huggingface.co/EpistemeAI2/Fireball-Alpaca-Llama3.1-8B-Philos  static quants are available at https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Alpaca-Llama3.1-8B-Philos-i1-GGUF/resolve/main/Fireball-Alpaca-Llama3.1-8B-Philos.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

netcat420/MFANNv0.20.11

netcat420

2024-08-30T16:53:41Z

9

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T15:26:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/pythia-410m-tulu-v2-mix-GGUF

mradermacher

2024-08-30T16:48:55Z

10

0

transformers

[ "transformers", "gguf", "alignment-handbook", "trl", "sft", "generated_from_trainer", "en", "dataset:allenai/tulu-v2-sft-mixture", "base_model:kykim0/pythia-410m-tulu-v2-mix", "base_model:quantized:kykim0/pythia-410m-tulu-v2-mix", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T16:47:18Z

--- base_model: kykim0/pythia-410m-tulu-v2-mix datasets: - allenai/tulu-v2-sft-mixture language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer --- ## About      static quants of https://huggingface.co/kykim0/pythia-410m-tulu-v2-mix  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q2_K.gguf) | Q2_K | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ3_XS.gguf) | IQ3_XS | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ3_S.gguf) | IQ3_S | 0.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q3_K_S.gguf) | Q3_K_S | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ3_M.gguf) | IQ3_M | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.IQ4_XS.gguf) | IQ4_XS | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q3_K_L.gguf) | Q3_K_L | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q5_K_S.gguf) | Q5_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q5_K_M.gguf) | Q5_K_M | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q6_K.gguf) | Q6_K | 0.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/pythia-410m-tulu-v2-mix-GGUF/resolve/main/pythia-410m-tulu-v2-mix.f16.gguf) | f16 | 0.9 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/pythia-1b-tulu-v2-mix-GGUF

mradermacher

2024-08-30T16:48:09Z

14

0

transformers

[ "transformers", "gguf", "alignment-handbook", "trl", "sft", "generated_from_trainer", "en", "dataset:allenai/tulu-v2-sft-mixture", "base_model:kykim0/pythia-1b-tulu-v2-mix", "base_model:quantized:kykim0/pythia-1b-tulu-v2-mix", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T16:44:01Z

--- base_model: kykim0/pythia-1b-tulu-v2-mix datasets: - allenai/tulu-v2-sft-mixture language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer --- ## About      static quants of https://huggingface.co/kykim0/pythia-1b-tulu-v2-mix  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q2_K.gguf) | Q2_K | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ3_XS.gguf) | IQ3_XS | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ3_S.gguf) | IQ3_S | 0.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q3_K_S.gguf) | Q3_K_S | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ3_M.gguf) | IQ3_M | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.IQ4_XS.gguf) | IQ4_XS | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q3_K_L.gguf) | Q3_K_L | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q5_K_S.gguf) | Q5_K_S | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q5_K_M.gguf) | Q5_K_M | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q6_K.gguf) | Q6_K | 0.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.Q8_0.gguf) | Q8_0 | 1.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/pythia-1b-tulu-v2-mix-GGUF/resolve/main/pythia-1b-tulu-v2-mix.f16.gguf) | f16 | 2.1 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

LoneStriker/LongWriter-llama3.1-8b-6.0bpw-h6-exl2

LoneStriker

2024-08-30T16:39:33Z

10

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "Long Context", "chatglm", "en", "zh", "dataset:THUDM/LongWriter-6k", "arxiv:2408.07055", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]

text-generation

2024-08-30T16:36:45Z

--- language: - en - zh library_name: transformers tags: - Long Context - chatglm - llama datasets: - THUDM/LongWriter-6k license: llama3.1 --- # LongWriter-llama3.1-8b 🤗 <a href="https://huggingface.co/datasets/THUDM/LongWriter-6k" target="_blank">[LongWriter Dataset] </a> • 💻 <a href="https://github.com/THUDM/LongWriter" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2408.07055" target="_blank">[LongWriter Paper]</a> LongWriter-llama3.1-8b is trained based on [Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B), and is capable of generating 10,000+ words at once. Environment: `transformers>=4.43.0` Please ahere to the prompt template (system prompt is optional): `<<SYS>>\n{system prompt}\n<</SYS>>\n\n[INST]{query1}[/INST]{response1}[INST]{query2}[/INST]{response2}...` A simple demo for deployment of the model: ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("THUDM/LongWriter-llama3.1-8b", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("THUDM/LongWriter-llama3.1-8b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") model = model.eval() query = "Write a 10000-word China travel guide" prompt = f"[INST]{query}[/INST]" input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device) context_length = input.input_ids.shape[-1] output = model.generate( **input, max_new_tokens=32768, num_beams=1, do_sample=True, temperature=0.5, )[0] response = tokenizer.decode(output[context_length:], skip_special_tokens=True) print(response) ``` You can also deploy the model with [vllm](https://github.com/vllm-project/vllm), which allows 10,000+ words generation within a minute. Here is an example code: ```python model = LLM( model= "THUDM/LongWriter-llama3.1-8b", dtype="auto", trust_remote_code=True, tensor_parallel_size=1, max_model_len=32768, gpu_memory_utilization=0.5, ) tokenizer = model.get_tokenizer() generation_params = SamplingParams( temperature=0.5, top_p=0.8, top_k=50, max_tokens=32768, repetition_penalty=1, ) query = "Write a 10000-word China travel guide" prompt = f"[INST]{query}[/INST]" input_ids = tokenizer(prompt, truncation=False, return_tensors="pt").input_ids[0].tolist() outputs = model.generate( sampling_params=generation_params, prompt_token_ids=[input_ids], ) output = outputs[0] print(output.outputs[0].text) ``` License: [Llama-3.1 License](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) ## Citation If you find our work useful, please consider citing LongWriter: ``` @article{bai2024longwriter, title={LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs}, author={Yushi Bai and Jiajie Zhang and Xin Lv and Linzhi Zheng and Siqi Zhu and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li}, journal={arXiv preprint arXiv:2408.07055}, year={2024} } ```

Gabrioloruioni/BertRE

Gabrioloruioni

2024-08-30T16:37:34Z

108

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-17T10:13:30Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

anchovy/autogluon-chronos-t5-large

anchovy

2024-08-30T16:29:35Z

44

0

null

[ "safetensors", "t5", "time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series", "time-series-forecasting", "arxiv:2403.07815", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]

time-series-forecasting

2024-08-30T16:29:35Z

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Large) Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <img src="figures/main-figure.png" width="100%"> Fig. 1: High-level depiction of Chronos. (Left) The input time series is scaled and quantized to obtain a sequence of tokens. (Center) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (Right) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | [**chronos-t5-tiny**](https://huggingface.co/amazon/chronos-t5-tiny) | 8M | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) | | [**chronos-t5-mini**](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) | | [**chronos-t5-small**](https://huggingface.co/amazon/chronos-t5-small) | 46M | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) | | [**chronos-t5-base**](https://huggingface.co/amazon/chronos-t5-base) | 200M | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) | | [**chronos-t5-large**](https://huggingface.co/amazon/chronos-t5-large) | 710M | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) | ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-large", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.

anchovy/autogluon-chronos-t5-mini

anchovy

2024-08-30T16:29:21Z

149

0

null

[ "safetensors", "t5", "time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series", "time-series-forecasting", "arxiv:2403.07815", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]

time-series-forecasting

2024-08-30T16:29:20Z

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Mini) Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <img src="figures/main-figure.png" width="100%"> Fig. 1: High-level depiction of Chronos. (Left) The input time series is scaled and quantized to obtain a sequence of tokens. (Center) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (Right) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | [**chronos-t5-tiny**](https://huggingface.co/amazon/chronos-t5-tiny) | 8M | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) | | [**chronos-t5-mini**](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) | | [**chronos-t5-small**](https://huggingface.co/amazon/chronos-t5-small) | 46M | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) | | [**chronos-t5-base**](https://huggingface.co/amazon/chronos-t5-base) | 200M | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) | | [**chronos-t5-large**](https://huggingface.co/amazon/chronos-t5-large) | 710M | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) | ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-mini", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.

anchovy/autogluon-chronos-t5-tiny

anchovy

2024-08-30T16:29:03Z

6

0

null

[ "safetensors", "t5", "time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series", "time-series-forecasting", "arxiv:2403.07815", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]

time-series-forecasting

2024-08-30T16:29:03Z

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Tiny) Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <img src="figures/main-figure.png" width="100%"> Fig. 1: High-level depiction of Chronos. (Left) The input time series is scaled and quantized to obtain a sequence of tokens. (Center) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (Right) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | [**chronos-t5-tiny**](https://huggingface.co/amazon/chronos-t5-tiny) | 8M | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) | | [**chronos-t5-mini**](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) | | [**chronos-t5-small**](https://huggingface.co/amazon/chronos-t5-small) | 46M | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) | | [**chronos-t5-base**](https://huggingface.co/amazon/chronos-t5-base) | 200M | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) | | [**chronos-t5-large**](https://huggingface.co/amazon/chronos-t5-large) | 710M | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) | ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-tiny", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.

RWKV/v5-EagleX-v2-7B-HF

RWKV

2024-08-30T16:26:20Z

86

13

transformers

[ "transformers", "pytorch", "safetensors", "rwkv5", "text-generation", "custom_code", "en", "dataset:cerebras/SlimPajama-627B", "dataset:EleutherAI/pile", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-04-17T21:41:27Z

--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - EleutherAI/pile language: - en --- ![An eagle flying high up in the sky](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F304f2c7a-fc67-4df4-ba57-c6f38f86826c_2688x1536.png) ### Huggingface RWKV EagleX 7B v2 Model > **! Important Note !** > > The following is the HF transformers implementation of the EagleX 7B 2.25T model. This is meant to be used with the huggingface transformers > > [For the full model weights on its own, to use with other RWKV libraries, refer to `RWKV/v5-EagleX-v2-7B-pth`](https://huggingface.co/RWKV/v5-EagleX-v2-7B-pth) > > > This is not an instruct tune model! (soon...) ## Quickstart with the hugging face transformer library ``` model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) ``` ## Evaluation The following shows the progression of the model from 1.1T trained to 2.25T trained. |Model |Eagle-7B-HF|EagleX-7B-HF-v1|EagleX-7B-HF-v2| |----------------------|-----------|---------------|---------------| |Param Count |7.52 B |7.52 B |7.52 B | |Tokens Trained |1.1 T |1.7 T |2.25 T | |avg_acc |0.4822 |0.5391 |0.5495 | |glue (acc) |0.5752 |0.7463 |0.7439 | |anli (acc) |0.3594 |0.4847 |0.5097 | |mnli (acc) |0.3802 |0.7928 |0.7884 | |mnli_mismatch (acc) |0.3687 |0.7985 |0.784 | |swag (acc) |0.568 |0.5814 |0.5905 | |lambada_standard (acc)|0.685 |0.686 |0.7004 | |lambada_openai (acc) |0.7425 |0.7522 |0.7502 | |mmlu (acc) |0.3321 |0.4014 |0.438 | |winogrande (acc) |0.674 |0.7206 |0.7332 | |wnli (acc) |0.4225 |0.4648 |0.493 | |truthfulqa (acc) |0.3303 |0.3268 |0.3401 | |logiqa (acc) |0.2458 |0.2458 |0.2458 | |logiqa2 (acc) |0.2494 |0.2595 |0.2621 | |sciq (acc) |0.955 |0.96 |0.93 | |piqa (acc) |0.7704 |0.7758 |0.7764 | |arc_easy (acc) |0.7382 |0.7555 |0.7445 | |arc_challenge (acc) |0.3951 |0.4087 |0.4155 | |hellaswag (acc) |0.5264 |0.5411 |0.56 | |openbookqa (acc) |0.302 |0.296 |0.304 | |mathqa (acc) |0.26 |0.26 |0.2593 | |arithmetic (acc) |0.245 |0.0634 |0.1703 | Compared against other top performing models in the same weight class. |Model |OLMo-7B |falcon-7b |Llama-2-7b-hf|EagleX-7B-HF-v2|Mistral-7B-v0.1| |----------------------|---------------|----------------|-------------|---------------|---------------| |Param Count |6.89 B |6.92 B |6.74 B |7.52 B |7.24 B | |Tokens Trained |2.5 T |1.5 T |2 T |2.25 T |2 - 7 T? | |avg_acc |0.4578 |0.4775 |0.5045 |0.5495 |0.5676 | |glue (acc) |0.474 |0.4578 |0.4289 |0.7439 |0.515 | |anli (acc) |0.3478 |0.3541 |0.3697 |0.5097 |0.3803 | |mnli (acc) |0.3294 |0.3893 |0.4269 |0.7884 |0.4542 | |mnli_mismatch (acc) |0.3348 |0.404 |0.4395 |0.784 |0.4632 | |swag (acc) |0.5512 |0.5685 |0.5658 |0.5905 |0.5756 | |lambada_standard (acc)|0.6396 |0.6868 |0.6808 |0.7004 |0.6944 | |lambada_openai (acc) |0.6872 |0.746 |0.7353 |0.7502 |0.7553 | |mmlu (acc) |0.2812 |0.2512 |0.4077 |0.438 |0.5964 | |winogrande (acc) |0.6725 |0.6709 |0.6914 |0.7332 |0.7364 | |wnli (acc) |0.5775 |0.4789 |0.4648 |0.493 |0.5775 | |truthfulqa (acc) |0.3015 |0.2826 |0.3205 |0.3401 |0.3537 | |logiqa (acc) |0.2335 |0.2151 |0.2535 |0.2458 |0.2427 | |logiqa2 (acc) |0.2506 |0.2252 |0.2564 |0.2621 |0.3022 | |sciq (acc) |0.927 |0.944 |0.939 |0.93 |0.959 | |piqa (acc) |0.7878 |0.7949 |0.7807 |0.7764 |0.8052 | |arc_easy (acc) |0.7353 |0.7479 |0.7643 |0.7445 |0.8081 | |arc_challenge (acc) |0.3677 |0.4027 |0.4309 |0.4155 |0.5009 | |hellaswag (acc) |0.5572 |0.5772 |0.5713 |0.56 |0.6131 | |openbookqa (acc) |0.292 |0.306 |0.316 |0.304 |0.33 | |mathqa (acc) |0.26 |0.2884 |0.2801 |0.2593 |0.3554 | |arithmetic (acc) |0.0069 |0.2367 |0.4703 |0.1703 |0.9004 | See the following, for the full details on this model: [https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b) #### Running on CPU via HF transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') input = input.strip().replace('\r\n','\n').replace('\n\n','\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) text = "请介绍北京的旅游景点" prompt = generate_prompt(text) inputs = tokenizer(prompt, return_tensors="pt") output = model.generate(inputs["input_ids"], max_new_tokens=333, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 请介绍北京的旅游景点 Assistant: 北京是中国的首都，拥有众多的旅游景点，以下是其中一些著名的景点： 1. 故宫：位于北京市中心，是明清两代的皇宫，内有大量的文物和艺术品。 2. 天安门广场：是中国最著名的广场之一，是中国人民政治协商会议的旧址，也是中国人民政治协商会议的中心。 3. 颐和园：是中国古代皇家园林之一，有着悠久的历史和丰富的文化内涵。 4. 长城：是中国古代的一道长城，全长约万里，是中国最著名的旅游景点之一。 5. 北京大学：是中国著名的高等教育机构之一，有着悠久的历史和丰富的文化内涵。 6. 北京动物园：是中国最大的动物园之一，有着丰富的动物资源和丰富的文化内涵。 7. 故宫博物院：是中国最著名的博物馆之一，收藏了大量的文物和艺术品，是中国最重要的文化遗产之一。 8. 天坛：是中国古代皇家 ``` #### Running on GPU via HF transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') input = input.strip().replace('\r\n','\n').replace('\n\n','\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True, torch_dtype=torch.float16).to(0) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) text = "介绍一下大熊猫" prompt = generate_prompt(text) inputs = tokenizer(prompt, return_tensors="pt").to(0) output = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 介绍一下大熊猫 Assistant: 大熊猫是一种中国特有的哺乳动物，也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体，有着黑色的毛发和白色的耳朵。大熊猫的食物主要是竹子，它们会在竹林中寻找竹子，并且会将竹子放在竹笼中进行储存。大熊猫的寿命约为20至30年，但由于栖息地的丧失和人类活动的 ``` #### Batch Inference ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n', '\n').replace('\n\n', '\n') input = input.strip().replace('\r\n', '\n').replace('\n\n', '\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) texts = ["请介绍北京的旅游景点", "介绍一下大熊猫", "乌兰察布"] prompts = [generate_prompt(text) for text in texts] inputs = tokenizer(prompts, return_tensors="pt", padding=True) outputs = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) for output in outputs: print(tokenizer.decode(output.tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 请介绍北京的旅游景点 Assistant: 北京是中国的首都，拥有丰富的旅游资源和历史文化遗产。以下是一些北京的旅游景点： 1. 故宫：位于北京市中心，是明清两代的皇宫，是中国最大的古代宫殿建筑群之一。 2. 天安门广场：位于北京市中心，是中国最著名的城市广场之一，也是中国最大的城市广场。 3. 颐和 User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 介绍一下大熊猫 Assistant: 大熊猫是一种生活在中国中部地区的哺乳动物，也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体，有着黑色的毛发和圆圆的眼睛。大熊猫是一种濒危物种，目前只有在野外的几个保护区才能看到它们的身影。大熊猫的食物主要是竹子，它们会在竹子上寻找食物，并且可以通 User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 乌兰察布 Assistant: 乌兰察布是中国新疆维吾尔自治区的一个县级市，位于新疆维吾尔自治区中部，是新疆的第二大城市。乌兰察布市是新疆的第一大城市，也是新疆的重要城市之一。乌兰察布市是新疆的经济中心，也是新疆的重要交通枢纽之一。乌兰察布市的人口约为2.5万人，其中汉族占绝大多数。乌 ``` ## Links - [Our wiki](https://wiki.rwkv.com) - [Full eval data](https://docs.google.com/spreadsheets/d/1CBLU6yKkW-8FMvGD4INO3qjeHZ0qkKnZFcM6n6lWNOs/edit#gid=912381775) - [Recursal.AI Cloud Platform](https://recursal.ai) - [HF Gradio Demo](https://huggingface.co/spaces/RWKV/v5-EagleX-v2-7B-gradio) - [Blog article, detailing our model launch](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b) ## Acknowledgement We are grateful for the help and support from the following key groups: - [Recursal.ai](https://recursal.ai) team for financing the GPU resources, and managing the training of this foundation model - you can run the Eagle line of RWKV models on their cloud / on-premise platform today. - EleutherAI for their support, especially in the v5/v6 Eagle/Finch paper - Linux Foundation AI & Data group for supporting and hosting the RWKV project

Bajiyo/whisper-medium-studio-records_test

Bajiyo

2024-08-30T16:25:51Z

11

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-08-30T10:27:48Z

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-studio-records_test results: [] ---  # whisper-medium-studio-records_test This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0333 - Wer: 15.6507 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.0568 | 0.4110 | 1000 | 0.0894 | 43.3939 | | 0.0362 | 0.8220 | 2000 | 0.0589 | 29.9079 | | 0.0149 | 1.2330 | 3000 | 0.0463 | 22.6922 | | 0.0117 | 1.6441 | 4000 | 0.0375 | 19.2088 | | 0.0039 | 2.0551 | 5000 | 0.0355 | 16.1483 | | 0.0032 | 2.4661 | 6000 | 0.0333 | 15.6507 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

mradermacher/TwinLlama-3.1-8B-DPO2-GGUF

mradermacher

2024-08-30T16:17:09Z

7

0

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "dpo", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T15:50:20Z

--- base_model: mlabonne/TwinLlama-3.1-8B-DPO2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - dpo --- ## About      static quants of https://huggingface.co/mlabonne/TwinLlama-3.1-8B-DPO2  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/TwinLlama-3.1-8B-DPO2-GGUF/resolve/main/TwinLlama-3.1-8B-DPO2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

ProElectro07/BioMistral-sharded

ProElectro07

2024-08-30T16:13:23Z

7

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T16:08:41Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

CharlesLi/OpenELM-1_1B-DPO-full-1-5

CharlesLi

2024-08-30T16:09:04Z

133

0

transformers

[ "transformers", "tensorboard", "safetensors", "openelm", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "custom_code", "autotrain_compatible", "region:us" ]

text-generation

2024-08-30T08:21:20Z

--- library_name: transformers tags: - trl - dpo - generated_from_trainer model-index: - name: OpenELM-1_1B-DPO-full-1-5 results: [] ---  # OpenELM-1_1B-DPO-full-1-5 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1836 - Rewards/chosen: -14.0 - Rewards/rejected: -17.625 - Rewards/accuracies: 0.7227 - Rewards/margins: 3.625 - Logps/rejected: -2048.0 - Logps/chosen: -1720.0 - Logits/rejected: 4.2812 - Logits/chosen: 2.625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6268 | 0.1047 | 100 | 0.6449 | -0.4805 | -0.6680 | 0.6406 | 0.1885 | -356.0 | -366.0 | -9.5625 | -10.0 | | 0.5924 | 0.2093 | 200 | 0.5985 | -1.2031 | -1.6172 | 0.6875 | 0.4199 | -450.0 | -438.0 | -12.875 | -13.125 | | 0.6197 | 0.3140 | 300 | 0.5811 | -1.375 | -1.8438 | 0.7090 | 0.4668 | -474.0 | -456.0 | -11.75 | -12.1875 | | 0.5968 | 0.4186 | 400 | 0.5933 | -2.3125 | -2.8438 | 0.6934 | 0.5273 | -572.0 | -548.0 | -8.5625 | -9.25 | | 0.5854 | 0.5233 | 500 | 0.5737 | -1.7422 | -2.2812 | 0.6953 | 0.5352 | -516.0 | -492.0 | -7.7188 | -8.625 | | 0.5524 | 0.6279 | 600 | 0.5768 | -3.0156 | -3.7031 | 0.6914 | 0.6953 | -660.0 | -620.0 | -7.0312 | -7.7188 | | 0.5602 | 0.7326 | 700 | 0.5756 | -3.1562 | -3.9062 | 0.7168 | 0.75 | -680.0 | -636.0 | -5.125 | -6.3438 | | 0.5581 | 0.8373 | 800 | 0.5854 | -3.3906 | -4.0312 | 0.6914 | 0.6289 | -692.0 | -656.0 | -5.0938 | -5.9688 | | 0.5793 | 0.9419 | 900 | 0.5657 | -3.1719 | -3.9062 | 0.7207 | 0.7383 | -680.0 | -636.0 | -3.9531 | -5.0312 | | 0.2783 | 1.0466 | 1000 | 0.6053 | -4.75 | -5.875 | 0.7188 | 1.125 | -876.0 | -792.0 | -2.2188 | -3.3594 | | 0.2417 | 1.1512 | 1100 | 0.6139 | -4.7812 | -5.8125 | 0.7070 | 1.0469 | -872.0 | -796.0 | -2.3594 | -4.125 | | 0.2429 | 1.2559 | 1200 | 0.5897 | -5.7188 | -6.8125 | 0.7227 | 1.0781 | -968.0 | -892.0 | -0.7188 | -2.1719 | | 0.2508 | 1.3605 | 1300 | 0.5948 | -5.4062 | -6.4062 | 0.6914 | 1.0 | -928.0 | -860.0 | -0.0104 | -1.5156 | | 0.2169 | 1.4652 | 1400 | 0.6104 | -5.7812 | -6.9062 | 0.7031 | 1.1016 | -976.0 | -896.0 | 0.0820 | -1.75 | | 0.2107 | 1.5699 | 1500 | 0.6062 | -6.0625 | -7.2812 | 0.6973 | 1.1953 | -1016.0 | -924.0 | -0.4590 | -2.1719 | | 0.2472 | 1.6745 | 1600 | 0.6158 | -5.625 | -6.7188 | 0.7070 | 1.1016 | -960.0 | -880.0 | -2.0312 | -3.9688 | | 0.2545 | 1.7792 | 1700 | 0.6170 | -6.25 | -7.5 | 0.7031 | 1.25 | -1040.0 | -944.0 | -1.2578 | -3.2031 | | 0.2383 | 1.8838 | 1800 | 0.6061 | -5.625 | -6.75 | 0.7012 | 1.1172 | -964.0 | -880.0 | 0.7383 | -1.1328 | | 0.2107 | 1.9885 | 1900 | 0.6135 | -6.5 | -7.7812 | 0.7383 | 1.2578 | -1064.0 | -968.0 | 0.3027 | -1.4297 | | 0.0186 | 2.0931 | 2000 | 0.7473 | -8.0625 | -9.875 | 0.7090 | 1.8594 | -1280.0 | -1120.0 | 2.2812 | 0.4980 | | 0.03 | 2.1978 | 2100 | 0.8345 | -9.9375 | -12.25 | 0.7070 | 2.2812 | -1512.0 | -1312.0 | 3.2031 | 1.5938 | | 0.0284 | 2.3025 | 2200 | 0.7741 | -9.1875 | -11.3125 | 0.7012 | 2.0781 | -1416.0 | -1240.0 | 2.7812 | 1.0156 | | 0.0352 | 2.4071 | 2300 | 0.7983 | -9.3125 | -11.3125 | 0.7090 | 2.0156 | -1424.0 | -1248.0 | 2.6406 | 0.9961 | | 0.0345 | 2.5118 | 2400 | 0.8249 | -9.8125 | -12.0 | 0.7266 | 2.1719 | -1488.0 | -1304.0 | 3.2656 | 1.5625 | | 0.0192 | 2.6164 | 2500 | 0.8865 | -10.25 | -12.5625 | 0.6973 | 2.2969 | -1544.0 | -1344.0 | 3.5938 | 1.9609 | | 0.0261 | 2.7211 | 2600 | 0.7963 | -9.1875 | -11.4375 | 0.7129 | 2.25 | -1432.0 | -1240.0 | 2.7031 | 0.8672 | | 0.0315 | 2.8257 | 2700 | 0.7619 | -9.0 | -10.9375 | 0.7109 | 1.9766 | -1384.0 | -1216.0 | 2.8594 | 0.8320 | | 0.0293 | 2.9304 | 2800 | 0.8241 | -9.75 | -12.0625 | 0.7070 | 2.2656 | -1496.0 | -1296.0 | 3.1719 | 1.3359 | | 0.0071 | 3.0351 | 2900 | 0.8609 | -10.0625 | -12.5 | 0.7188 | 2.3906 | -1536.0 | -1328.0 | 3.1719 | 1.3125 | | 0.0099 | 3.1397 | 3000 | 0.9558 | -11.5 | -14.1875 | 0.7051 | 2.6875 | -1704.0 | -1472.0 | 3.4062 | 1.6484 | | 0.0079 | 3.2444 | 3100 | 0.9341 | -11.125 | -13.75 | 0.7090 | 2.6562 | -1664.0 | -1432.0 | 3.25 | 1.5078 | | 0.0104 | 3.3490 | 3200 | 0.9926 | -11.9375 | -14.8125 | 0.7090 | 2.9062 | -1768.0 | -1512.0 | 3.6719 | 1.9922 | | 0.0089 | 3.4537 | 3300 | 0.9665 | -11.9375 | -14.8125 | 0.7188 | 2.875 | -1768.0 | -1512.0 | 3.8594 | 2.2656 | | 0.0098 | 3.5583 | 3400 | 0.9548 | -11.1875 | -13.875 | 0.7109 | 2.75 | -1680.0 | -1432.0 | 4.0 | 2.3438 | | 0.0109 | 3.6630 | 3500 | 1.0670 | -12.5625 | -15.6875 | 0.7168 | 3.1406 | -1856.0 | -1576.0 | 4.1875 | 2.5312 | | 0.0081 | 3.7677 | 3600 | 1.0376 | -12.375 | -15.4375 | 0.7188 | 3.0938 | -1832.0 | -1552.0 | 4.125 | 2.4844 | | 0.0081 | 3.8723 | 3700 | 1.0725 | -13.0 | -16.25 | 0.7168 | 3.25 | -1912.0 | -1616.0 | 4.1875 | 2.5938 | | 0.0041 | 3.9770 | 3800 | 1.1346 | -13.5 | -17.0 | 0.7188 | 3.4688 | -1984.0 | -1672.0 | 4.2188 | 2.5781 | | 0.0036 | 4.0816 | 3900 | 1.1589 | -13.8125 | -17.375 | 0.7168 | 3.5156 | -2024.0 | -1696.0 | 4.25 | 2.625 | | 0.0016 | 4.1863 | 4000 | 1.1790 | -14.0625 | -17.625 | 0.7168 | 3.5781 | -2048.0 | -1720.0 | 4.2812 | 2.6719 | | 0.0037 | 4.2909 | 4100 | 1.1847 | -14.0625 | -17.625 | 0.7168 | 3.6094 | -2064.0 | -1728.0 | 4.3125 | 2.6562 | | 0.007 | 4.3956 | 4200 | 1.1905 | -14.1875 | -17.75 | 0.7227 | 3.6406 | -2064.0 | -1736.0 | 4.3125 | 2.6719 | | 0.0038 | 4.5003 | 4300 | 1.1835 | -14.0625 | -17.75 | 0.7207 | 3.6406 | -2064.0 | -1728.0 | 4.2812 | 2.6406 | | 0.0093 | 4.6049 | 4400 | 1.1819 | -14.0625 | -17.625 | 0.7207 | 3.625 | -2048.0 | -1720.0 | 4.2812 | 2.625 | | 0.006 | 4.7096 | 4500 | 1.1817 | -14.0 | -17.625 | 0.7227 | 3.6406 | -2048.0 | -1720.0 | 4.2812 | 2.6094 | | 0.0037 | 4.8142 | 4600 | 1.1826 | -14.0 | -17.625 | 0.7227 | 3.6406 | -2048.0 | -1720.0 | 4.25 | 2.6094 | | 0.0059 | 4.9189 | 4700 | 1.1836 | -14.0 | -17.625 | 0.7227 | 3.625 | -2048.0 | -1720.0 | 4.2812 | 2.625 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1

mradermacher/NeuroCom_4B-GGUF

mradermacher

2024-08-30T16:03:11Z

20

0

transformers

[ "transformers", "gguf", "en", "base_model:FourOhFour/NeuroCom_4B", "base_model:quantized:FourOhFour/NeuroCom_4B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T15:34:36Z

--- base_model: FourOhFour/NeuroCom_4B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/FourOhFour/NeuroCom_4B  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q2_K.gguf) | Q2_K | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ3_XS.gguf) | IQ3_XS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q3_K_S.gguf) | Q3_K_S | 2.2 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ3_S.gguf) | IQ3_S | 2.2 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ3_M.gguf) | IQ3_M | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q3_K_M.gguf) | Q3_K_M | 2.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q3_K_L.gguf) | Q3_K_L | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.IQ4_XS.gguf) | IQ4_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q4_K_S.gguf) | Q4_K_S | 2.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q4_K_M.gguf) | Q4_K_M | 2.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q5_K_S.gguf) | Q5_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q5_K_M.gguf) | Q5_K_M | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q6_K.gguf) | Q6_K | 3.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/NeuroCom_4B-GGUF/resolve/main/NeuroCom_4B.Q8_0.gguf) | Q8_0 | 4.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

AQ206/models-that-will-literally-shatter-your-right-leg

AQ206

2024-08-30T15:59:26Z

0

null

[ "license:openrail", "region:us" ]

null

2023-06-30T16:56:10Z

--- license: openrail --- if you use any of these models on rvc... your right leg will absolutely shatter into pieces! if you want to keep your right leg, be cautious.

mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-64-5e

mrm8488

2024-08-30T15:54:21Z

74

2

sentence-transformers

[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "dataset_size:1K<n<10K", "loss:MatryoshkaLoss", "loss:CoSENTLoss", "arxiv:1908.10084", "arxiv:2205.13147", "base_model:intfloat/multilingual-e5-large", "base_model:finetune:intfloat/multilingual-e5-large", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-01T22:13:40Z

--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:1K<n<10K - loss:MatryoshkaLoss - loss:CoSENTLoss base_model: intfloat/multilingual-e5-large metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: El hombre captura una pelota sentences: - Un hombre lanza una pelota en el aire. - Un hombre se encuentra tocando una flauta de madera. - La mujer está maquillándose usando sombra de ojos. - source_sentence: Un hombre está buscando algo. sentences: - En un mercado de granjeros, se encuentra un hombre. - Se acerca a la pista un avión suizo de color blanco. - dos chicas jóvenes se abrazan en la hierba. - source_sentence: El avión está tocando tierra. sentences: - El avión animado se encuentra en proceso de aterrizaje. - La capital de Siria fue golpeada por dos explosiones - Violentos incidentes afectan a estudiantes chinos en Francia - source_sentence: Un hombre saltando la cuerda. sentences: - Un hombre está saltando la cuerda. - Una mujer entrena a su perro para saltar en el aire. - Los gatitos están comiendo de los platos. - source_sentence: tres perros gruñendo entre sí sentences: - Dos perros se aproximan uno al otro en el pasto. - Una mujer sonriente brinda cariño a un pequeño bebé. - Una mujer está montando a caballo en el campo. pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-large results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 768 type: sts-dev-768 metrics: - type: pearson_cosine value: 0.8279951103268512 name: Pearson Cosine - type: spearman_cosine value: 0.8342643795984531 name: Spearman Cosine - type: pearson_manhattan value: 0.8228439538329566 name: Pearson Manhattan - type: spearman_manhattan value: 0.834870903153992 name: Spearman Manhattan - type: pearson_euclidean value: 0.8231076969394738 name: Pearson Euclidean - type: spearman_euclidean value: 0.8349270059177344 name: Spearman Euclidean - type: pearson_dot value: 0.8196281042113861 name: Pearson Dot - type: spearman_dot value: 0.8248683461954115 name: Spearman Dot - type: pearson_max value: 0.8279951103268512 name: Pearson Max - type: spearman_max value: 0.8349270059177344 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 512 type: sts-dev-512 metrics: - type: pearson_cosine value: 0.8236357426336446 name: Pearson Cosine - type: spearman_cosine value: 0.8332692872015282 name: Spearman Cosine - type: pearson_manhattan value: 0.8217552769156274 name: Pearson Manhattan - type: spearman_manhattan value: 0.8331746060276878 name: Spearman Manhattan - type: pearson_euclidean value: 0.8217859136681092 name: Pearson Euclidean - type: spearman_euclidean value: 0.8334069456110773 name: Spearman Euclidean - type: pearson_dot value: 0.8101789790612713 name: Pearson Dot - type: spearman_dot value: 0.8179205607773823 name: Spearman Dot - type: pearson_max value: 0.8236357426336446 name: Pearson Max - type: spearman_max value: 0.8334069456110773 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 256 type: sts-dev-256 metrics: - type: pearson_cosine value: 0.816222860848086 name: Pearson Cosine - type: spearman_cosine value: 0.8303708513421737 name: Spearman Cosine - type: pearson_manhattan value: 0.8178715987143794 name: Pearson Manhattan - type: spearman_manhattan value: 0.8301047046554985 name: Spearman Manhattan - type: pearson_euclidean value: 0.8183826652089494 name: Pearson Euclidean - type: spearman_euclidean value: 0.8301804247624904 name: Spearman Euclidean - type: pearson_dot value: 0.7878741921967743 name: Pearson Dot - type: spearman_dot value: 0.7904844114269662 name: Spearman Dot - type: pearson_max value: 0.8183826652089494 name: Pearson Max - type: spearman_max value: 0.8303708513421737 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 128 type: sts-dev-128 metrics: - type: pearson_cosine value: 0.794202606017138 name: Pearson Cosine - type: spearman_cosine value: 0.8198385906414491 name: Spearman Cosine - type: pearson_manhattan value: 0.8088714046889546 name: Pearson Manhattan - type: spearman_manhattan value: 0.8222921243120748 name: Spearman Manhattan - type: pearson_euclidean value: 0.8092312345267045 name: Pearson Euclidean - type: spearman_euclidean value: 0.8220266161646009 name: Spearman Euclidean - type: pearson_dot value: 0.7341586721030032 name: Pearson Dot - type: spearman_dot value: 0.7351749794310246 name: Spearman Dot - type: pearson_max value: 0.8092312345267045 name: Pearson Max - type: spearman_max value: 0.8222921243120748 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 64 type: sts-dev-64 metrics: - type: pearson_cosine value: 0.7727295051414095 name: Pearson Cosine - type: spearman_cosine value: 0.8076629783565549 name: Spearman Cosine - type: pearson_manhattan value: 0.7976419723073269 name: Pearson Manhattan - type: spearman_manhattan value: 0.8147883308842346 name: Spearman Manhattan - type: pearson_euclidean value: 0.7979124462870892 name: Pearson Euclidean - type: spearman_euclidean value: 0.8123832197697319 name: Spearman Euclidean - type: pearson_dot value: 0.6725844492342726 name: Pearson Dot - type: spearman_dot value: 0.6673162832940408 name: Spearman Dot - type: pearson_max value: 0.7979124462870892 name: Pearson Max - type: spearman_max value: 0.8147883308842346 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: 0.8630482725201897 name: Pearson Cosine - type: spearman_cosine value: 0.8813284718659181 name: Spearman Cosine - type: pearson_manhattan value: 0.8770818288812614 name: Pearson Manhattan - type: spearman_manhattan value: 0.8810971983428288 name: Spearman Manhattan - type: pearson_euclidean value: 0.8770132070253477 name: Pearson Euclidean - type: spearman_euclidean value: 0.8812162173545179 name: Spearman Euclidean - type: pearson_dot value: 0.8581811981775829 name: Pearson Dot - type: spearman_dot value: 0.8707402246720045 name: Spearman Dot - type: pearson_max value: 0.8770818288812614 name: Pearson Max - type: spearman_max value: 0.8813284718659181 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.8589909139210625 name: Pearson Cosine - type: spearman_cosine value: 0.8799604919891442 name: Spearman Cosine - type: pearson_manhattan value: 0.8744468387217347 name: Pearson Manhattan - type: spearman_manhattan value: 0.8791142262015441 name: Spearman Manhattan - type: pearson_euclidean value: 0.8747974723064821 name: Pearson Euclidean - type: spearman_euclidean value: 0.8795698184784307 name: Spearman Euclidean - type: pearson_dot value: 0.8464185524060444 name: Pearson Dot - type: spearman_dot value: 0.8549652098582826 name: Spearman Dot - type: pearson_max value: 0.8747974723064821 name: Pearson Max - type: spearman_max value: 0.8799604919891442 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.8528262537030415 name: Pearson Cosine - type: spearman_cosine value: 0.8762917275750132 name: Spearman Cosine - type: pearson_manhattan value: 0.8715060008387856 name: Pearson Manhattan - type: spearman_manhattan value: 0.8780718380107112 name: Spearman Manhattan - type: pearson_euclidean value: 0.87251419758469 name: Pearson Euclidean - type: spearman_euclidean value: 0.8788770265821976 name: Spearman Euclidean - type: pearson_dot value: 0.801980870958869 name: Pearson Dot - type: spearman_dot value: 0.8007112694661982 name: Spearman Dot - type: pearson_max value: 0.87251419758469 name: Pearson Max - type: spearman_max value: 0.8788770265821976 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.8392066286150661 name: Pearson Cosine - type: spearman_cosine value: 0.8692426944903685 name: Spearman Cosine - type: pearson_manhattan value: 0.8631603748425567 name: Pearson Manhattan - type: spearman_manhattan value: 0.8715673768304316 name: Spearman Manhattan - type: pearson_euclidean value: 0.8643871758114816 name: Pearson Euclidean - type: spearman_euclidean value: 0.8724091426441261 name: Spearman Euclidean - type: pearson_dot value: 0.7461565194503229 name: Pearson Dot - type: spearman_dot value: 0.7403017354497338 name: Spearman Dot - type: pearson_max value: 0.8643871758114816 name: Pearson Max - type: spearman_max value: 0.8724091426441261 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.8213671607347727 name: Pearson Cosine - type: spearman_cosine value: 0.8621003145087452 name: Spearman Cosine - type: pearson_manhattan value: 0.8530869243121955 name: Pearson Manhattan - type: spearman_manhattan value: 0.8631973638935834 name: Spearman Manhattan - type: pearson_euclidean value: 0.854140567169475 name: Pearson Euclidean - type: spearman_euclidean value: 0.8632627342101252 name: Spearman Euclidean - type: pearson_dot value: 0.6853599968011839 name: Pearson Dot - type: spearman_dot value: 0.6726454086764928 name: Spearman Dot - type: pearson_max value: 0.854140567169475 name: Pearson Max - type: spearman_max value: 0.8632627342101252 name: Spearman Max --- # SentenceTransformer based on intfloat/multilingual-e5-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the clibrain/stsb_multi_es_aug_gpt3.5-turbo_2 dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - stsb_multi_es_aug   ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-64-5e") # Run inference sentences = [ 'tres perros gruñendo entre sí', 'Dos perros se aproximan uno al otro en el pasto.', 'Una mujer sonriente brinda cariño a un pequeño bebé.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev-768` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.828 | | **spearman_cosine** | **0.8343** | | pearson_manhattan | 0.8228 | | spearman_manhattan | 0.8349 | | pearson_euclidean | 0.8231 | | spearman_euclidean | 0.8349 | | pearson_dot | 0.8196 | | spearman_dot | 0.8249 | | pearson_max | 0.828 | | spearman_max | 0.8349 | #### Semantic Similarity * Dataset: `sts-dev-512` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8236 | | **spearman_cosine** | **0.8333** | | pearson_manhattan | 0.8218 | | spearman_manhattan | 0.8332 | | pearson_euclidean | 0.8218 | | spearman_euclidean | 0.8334 | | pearson_dot | 0.8102 | | spearman_dot | 0.8179 | | pearson_max | 0.8236 | | spearman_max | 0.8334 | #### Semantic Similarity * Dataset: `sts-dev-256` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8162 | | **spearman_cosine** | **0.8304** | | pearson_manhattan | 0.8179 | | spearman_manhattan | 0.8301 | | pearson_euclidean | 0.8184 | | spearman_euclidean | 0.8302 | | pearson_dot | 0.7879 | | spearman_dot | 0.7905 | | pearson_max | 0.8184 | | spearman_max | 0.8304 | #### Semantic Similarity * Dataset: `sts-dev-128` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7942 | | **spearman_cosine** | **0.8198** | | pearson_manhattan | 0.8089 | | spearman_manhattan | 0.8223 | | pearson_euclidean | 0.8092 | | spearman_euclidean | 0.822 | | pearson_dot | 0.7342 | | spearman_dot | 0.7352 | | pearson_max | 0.8092 | | spearman_max | 0.8223 | #### Semantic Similarity * Dataset: `sts-dev-64` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7727 | | **spearman_cosine** | **0.8077** | | pearson_manhattan | 0.7976 | | spearman_manhattan | 0.8148 | | pearson_euclidean | 0.7979 | | spearman_euclidean | 0.8124 | | pearson_dot | 0.6726 | | spearman_dot | 0.6673 | | pearson_max | 0.7979 | | spearman_max | 0.8148 | #### Semantic Similarity * Dataset: `sts-test-768` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.863 | | **spearman_cosine** | **0.8813** | | pearson_manhattan | 0.8771 | | spearman_manhattan | 0.8811 | | pearson_euclidean | 0.877 | | spearman_euclidean | 0.8812 | | pearson_dot | 0.8582 | | spearman_dot | 0.8707 | | pearson_max | 0.8771 | | spearman_max | 0.8813 | #### Semantic Similarity * Dataset: `sts-test-512` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:---------| | pearson_cosine | 0.859 | | **spearman_cosine** | **0.88** | | pearson_manhattan | 0.8744 | | spearman_manhattan | 0.8791 | | pearson_euclidean | 0.8748 | | spearman_euclidean | 0.8796 | | pearson_dot | 0.8464 | | spearman_dot | 0.855 | | pearson_max | 0.8748 | | spearman_max | 0.88 | #### Semantic Similarity * Dataset: `sts-test-256` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8528 | | **spearman_cosine** | **0.8763** | | pearson_manhattan | 0.8715 | | spearman_manhattan | 0.8781 | | pearson_euclidean | 0.8725 | | spearman_euclidean | 0.8789 | | pearson_dot | 0.802 | | spearman_dot | 0.8007 | | pearson_max | 0.8725 | | spearman_max | 0.8789 | #### Semantic Similarity * Dataset: `sts-test-128` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8392 | | **spearman_cosine** | **0.8692** | | pearson_manhattan | 0.8632 | | spearman_manhattan | 0.8716 | | pearson_euclidean | 0.8644 | | spearman_euclidean | 0.8724 | | pearson_dot | 0.7462 | | spearman_dot | 0.7403 | | pearson_max | 0.8644 | | spearman_max | 0.8724 | #### Semantic Similarity * Dataset: `sts-test-64` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8214 | | **spearman_cosine** | **0.8621** | | pearson_manhattan | 0.8531 | | spearman_manhattan | 0.8632 | | pearson_euclidean | 0.8541 | | spearman_euclidean | 0.8633 | | pearson_dot | 0.6854 | | spearman_dot | 0.6726 | | pearson_max | 0.8541 | | spearman_max | 0.8633 |   ## Training Details ### Training Dataset #### stsb_multi_es_aug * Dataset: stsb_multi_es_aug * Size: 2,697 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 8 tokens</li><li>mean: 22.25 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 22.01 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.67</li><li>max: 5.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------| | <code>El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha.</code> | <code>Un ave de color amarillo descansaba tranquilamente en una rama.</code> | <code>3.200000047683716</code> | | <code>Una chica está tocando la flauta en un parque.</code> | <code>Un grupo de músicos está tocando en un escenario al aire libre.</code> | <code>1.286</code> | | <code>La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece</code> | <code>La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere</code> | <code>4.199999809265137</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### stsb_multi_es_aug * Dataset: stsb_multi_es_aug * Size: 697 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 8 tokens</li><li>mean: 22.76 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 22.26 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.3</li><li>max: 5.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------| | <code>Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas.</code> | <code>Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos.</code> | <code>4.199999809265137</code> | | <code>"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud"</code> | <code>"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar."</code> | <code>3.5</code> | | <code>El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario.</code> | <code>Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida.</code> | <code>3.691999912261963</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | loss | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine | |:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | 0.5917 | 100 | 21.7032 | 21.7030 | 0.8030 | 0.8124 | 0.8205 | 0.7839 | 0.8215 | - | - | - | - | - | | 1.1834 | 200 | 21.4019 | 24.0898 | 0.7839 | 0.7972 | 0.8038 | 0.7680 | 0.8062 | - | - | - | - | - | | 1.7751 | 300 | 21.2168 | 22.5421 | 0.7909 | 0.8027 | 0.8058 | 0.7786 | 0.8068 | - | - | - | - | - | | 2.3669 | 400 | 20.7049 | 23.6522 | 0.7938 | 0.8049 | 0.8108 | 0.7873 | 0.8123 | - | - | - | - | - | | 2.9586 | 500 | 20.5077 | 23.6100 | 0.8017 | 0.8116 | 0.8155 | 0.7893 | 0.8185 | - | - | - | - | - | | 3.5503 | 600 | 19.2725 | 24.7539 | 0.8133 | 0.8254 | 0.8291 | 0.8032 | 0.8314 | - | - | - | - | - | | 4.1420 | 700 | 19.0841 | 26.5286 | 0.8210 | 0.8298 | 0.8333 | 0.8102 | 0.8333 | - | - | - | - | - | | 4.7337 | 800 | 18.6847 | 26.8158 | 0.8198 | 0.8304 | 0.8333 | 0.8077 | 0.8343 | - | - | - | - | - | | 5.0 | 845 | - | - | - | - | - | - | - | 0.8692 | 0.8763 | 0.8800 | 0.8621 | 0.8813 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - Transformers: 4.41.1 - PyTorch: 2.3.0+cu121 - Accelerate: 0.30.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```

mradermacher/Westlake-Scribe-GGUF

mradermacher

2024-08-30T15:54:08Z

46

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:DazzlingXeno/Westlake-Scribe", "base_model:quantized:DazzlingXeno/Westlake-Scribe", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T10:45:30Z

--- base_model: DazzlingXeno/Westlake-Scribe language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/DazzlingXeno/Westlake-Scribe  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Westlake-Scribe-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q2_K.gguf) | Q2_K | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ3_XS.gguf) | IQ3_XS | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q3_K_S.gguf) | Q3_K_S | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ3_S.gguf) | IQ3_S | 7.9 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ3_M.gguf) | IQ3_M | 8.2 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q3_K_M.gguf) | Q3_K_M | 8.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q3_K_L.gguf) | Q3_K_L | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.IQ4_XS.gguf) | IQ4_XS | 9.8 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q4_K_S.gguf) | Q4_K_S | 10.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q4_K_M.gguf) | Q4_K_M | 10.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q5_K_S.gguf) | Q5_K_S | 12.4 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q5_K_M.gguf) | Q5_K_M | 12.8 | | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q6_K.gguf) | Q6_K | 14.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Westlake-Scribe-GGUF/resolve/main/Westlake-Scribe.Q8_0.gguf) | Q8_0 | 19.2 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

bif02/eb_t5_base_16

bif02

2024-08-30T15:53:17Z

5

0

null

[ "tensorboard", "safetensors", "t5", "generated_from_trainer", "base_model:bif02/eb_t5_base_16", "base_model:finetune:bif02/eb_t5_base_16", "license:apache-2.0", "region:us" ]

null

2024-08-25T12:11:37Z

--- license: apache-2.0 base_model: bif02/eb_t5_base_16 tags: - generated_from_trainer model-index: - name: eb_t5_base_16 results: [] ---  # eb_t5_base_16 This model is a fine-tuned version of [bif02/eb_t5_base_16](https://huggingface.co/bif02/eb_t5_base_16) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0058 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0031 | 1.0 | 1107 | 0.0058 | ### Framework versions - Transformers 4.42.4 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

leap-llm/Meta-Llama-3-8B-Instruct-sft-alfworld-iter2-3e-5

leap-llm

2024-08-30T15:42:35Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T15:36:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BishFish/DialoGPT-small-edward

BishFish

2024-08-30T15:39:26Z

117

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-29T14:08:36Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

julienkay/stable-diffusion-v1-5

julienkay

2024-08-30T15:39:14Z

6

1

diffusers

[ "diffusers", "onnx", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "diffusers:OnnxStableDiffusionPipeline", "region:us" ]

text-to-image

2024-08-30T12:52:44Z

--- license: creativeml-openrail-m pipeline_tag: text-to-image library_name: diffusers --- This is a reupload of the ONNX version of the stable-diffusion-v1-5 model previously hosted in the runwayml/stable-diffusion-v1-5 repository that is now offline. I'm mostly reuploading this for my own experiments on running diffusion models with Unity Sentis, but the model should still work with any onnx-compatible library of course. See [com.doji.diffusers](https://github.com/julienkay/com.doji.diffusers) for details. Here is the original model card: --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this with the [🧨Diffusers library](https://github.com/huggingface/diffusers). ### Diffusers usage ```py from diffusers import StableDiffusionPipeline import torch pipe = StableDiffusionPipeline.from_pretrained( "benjamin-paine/stable-diffusion-v1-5", torch_dtype=torch.float16 ) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/benjamin-paine/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/benjamin-paine/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit

Agnuxo

2024-08-30T15:34:12Z

346

1

adapter-transformers

[ "adapter-transformers", "gguf", "llama", "text-generation-inference", "transformers", "unsloth", "mistral", "en", "es", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:codefuse-ai/CodeExercise-Python-27k", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:mlabonne/Evol-Instruct-Python-26k", "base_model:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit", "base_model:adapter:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-24T13:50:42Z

--- base_model: Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit language: - en - es license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca - codefuse-ai/CodeExercise-Python-27k - Vezora/Tested-22k-Python-Alpaca - mlabonne/Evol-Instruct-Python-26k metrics: - code_eval library_name: adapter-transformers --- # Uploaded model [<img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" width="100"/><img src="https://github.githubassets.com/assets/GitHub-Logo-ee398b662d42.png" width="100"/>](https://github.com/Agnuxo1) - **Developed by:** Agnuxo(https://github.com/Agnuxo1) - **License:** apache-2.0 - **Finetuned from model :** Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Agnuxo/Mistral-NeMo-Minitron-8B-Alpaca-CODE-Python-GGUF-16bit

Agnuxo

2024-08-30T15:32:15Z

29

0

adapter-transformers

[ "adapter-transformers", "gguf", "mistral", "text-generation-inference", "transformers", "unsloth", "en", "es", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:codefuse-ai/CodeExercise-Python-27k", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:mlabonne/Evol-Instruct-Python-26k", "base_model:Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal", "base_model:adapter:Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-08-23T12:02:41Z

--- base_model: Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal language: - en - es license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca - codefuse-ai/CodeExercise-Python-27k - Vezora/Tested-22k-Python-Alpaca - mlabonne/Evol-Instruct-Python-26k metrics: - code_eval library_name: adapter-transformers --- # Uploaded model [<img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" width="100"/><img src="https://github.githubassets.com/assets/GitHub-Logo-ee398b662d42.png" width="100"/>](https://github.com/Agnuxo1) - **Developed by:** Agnuxo(https://github.com/Agnuxo1) - **License:** apache-2.0 - **Finetuned from model :** Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

abhinayadutta/deepseek-coder-1.3b-base-int8-plsql-codegen

abhinayadutta

2024-08-30T15:31:57Z

75

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

2024-07-19T10:12:22Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

alpacaml/stable-diffusion-v1-5

alpacaml

2024-08-30T15:26:28Z

121

0

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-08-30T15:26:28Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true extra_gated_prompt: >- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model duplicated_from: runwayml/stable-diffusion-v1-5 --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

Neuranest/Phi-3.5-mini-instruct-hfc-gguf

Neuranest

2024-08-30T15:21:19Z

6

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T15:15:49Z

--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** Neuranest - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_4bit

Agnuxo

2024-08-30T15:14:37Z

18

0

adapter-transformers

[ "adapter-transformers", "gguf", "llama", "text-generation-inference", "transformers", "unsloth", "en", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:codefuse-ai/CodeExercise-Python-27k", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:mlabonne/Evol-Instruct-Python-26k", "base_model:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant_16bit", "base_model:adapter:Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant_16bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-24T13:57:37Z

--- base_model: Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant_16bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca - codefuse-ai/CodeExercise-Python-27k - Vezora/Tested-22k-Python-Alpaca - mlabonne/Evol-Instruct-Python-26k metrics: - code_eval library_name: adapter-transformers --- # Uploaded model - **Developed by:** Agnuxo - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit

Agnuxo

2024-08-30T15:11:44Z

234

3

adapter-transformers

[ "adapter-transformers", "gguf", "mistral", "text-generation-inference", "transformers", "unsloth", "en", "es", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:jtatman/python-code-dataset-500k", "dataset:flytech/python-codes-25k", "dataset:Vezora/Tested-143k-Python-Alpaca", "base_model:Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit", "base_model:adapter:Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-08-24T16:38:03Z

--- base_model: Agnuxo/Mamba-Codestral-7B-v0.1-instruct-python_coding_assistant-GGUF_4bit language: - en - es license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf datasets: - iamtarun/python_code_instructions_18k_alpaca - jtatman/python-code-dataset-500k - flytech/python-codes-25k - Vezora/Tested-143k-Python-Alpaca metrics: - code_eval library_name: adapter-transformers --- # Uploaded model - **Developed by:** Agnuxo - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Neuranest/Phi-3.5-mini-instruct-hfc-16bit

Neuranest

2024-08-30T15:10:40Z

118

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-08-30T15:08:33Z

--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - **Developed by:** Neuranest - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX

v000000

2024-08-30T14:57:56Z

125

4

null

[ "gguf", "llama", "merge", "llama-cpp", "base_model:v000000/L3.1-Niitorm-8B-LATCOSx2", "base_model:quantized:v000000/L3.1-Niitorm-8B-LATCOSx2", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-29T21:10:49Z

--- base_model: v000000/L3.1-Niitorm-8B-LATCOSx2 tags: - llama - merge - llama-cpp --- # Llama-3.1-Niitorm-8B-LATCOSx2 ![d48ca23f-9063-4a66-a6b8-0abcbfe26dc5.jpg](https://cdn-uploads.huggingface.co/production/uploads/64f74b6e6389380c77562762/96he_28zzbVZBoh29QlIm.jpeg) # Ordered by quality: * q8_0 imatrix * q8_0 * q6_k imatrix * q6_k * q5_k_m imatrix * q5_k_s imatrix * q4_k_m imatrix * q4_k_s imatrix * iq4_xs imatrix * q4_0_4_8 imatrix arm * q4_0_4_4 imatrix arm This is a test *RP* model, "v000000/L3.1-Niitorm-8B-t0.0001" but merged one extra time with "akjindal53244/Llama-3.1-Storm-8B". Using a new merging algorithm I wrote "LATCOS", which is non linear interpolation and cosine vector similarity between tensors in both magnitude and direction. This attempts to find the smoothest possible interpolation and make them work more seamlessly together by taking into account the vector direction where both models agree. The model seems a lot smarter even though it's just a bit more of storm, but also more compliant which could be a negative since it's less "dynamic". imatrix data randomized bartowski, kalomeze, rp snippets, working gpt4 code, human messaging, story

lmstudio-community/c4ai-command-r-08-2024-GGUF

lmstudio-community

2024-08-30T14:56:25Z

528

22

transformers

[ "transformers", "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "base_model:CohereForAI/c4ai-command-r-08-2024", "base_model:quantized:CohereForAI/c4ai-command-r-08-2024", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-08-30T14:34:31Z

--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy)." extra_gated_fields: Name: text Affiliation: text Country: type: select options: - Aruba - Afghanistan - Angola - Anguilla - Åland Islands - Albania - Andorra - United Arab Emirates - Argentina - Armenia - American Samoa - Antarctica - French Southern Territories - Antigua and Barbuda - Australia - Austria - Azerbaijan - Burundi - Belgium - Benin - Bonaire Sint Eustatius and Saba - Burkina Faso - Bangladesh - Bulgaria - Bahrain - Bahamas - Bosnia and Herzegovina - Saint Barthélemy - Belarus - Belize - Bermuda - Plurinational State of Bolivia - Brazil - Barbados - Brunei-Darussalam - Bhutan - Bouvet-Island - Botswana - Central African Republic - Canada - Cocos (Keeling) Islands - Switzerland - Chile - China - Côte-dIvoire - Cameroon - Democratic Republic of the Congo - Cook Islands - Colombia - Comoros - Cabo Verde - Costa Rica - Cuba - Curaçao - Christmas Island - Cayman Islands - Cyprus - Czechia - Germany - Djibouti - Dominica - Denmark - Dominican Republic - Algeria - Ecuador - Egypt - Eritrea - Western Sahara - Spain - Estonia - Ethiopia - Finland - Fiji - Falkland Islands (Malvinas) - France - Faroe Islands - Federated States of Micronesia - Gabon - United Kingdom - Georgia - Guernsey - Ghana - Gibraltar - Guinea - Guadeloupe - Gambia - Guinea Bissau - Equatorial Guinea - Greece - Grenada - Greenland - Guatemala - French Guiana - Guam - Guyana - Hong Kong - Heard Island and McDonald Islands - Honduras - Croatia - Haiti - Hungary - Indonesia - Isle of Man - India - British Indian Ocean Territory - Ireland - Islamic Republic of Iran - Iraq - Iceland - Israel - Italy - Jamaica - Jersey - Jordan - Japan - Kazakhstan - Kenya - Kyrgyzstan - Cambodia - Kiribati - Saint-Kitts-and-Nevis - South Korea - Kuwait - Lao-Peoples-Democratic-Republic - Lebanon - Liberia - Libya - Saint-Lucia - Liechtenstein - Sri Lanka - Lesotho - Lithuania - Luxembourg - Latvia - Macao - Saint Martin (French-part) - Morocco - Monaco - Republic of Moldova - Madagascar - Maldives - Mexico - Marshall Islands - North Macedonia - Mali - Malta - Myanmar - Montenegro - Mongolia - Northern Mariana Islands - Mozambique - Mauritania - Montserrat - Martinique - Mauritius - Malawi - Malaysia - Mayotte - Namibia - New Caledonia - Niger - Norfolk Island - Nigeria - Nicaragua - Niue - Netherlands - Norway - Nepal - Nauru - New Zealand - Oman - Pakistan - Panama - Pitcairn - Peru - Philippines - Palau - Papua New Guinea - Poland - Puerto Rico - North Korea - Portugal - Paraguay - State of Palestine - French Polynesia - Qatar - Réunion - Romania - Russia - Rwanda - Saudi Arabia - Sudan - Senegal - Singapore - South Georgia and the South Sandwich Islands - Saint Helena Ascension and Tristan da Cunha - Svalbard and Jan Mayen - Solomon Islands - Sierra Leone - El Salvador - San Marino - Somalia - Saint Pierre and Miquelon - Serbia - South Sudan - Sao Tome and Principe - Suriname - Slovakia - Slovenia - Sweden - Eswatini - Sint Maarten (Dutch-part) - Seychelles - Syrian Arab Republic - Turks and Caicos Islands - Chad - Togo - Thailand - Tajikistan - Tokelau - Turkmenistan - Timor Leste - Tonga - Trinidad and Tobago - Tunisia - Turkey - Tuvalu - Taiwan - United Republic of Tanzania - Uganda - Ukraine - United States Minor Outlying Islands - Uruguay - United-States - Uzbekistan - Holy See (Vatican City State) - Saint Vincent and the Grenadines - Bolivarian Republic of Venezuela - Virgin Islands British - Virgin Islands U.S. - VietNam - Vanuatu - Wallis and Futuna - Samoa - Yemen - South Africa - Zambia - Zimbabwe Receive email updates on C4AI and Cohere research, events, products and services?: type: select options: - Yes - No I agree to use this model for non-commercial use ONLY: checkbox quantized_by: bartowski pipeline_tag: text-generation base_model: CohereForAI/c4ai-command-r-08-2024 lm_studio: param_count: 35b use_case: general release_date: 30-08-2024 model_creator: CohereForAI prompt_template: cohere_command_r base_model: Cohere system_prompt: You are a large language model called Command R built by the company Cohere. You act as a brilliant, sophisticated, AI-assistant chatbot trained to assist human users by providing thorough responses. original_repo: CohereForAI/c4ai-command-r-08-2024 --- ## 💫 Community Model> C4AI Command R 08-2024 by Cohere For AI *👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*. **Model creator:** [CohereForAI](https://huggingface.co/CohereForAI) **Original model**: [c4ai-command-r-08-2024](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024) **GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3634](https://github.com/ggerganov/llama.cpp/releases/tag/b3634) ## Model Summary: C4AI Command R 08-2024 is an update to the originally released 35B paramater Command R. The original Command R model received sweeping praise for its incredible RAG and multilingual abilities, and this model is no different. Not for commercial use, must adhere to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ## Prompt Template: Choose the `Cohere Command R` preset in your LM Studio. Under the hood, the model will see a prompt that's formatted like so: ``` <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ``` This model also supports tool use and RAG prompt formats. For details on formatting for those use cases, view [tool use here](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024#tool-use--agent-capabilities) and [RAG capabilities here](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024#grounded-generation-and-rag-capabilities) ## Technical Details C4AI Command R 08-2024 has been trained on 23 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian). Due to this multilingual training, it excels in multilingual tasks. Command R 08-2024 supports a context length of 128K. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio. ### Terms of Use (directly from Cohere For AI) We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).

U4R/StructTable-base

U4R

2024-08-30T14:53:12Z

42,463

7

transformers

[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "image-to-text", "en", "zh", "arxiv:2406.11633", "license:apache-2.0", "region:us" ]

image-to-text

2024-07-26T13:34:55Z

--- language: - en - zh pipeline_tag: image-to-text inference: false license: apache-2.0 --- <div align="center"> <h1>StructEqTable-Deploy: A High-efficiency Open-source Toolkit for Table-to-Latex Transformation</h1> [[ Github Repo ]](https://github.com/UniModal4Reasoning/StructEqTable-Deploy) [[ Related Paper ]](https://arxiv.org/abs/2406.11633) [[ Website ]](https://unimodal4reasoning.github.io/DocGenome_page/) [[ Dataset (Google Drive)]](https://drive.google.com/drive/folders/1OIhnuQdIjuSSDc_QL2nP4NwugVDgtItD) [[ Dataset (Hugging Face) ]](https://huggingface.co/datasets/U4R/DocGenome/tree/main) [[Models 🤗(Hugging Face)]](https://huggingface.co/U4R/StructTable-base/tree/main) </div> Welcome to the official repository of StructEqTable-Deploy, a solution that converts images of Table into LaTeX, powered by scalable data from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/). ## Overview Table is an effective way to represent structured data in scientific publications, financial statements, invoices, web pages, and many other scenarios. Extracting tabular data from a visual table image and performing the downstream reasoning tasks according to the extracted data is challenging, mainly due to that tables often present complicated column and row headers with spanning cell operation. To address these challenges, we present TableX, a large-scale multi-modal table benchmark extracted from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/) for table pre-training, comprising more than 2 million high-quality Image-LaTeX pair data covering 156 disciplinary classes. Besides, benefiting from such large-scale data, we train an end-to-end model, StructEqTable, which provides the capability to precisely obtain the corresponding LaTeX description from a visual table image and perform multiple table-related reasoning tasks, including structural extraction and question answering, broadening its application scope and potential. ## Changelog Tips: Current version of StructEqTable is able to process table images from scientific documents such as arXiv, Scihub papers. Times New Roman And Songti(宋体) are main fonts used in table image, other fonts may decrease the accuracy of the model's output. - **[2024/8/22] 🔥 We have released our [latest model](https://huggingface.co/U4R/StructTable-base/tree/v0.2), fine-tuned on the DocGenome dataset. This version features improved inference speed and robustness, achieved through data augmentation and reduced image token num.** - [2024/8/08] We have released the TensorRT accelerated version, which only takes about 1 second for most images on GPU A100. Please follow the tutorial to install the environment and compile the model weights. - [2024/7/30] We have released the first version of StructEqTable. ## TODO - [x] Release inference code and checkpoints of StructEqTable. - [x] Support Chinese version of StructEqTable. - [x] Accelerated version of StructEqTable using TensorRT-LLM. - [ ] Expand more domains of table image to improve the model's general capabilities. - [ ] Release our table pre-training and fine-tuning code ## Efficient Inference Our model now supports TensorRT-LLM deployment, achieving a 10x or more speedup in during inference. Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) to learn how to depoly. ## Installation ``` bash conda create -n structeqtable python>=3.10 conda activate structeqtable # Install from Source code (Suggested) git clone https://github.com/UniModal4Reasoning/StructEqTable-Deploy.git cd StructEqTable-Deploy python setup develop # or Install from Github repo pip install "git+https://github.com/UniModal4Reasoning/StructEqTable-Deploy.git" # or Install from PyPI pip install struct-eqtable==0.1.0 ``` ## Model Zoo | Model | Image Token Num | Model Size | Training Data | Data Augmentation | TensorRT | HuggingFace | |---------------------|---------------------|------------|------------------|-------------------|----------|-------------------| | StructEqTable-base | 4096 | ~300M | DocGenome | | ☑️ | [v0.1](https://huggingface.co/U4R/StructTable-base/tree/v0.1) | | StructEqTable-base | 2048 | ~300M | DocGenome | ☑️ | ☑️ | [v0.2](https://huggingface.co/U4R/StructTable-base/tree/v0.2) | ## Quick Demo - Run the demo/demo.py ```shell script cd tools/demo python demo.py \ --image_path ./demo.png \ --ckpt_path ${CKPT_PATH} \ --output_format latex ``` - HTML or Markdown format output Our model output Latex format code by default. If you want to get other format like HTML or Markdown, `pypandoc` support convert latex format code into HTML and Markdown format for simple table (table has no merge cell ). ```shell script sudo apt install pandoc pip install pypandoc cd tools/demo python demo.py \ --image_path ./demo.png \ --ckpt_path ${CKPT_PATH} \ --output_format html markdown ``` - Visualization Results - The input data are sampled from SciHub domain. ![](docs/demo_1.png) ![](docs/demo_2.png) ## Acknowledgements - [DocGenome](https://github.com/UniModal4Reasoning/DocGenome). An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Models. - [ChartVLM](https://github.com/UniModal4Reasoning/ChartVLM). A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning. - [Pix2Struct](https://github.com/google-research/pix2struct). Screenshot Parsing as Pretraining for Visual Language Understanding. - [UniMERNet](https://github.com/opendatalab/UniMERNet). A Universal Network for Real-World Mathematical Expression Recognition. - [Donut](https://huggingface.co/naver-clova-ix/donut-base). The UniMERNet's Transformer Encoder-Decoder are referenced from Donut. - [Nougat](https://github.com/facebookresearch/nougat). The tokenizer uses Nougat. - [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM). Model inference acceleration uses TensorRT-LLM. ## License StructEqTable is released under the [Apache License 2.0](LICENSE) ## Citation If you find our models / code / papers useful in your research, please consider giving ⭐ and citations 📝, thx :) ```bibtex @article{xia2024docgenome, title={DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models}, author={Xia, Renqiu and Mao, Song and Yan, Xiangchao and Zhou, Hongbin and Zhang, Bo and Peng, Haoyang and Pi, Jiahao and Fu, Daocheng and Wu, Wenjie and Ye, Hancheng and others}, journal={arXiv preprint arXiv:2406.11633}, year={2024} } ``` ## Contact Us If you encounter any issues or have questions, please feel free to contact us via zhouhongbin@pjlab.org.cn.

lamm-mit/mistralai-Mistral-7B-v0.3

lamm-mit

2024-08-30T14:51:53Z

5

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-08-12T22:41:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BeaverAI/Theia-21B-v2d-GGUF

BeaverAI

2024-08-30T14:46:44Z

11

0

null

[ "gguf", "endpoints_compatible", "region:us" ]

null

2024-08-30T11:45:26Z

chatml seems broken. text completion looks fine

AshtonLKY/Whisper_ASR_ATC_v6

AshtonLKY

2024-08-30T14:45:46Z

5

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "asr-fyp", "generated_from_trainer", "en", "dataset:AshtonLKY/Whisper_ASR_ATC", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-08-23T10:53:01Z

--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-small tags: - asr-fyp - generated_from_trainer datasets: - AshtonLKY/Whisper_ASR_ATC model-index: - name: Whisper_ASR_ATC results: [] ---  # Whisper_ASR_ATC This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AshtonLKY/augmented_audio dataset. It achieves the following results on the evaluation set: - eval_loss: 0.091453 - eval_wer: 6.122661 - step: 11000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 25000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

CharlesLi/OpenELM-1_1B-DPO-full-2-5

CharlesLi

2024-08-30T14:39:22Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "openelm", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "custom_code", "autotrain_compatible", "region:us" ]

text-generation

2024-08-30T08:25:51Z

--- library_name: transformers tags: - trl - dpo - generated_from_trainer model-index: - name: OpenELM-1_1B-DPO-full-2-5 results: [] ---  # OpenELM-1_1B-DPO-full-2-5 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1888 - Rewards/chosen: -13.5625 - Rewards/rejected: -17.0 - Rewards/accuracies: 0.7070 - Rewards/margins: 3.4062 - Logps/rejected: -1984.0 - Logps/chosen: -1672.0 - Logits/rejected: 6.2188 - Logits/chosen: 4.5312 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.615 | 0.1047 | 100 | 0.6275 | -0.7383 | -0.9961 | 0.6719 | 0.2578 | -386.0 | -388.0 | -9.625 | -9.8125 | | 0.5897 | 0.2093 | 200 | 0.6029 | -1.6641 | -2.0938 | 0.6934 | 0.4336 | -496.0 | -480.0 | -9.375 | -9.75 | | 0.6457 | 0.3140 | 300 | 0.5886 | -1.3828 | -1.8281 | 0.6895 | 0.4473 | -470.0 | -454.0 | -13.5625 | -13.75 | | 0.6271 | 0.4186 | 400 | 0.5936 | -1.7031 | -2.25 | 0.6992 | 0.5430 | -510.0 | -484.0 | -8.4375 | -8.8125 | | 0.5746 | 0.5233 | 500 | 0.5886 | -2.0156 | -2.5625 | 0.6816 | 0.5430 | -540.0 | -516.0 | -6.6562 | -7.4062 | | 0.5484 | 0.6279 | 600 | 0.5710 | -3.9531 | -4.6875 | 0.6973 | 0.7422 | -756.0 | -708.0 | -5.75 | -6.4375 | | 0.5747 | 0.7326 | 700 | 0.5820 | -2.75 | -3.4844 | 0.6953 | 0.7227 | -632.0 | -592.0 | -6.5312 | -7.5938 | | 0.5591 | 0.8373 | 800 | 0.5662 | -2.8594 | -3.5156 | 0.7090 | 0.6523 | -636.0 | -600.0 | -3.375 | -4.7812 | | 0.5892 | 0.9419 | 900 | 0.5821 | -2.625 | -3.2344 | 0.7012 | 0.5977 | -608.0 | -576.0 | -4.8438 | -6.125 | | 0.261 | 1.0466 | 1000 | 0.5852 | -3.9375 | -4.9688 | 0.7324 | 1.0078 | -780.0 | -708.0 | -0.8672 | -2.1094 | | 0.2407 | 1.1512 | 1100 | 0.5943 | -4.0625 | -5.0 | 0.6895 | 0.9336 | -784.0 | -720.0 | -0.3672 | -1.9688 | | 0.2348 | 1.2559 | 1200 | 0.6151 | -4.9375 | -5.9688 | 0.6777 | 1.0547 | -884.0 | -808.0 | 1.5312 | 0.2227 | | 0.257 | 1.3605 | 1300 | 0.6005 | -4.4688 | -5.4688 | 0.6973 | 0.9883 | -832.0 | -760.0 | 1.5312 | -0.1445 | | 0.2416 | 1.4652 | 1400 | 0.6023 | -5.1875 | -6.125 | 0.6855 | 0.9258 | -900.0 | -836.0 | 1.9141 | 0.2715 | | 0.215 | 1.5699 | 1500 | 0.6062 | -5.5938 | -6.7188 | 0.6934 | 1.1328 | -960.0 | -872.0 | 1.9219 | 0.2637 | | 0.2534 | 1.6745 | 1600 | 0.6013 | -4.6562 | -5.7188 | 0.7129 | 1.0391 | -856.0 | -780.0 | 2.7969 | 1.1406 | | 0.2463 | 1.7792 | 1700 | 0.6173 | -5.2812 | -6.4375 | 0.6914 | 1.1484 | -928.0 | -844.0 | 1.9688 | 0.0977 | | 0.23 | 1.8838 | 1800 | 0.6153 | -5.8438 | -7.0625 | 0.7090 | 1.2266 | -992.0 | -896.0 | 2.9062 | 1.0156 | | 0.2092 | 1.9885 | 1900 | 0.6082 | -5.5625 | -6.7188 | 0.7051 | 1.1641 | -956.0 | -868.0 | 2.9375 | 1.0781 | | 0.0271 | 2.0931 | 2000 | 0.7202 | -7.625 | -9.375 | 0.7207 | 1.7734 | -1224.0 | -1080.0 | 3.5781 | 1.8516 | | 0.0367 | 2.1978 | 2100 | 0.8323 | -9.3125 | -11.5 | 0.7168 | 2.1406 | -1432.0 | -1248.0 | 4.7188 | 2.9219 | | 0.0443 | 2.3025 | 2200 | 0.7840 | -8.0 | -10.0625 | 0.7324 | 2.0625 | -1296.0 | -1112.0 | 3.9375 | 2.0312 | | 0.0302 | 2.4071 | 2300 | 0.7981 | -8.375 | -10.375 | 0.7070 | 2.0 | -1328.0 | -1152.0 | 4.625 | 2.8125 | | 0.031 | 2.5118 | 2400 | 0.7786 | -7.9062 | -9.875 | 0.7129 | 1.9922 | -1280.0 | -1104.0 | 4.875 | 3.0156 | | 0.018 | 2.6164 | 2500 | 0.8584 | -9.9375 | -12.125 | 0.6914 | 2.2031 | -1496.0 | -1312.0 | 5.4688 | 3.6719 | | 0.0248 | 2.7211 | 2600 | 0.8079 | -8.625 | -10.6875 | 0.7012 | 2.0469 | -1352.0 | -1176.0 | 5.0312 | 3.0938 | | 0.0263 | 2.8257 | 2700 | 0.8371 | -9.3125 | -11.375 | 0.6914 | 2.0156 | -1424.0 | -1248.0 | 5.2812 | 3.4531 | | 0.033 | 2.9304 | 2800 | 0.8799 | -9.8125 | -12.1875 | 0.7207 | 2.4062 | -1504.0 | -1296.0 | 5.2188 | 3.3281 | | 0.0118 | 3.0351 | 2900 | 0.8372 | -9.625 | -11.875 | 0.7246 | 2.2969 | -1472.0 | -1280.0 | 5.6562 | 3.7812 | | 0.0094 | 3.1397 | 3000 | 0.9555 | -11.0 | -13.6875 | 0.7090 | 2.6875 | -1656.0 | -1416.0 | 6.0938 | 4.3125 | | 0.0073 | 3.2444 | 3100 | 0.9687 | -11.375 | -14.125 | 0.7129 | 2.7344 | -1696.0 | -1456.0 | 5.9062 | 4.1875 | | 0.0104 | 3.3490 | 3200 | 1.0111 | -11.75 | -14.5625 | 0.7070 | 2.8438 | -1744.0 | -1488.0 | 6.1875 | 4.4688 | | 0.01 | 3.4537 | 3300 | 1.0564 | -12.125 | -15.0625 | 0.7051 | 2.9375 | -1792.0 | -1528.0 | 5.9375 | 4.2188 | | 0.0089 | 3.5583 | 3400 | 0.9822 | -11.375 | -14.0625 | 0.7051 | 2.7031 | -1696.0 | -1448.0 | 5.875 | 4.2188 | | 0.0106 | 3.6630 | 3500 | 1.0239 | -11.5625 | -14.375 | 0.7070 | 2.8125 | -1720.0 | -1472.0 | 5.9688 | 4.25 | | 0.0099 | 3.7677 | 3600 | 1.0668 | -11.9375 | -14.9375 | 0.6973 | 3.0 | -1784.0 | -1512.0 | 6.125 | 4.375 | | 0.0066 | 3.8723 | 3700 | 1.0938 | -12.75 | -15.875 | 0.7070 | 3.1406 | -1872.0 | -1592.0 | 6.2188 | 4.5312 | | 0.0081 | 3.9770 | 3800 | 1.0255 | -11.6875 | -14.5625 | 0.7129 | 2.8906 | -1744.0 | -1488.0 | 5.9688 | 4.2812 | | 0.0035 | 4.0816 | 3900 | 1.1112 | -12.75 | -15.875 | 0.7031 | 3.1406 | -1872.0 | -1592.0 | 6.2188 | 4.5312 | | 0.002 | 4.1863 | 4000 | 1.1127 | -12.8125 | -16.0 | 0.7051 | 3.1562 | -1888.0 | -1600.0 | 6.1875 | 4.5 | | 0.0036 | 4.2909 | 4100 | 1.1368 | -13.0 | -16.25 | 0.7031 | 3.25 | -1912.0 | -1616.0 | 6.1875 | 4.4688 | | 0.0069 | 4.3956 | 4200 | 1.1589 | -13.25 | -16.625 | 0.7070 | 3.3125 | -1944.0 | -1640.0 | 6.2188 | 4.5312 | | 0.0043 | 4.5003 | 4300 | 1.1756 | -13.4375 | -16.75 | 0.7031 | 3.375 | -1968.0 | -1656.0 | 6.2188 | 4.5312 | | 0.0091 | 4.6049 | 4400 | 1.1842 | -13.5 | -16.875 | 0.7031 | 3.3906 | -1976.0 | -1664.0 | 6.2188 | 4.5312 | | 0.0058 | 4.7096 | 4500 | 1.1865 | -13.5 | -16.875 | 0.7051 | 3.3906 | -1976.0 | -1664.0 | 6.2188 | 4.5312 | | 0.0034 | 4.8142 | 4600 | 1.1880 | -13.5625 | -17.0 | 0.7051 | 3.3906 | -1984.0 | -1672.0 | 6.2188 | 4.5312 | | 0.006 | 4.9189 | 4700 | 1.1888 | -13.5625 | -17.0 | 0.7070 | 3.4062 | -1984.0 | -1672.0 | 6.2188 | 4.5312 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1

John6666/pony-kanoato-symphony-final-sdxl

John6666

2024-08-30T14:34:56Z

116

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "style", "semi-realistic", "illustration", "paintings", "pony", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-08-30T14:30:14Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - style - semi-realistic - illustration - paintings - pony --- Original model is [here](https://civitai.com/models/645642?modelVersionId=722292). This model created by [Kanoato](https://civitai.com/user/Kanoato).

QuantFactory/Bielik-11B-v2.2-Instruct-GGUF

QuantFactory

2024-08-30T14:26:16Z

18

1

transformers

[ "transformers", "gguf", "finetuned", "pl", "arxiv:2005.01643", "arxiv:2309.11235", "arxiv:2006.09092", "arxiv:2402.13228", "base_model:speakleash/Bielik-11B-v2", "base_model:quantized:speakleash/Bielik-11B-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T13:19:21Z

--- license: apache-2.0 base_model: speakleash/Bielik-11B-v2 language: - pl library_name: transformers tags: - finetuned inference: parameters: temperature: 0.2 widget: - messages: - role: user content: Co przedstawia polskie godło? extra_gated_description: If you want to learn more about how you can use the model, please refer to our <a href="https://bielik.ai/terms/">Terms of Use</a>. --- ![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ) # QuantFactory/Bielik-11B-v2.2-Instruct-GGUF This is quantized version of [speakleash/Bielik-11B-v2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct) created using llama.cpp # Original Model Card <img src="https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct/raw/main/speakleash_cyfronet.png"> # Bielik-11B-v2.2-Instruct Bielik-11B-v2.2-Instruct is a generative text model featuring 11 billion parameters. It is an instruct fine-tuned version of the [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2). Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH. Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH. The creation and training of the Bielik-11B-v2.2-Instruct was propelled by the support of computational grant number PLG/2024/016951, conducted on the Athena and Helios supercomputer, enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision. 🎥 Demo: https://chat.bielik.ai 🗣️ Chat Arena*: https://arena.speakleash.org.pl/ *Chat Arena is a platform for testing and comparing different AI language models, allowing users to evaluate their performance and quality. ## Model The [SpeakLeash](https://speakleash.org/) team is working on their own set of instructions in Polish, which is continuously being expanded and refined by annotators. A portion of these instructions, which had been manually verified and corrected, has been utilized for training purposes. Moreover, due to the limited availability of high-quality instructions in Polish, synthetic instructions were generated with [Mixtral 8x22B](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) and used in training. The dataset used for training comprised over 20 million instructions, consisting of more than 10 billion tokens. The instructions varied in quality, leading to a deterioration in the model’s performance. To counteract this while still allowing ourselves to utilize the aforementioned datasets, several improvements were introduced: * Weighted tokens level loss - a strategy inspired by [offline reinforcement learning](https://arxiv.org/abs/2005.01643) and [C-RLFT](https://arxiv.org/abs/2309.11235) * Adaptive learning rate inspired by the study on [Learning Rates as a Function of Batch Size](https://arxiv.org/abs/2006.09092) * Masked prompt tokens To align the model with user preferences we tested many different techniques: DPO, PPO, KTO, SiMPO. Finally the [DPO-Positive](https://arxiv.org/abs/2402.13228) method was employed, utilizing both generated and manually corrected examples, which were scored by a metamodel. A dataset comprising over 66,000 examples of varying lengths to address different aspects of response style. It was filtered and evaluated by the reward model to select instructions with the right level of difference between chosen and rejected. The novelty introduced in DPO-P was multi-turn conversations introduction. Bielik-11B-v2.2-Instruct has been trained with the use of an original open source framework called [ALLaMo](https://github.com/chrisociepa/allamo) implemented by [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/). This framework allows users to train language models with architecture similar to LLaMA and Mistral in fast and efficient way. ### Model description: * **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/) * **Language:** Polish * **Model type:** causal decoder-only * **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2) * **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/) * **Model ref:** speakleash:0deb975c3780df3a3ae98b619185faa1 ### Quantized models: We know that some people want to explore smaller models or don't have the resources to run a full model. Therefore, we have prepared quantized versions of the Bielik-11B-v2.2-Instruct model in separate repositories: - [GGUF - Q4_K_M, Q5_K_M, Q6_K, Q8_0](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-GGUF) - [GPTQ - 4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-GPTQ) - HQQ - [4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-HQQ-4bit-128gs), [8bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-HQQ-8bit-128gs) - [AWQ - 4bit GEMM](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-AWQ) - EXL2 - [4.5bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-EXL2-4.5bit), [6.5bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-EXL2-6.5bit) - MLX - [4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-MLX-4bit), [8bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-MLX-8bit) - Quanto - [4bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-Quanto-4bit), [8bit](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-Quanto-8bit) - [FP8](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-FP8) (vLLM, SGLang - Ada Lovelace, Hopper optimized) - [INT8 W8A8](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-W8A8) (vLLM INT8 quantization Weights=8bits and Activations=8bits) - [GGUF - experimental - IQ imatrix IQ2_XXS, IQ3_XXS, IQ4_XS and calibrated Q4_K_M, Q5_K_M, Q6_K, Q8_0](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct-GGUF-IQ-Imatrix) Please note that quantized models may offer lower quality of generated answers compared to full sized variatns. ### Chat template Bielik-11B-v2.2-Instruct uses [ChatML](https://github.com/cognitivecomputations/OpenChatML) as the prompt format. E.g. ``` prompt = "<s><|im_start|> user\nJakie mamy pory roku?<|im_end|> \n<|im_start|> assistant\n" completion = "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.<|im_end|> \n" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model_name = "speakleash/Bielik-11B-v2.2-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16) messages = [ {"role": "system", "content": "Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim."}, {"role": "user", "content": "Jakie mamy pory roku w Polsce?"}, {"role": "assistant", "content": "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima."}, {"role": "user", "content": "Która jest najcieplejsza?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = input_ids.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` Fully formated input conversation by apply_chat_template from previous example: ``` <s><|im_start|> system Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim.<|im_end|> <|im_start|> user Jakie mamy pory roku w Polsce?<|im_end|> <|im_start|> assistant W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.<|im_end|> <|im_start|> user Która jest najcieplejsza?<|im_end|> ``` ## Evaluation Bielik-11B-v2.2-Instruct has been evaluated on several benchmarks to assess its performance across various tasks and languages. These benchmarks include: 1. Open PL LLM Leaderboard 2. Open LLM Leaderboard 3. Polish MT-Bench 4. Polish EQ-Bench (Emotional Intelligence Benchmark) 5. MixEval The following sections provide detailed results for each of these benchmarks, demonstrating the model's capabilities in both Polish and English language tasks. ### Open PL LLM Leaderboard Models have been evaluated on [Open PL LLM Leaderboard](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) 5-shot. The benchmark evaluates models in NLP tasks like sentiment analysis, categorization, text classification but does not test chatting skills. Average column is an average score among all tasks normalized by baseline scores. | Model | Parameters (B)| Average | |---------------------------------|------------|---------| | Meta-Llama-3.1-405B-Instruct-FP8,API | 405 | 69.44 | | Mistral-Large-Instruct-2407 | 123 | 69.11 | | Qwen2-72B-Instruct | 72 | 65.87 | | **Bielik-11B-v2.2-Instruct** | **11** | **65.57** | | Meta-Llama-3.1-70B-Instruct | 70 | 65.49 | | Bielik-11B-v2.1-Instruct | 11 | 65.45 | | Mixtral-8x22B-Instruct-v0.1 | 141 | 65.23 | | Bielik-11B-v2.0-Instruct | 11 | 64.98 | | Meta-Llama-3-70B-Instruct | 70 | 64.45 | | Athene-70B | 70 | 63.65 | | WizardLM-2-8x22B | 141 | 62.35 | | Qwen1.5-72B-Chat | 72 | 58.67 | | Qwen2-57B-A14B-Instruct | 57 | 56.89 | | glm-4-9b-chat | 9 | 56.61 | | aya-23-35B | 35 | 56.37 | | Phi-3.5-MoE-instruct | 41.9 | 56.34 | | openchat-3.5-0106-gemma | 7 | 55.69 | | Mistral-Nemo-Instruct-2407 | 12 | 55.27 | | SOLAR-10.7B-Instruct-v1.0 | 10.7 | 55.24 | | Mixtral-8x7B-Instruct-v0.1 | 46.7 | 55.07 | | Bielik-7B-Instruct-v0.1 | 7 | 44.70 | | trurl-2-13b-academic | 13 | 36.28 | | trurl-2-7b | 7 | 26.93 | The results from the Open PL LLM Leaderboard demonstrate the exceptional performance of Bielik-11B-v2.2-Instruct: 1. Superior performance in its class: Bielik-11B-v2.2-Instruct outperforms all other models with less than 70B parameters. This is a significant achievement, showcasing its efficiency and effectiveness despite having fewer parameters than many competitors. 2. Competitive with larger models: with a score of 65.57, Bielik-11B-v2.2-Instruct performs on par with models in the 70B parameter range. This indicates that it achieves comparable results to much larger models, demonstrating its advanced architecture and training methodology. 3. Substantial improvement over previous version: the model shows a marked improvement over its predecessor, Bielik-7B-Instruct-v0.1, which scored 43.64. This leap in performance highlights the successful enhancements and optimizations implemented in this newer version. 4. Leading position for Polish language models: in the context of Polish language models, Bielik-11B-v2.2-Instruct stands out as a leader. There are no other competitive models specifically tailored for the Polish language that match its performance, making it a crucial resource for Polish NLP tasks. These results underscore Bielik-11B-v2.2-Instruct's position as a state-of-the-art model for Polish language processing, offering high performance with relatively modest computational requirements. #### Open PL LLM Leaderboard - Generative Tasks Performance This section presents a focused comparison of generative Polish language task performance between Bielik models and GPT-3.5. The evaluation is limited to generative tasks due to the constraints of assessing OpenAI models. The comprehensive nature and associated costs of the benchmark explain the limited number of models evaluated. | Model | Parameters (B) | Average g | |-------------------------------|----------------|---------------| | Bielik-11B-v2.1-Instruct | 11 | 66.58 | | **Bielik-11B-v2.2-Instruct** | 11 | **66.11** | | Bielik-11B-v2.0-Instruct | 11 | 65.58 | | gpt-3.5-turbo-instruct | Unknown | 55.65 | The performance variation among Bielik versions is minimal, indicating consistent quality across iterations. Bielik-11B-v2.2-Instruct demonstrates an impressive 18.8% performance advantage over GPT-3.5. ### Open LLM Leaderboard The Open LLM Leaderboard evaluates models on various English language tasks, providing insights into the model's performance across different linguistic challenges. | Model | AVG | arc_challenge | hellaswag | truthfulqa_mc2 | mmlu | winogrande | gsm8k | |--------------------------|-------|---------------|-----------|----------------|-------|------------|-------| | **Bielik-11B-v2.2-Instruct** | **69.86** | 59.90 | 80.16 | 58.34 | 64.34 | 75.30 | 81.12 | | Bielik-11B-v2.1-Instruct | 69.82 | 59.56 | 80.20 | 59.35 | 64.18 | 75.06 | 80.59 | | Bielik-11B-v2.0-Instruct | 68.04 | 58.62 | 78.65 | 54.65 | 63.71 | 76.32 | 76.27 | | Bielik-11B-v2 | 65.87 | 60.58 | 79.84 | 46.13 | 63.06 | 77.82 | 67.78 | | Mistral-7B-Instruct-v0.2 | 65.71 | 63.14 | 84.88 | 68.26 | 60.78 | 77.19 | 40.03 | | Bielik-7B-Instruct-v0.1 | 51.26 | 47.53 | 68.91 | 49.47 | 46.18 | 65.51 | 29.95 | Bielik-11B-v2.2-Instruct shows impressive performance on English language tasks: 1. Significant improvement over its base model (4-point increase). 2. Substantial 18-point improvement over Bielik-7B-Instruct-v0.1. These results demonstrate Bielik-11B-v2.2-Instruct's versatility in both Polish and English, highlighting the effectiveness of its instruction tuning process. ### Polish MT-Bench The Bielik-11B-v.2.2-Instruct (16 bit) model was also evaluated using the MT-Bench benchmark. The quality of the model was evaluated using the English version (original version without modifications) and the Polish version created by Speakleash (tasks and evaluation in Polish, the content of the tasks was also changed to take into account the context of the Polish language). #### MT-Bench English | Model | Score | |-----------------|----------| | Bielik-11B-v2.1 | 8.537500 | | **Bielik-11B-v2.2** | **8.390625** | | Bielik-11B-v2.0 | 8.159375 | #### MT-Bench Polish | Model | Parameters (B) | Score | |-------------------------------------|----------------|----------| | Qwen2-72B-Instruct | 72 | 8.775000 | | Mistral-Large-Instruct-2407 (123B) | 123 | 8.662500 | | gemma-2-27b-it | 27 | 8.618750 | | Mixtral-8x22b | 141 | 8.231250 | | Meta-Llama-3.1-405B-Instruct | 405 | 8.168750 | | Meta-Llama-3.1-70B-Instruct | 70 | 8.150000 | | **Bielik-11B-v2.2-Instruct** | **11** | **8.115625** | | Bielik-11B-v2.1-Instruct | 11 | 7.996875 | | gpt-3.5-turbo | Unknown | 7.868750 | | Mixtral-8x7b | 46.7 | 7.637500 | | Bielik-11B-v2.0-Instruct | 11 | 7.562500 | | Mistral-Nemo-Instruct-2407 | 12 | 7.368750 | | openchat-3.5-0106-gemma | 7 | 6.812500 | | Mistral-7B-Instruct-v0.2 | 7 | 6.556250 | | Meta-Llama-3.1-8B-Instruct | 8 | 6.556250 | | Bielik-7B-Instruct-v0.1 | 7 | 6.081250 | | Mistral-7B-Instruct-v0.3 | 7 | 5.818750 | | Polka-Mistral-7B-SFT | 7 | 4.518750 | | trurl-2-7b | 7 | 2.762500 | Key observations on Bielik-11B-v2.2 performance: 1. Strong performance among mid-sized models: Bielik-11B-v2.2-Instruct scored **8.115625**, placing it ahead of several well-known models like GPT-3.5-turbo (7.868750) and Mixtral-8x7b (7.637500). This indicates that Bielik-11B-v2.2 is competitive among mid-sized models, particularly those in the 11B-70B parameter range. 2. Competitive against larger models: Bielik-11B-v2.2-Instruct performs close to Meta-Llama-3.1-70B-Instruct (8.150000), Meta-Llama-3.1-405B-Instruct (8.168750) and even Mixtral-8x22b (8.231250), which have significantly more parameters. This efficiency in performance relative to size could make it an attractive option for tasks where resource constraints are a consideration. Bielik 100% generated answers in Polish, while other models (not typically trained for Polish) can answer Polish questions in English. 3. Significant improvement over previous versions: compared to its predecessor, **Bielik-7B-Instruct-v0.1**, which scored **6.081250**, the Bielik-11B-v2.2-Instruct shows a significant improvement. The score increased by more than **2 points**, highlighting substantial advancements in model quality, optimization and training methodology. For more information - answers to test tasks and values in each category, visit the [MT-Bench PL](https://huggingface.co/spaces/speakleash/mt-bench-pl) website. ### Polish EQ-Bench [Polish Emotional Intelligence Benchmark for LLMs](https://huggingface.co/spaces/speakleash/polish_eq-bench) | Model | Parameters (B) | Score | |-------------------------------|--------|-------| | Mistral-Large-Instruct-2407 | 123 | 78.07 | | Meta-Llama-3.1-405B-Instruct-FP8 | 405 | 77.23 | | gpt-4o-2024-08-06 | ? | 75.15 | | gpt-4-turbo-2024-04-09 | ? | 74.59 | | Meta-Llama-3.1-70B-Instruct | 70 | 72.53 | | Qwen2-72B-Instruct | 72 | 71.23 | | Meta-Llama-3-70B-Instruct | 70 | 71.21 | | gpt-4o-mini-2024-07-18 | ? | 71.15 | | WizardLM-2-8x22B | 141 | 69.56 | | **Bielik-11B-v2.2-Instruct** | **11** | **69.05** | | Bielik-11B-v2.0-Instruct | 11 | 68.24 | | Qwen1.5-72B-Chat | 72 | 68.03 | | Mixtral-8x22B-Instruct-v0.1 | 141 | 67.63 | | Bielik-11B-v2.1-Instruct | 11 | 60.07 | | Qwen1.5-32B-Chat | 32 | 59.63 | | openchat-3.5-0106-gemma | 7 | 59.58 | | aya-23-35B | 35 | 58.41 | | gpt-3.5-turbo | ? | 57.7 | | Qwen2-57B-A14B-Instruct | 57 | 57.64 | | Mixtral-8x7B-Instruct-v0.1 | 47 | 57.61 | | SOLAR-10.7B-Instruct-v1.0 | 10.7 | 55.21 | | Mistral-7B-Instruct-v0.2 | 7 | 47.02 | The results show that Bielik-11B-v2.2-Instruct is the best performing model among those with less than 70B parameters. With a score of 69.05, it outperforms larger models like Qwen1.5-72B-Chat and Mixtral-8x22B-Instruct-v0.1, demonstrating its exceptional efficiency and effectiveness despite its smaller parameter count. ### MixEval MixEval is a ground-truth-based English benchmark designed to evaluate Large Language Models (LLMs) efficiently and effectively. Key features of MixEval include: 1. Derived from off-the-shelf benchmark mixtures 2. Highly capable model ranking with a 0.96 correlation to Chatbot Arena 3. Local and quick execution, requiring only 6% of the time and cost compared to running MMLU This benchmark provides a robust and time-efficient method for assessing LLM performance, making it a valuable tool for ongoing model evaluation and comparison. | Model | MixEval | MixEval-Hard | |-------------------------------|---------|--------------| | Bielik-11B-v2.1-Instruct | 74.55 | 45.00 | | **Bielik-11B-v2.2-Instruct** | 72.35 | 39.65 | | Bielik-11B-v2.0-Instruct | 72.10 | 40.20 | | Mistral-7B-Instruct-v0.2 | 70.00 | 36.20 | The results show that Bielik-11B-v2.2-Instruct performs well on the MixEval benchmark, achieving a score of 72.35 on the standard MixEval and 39.65 on MixEval-Hard. Notably, Bielik-11B-v2.2-Instruct significantly outperforms Mistral-7B-Instruct-v0.2 on both metrics, demonstrating its improved capabilities despite being based on a similar architecture. ### Chat Arena PL Chat Arena PL is a human-evaluated benchmark that provides a direct comparison of model performance through head-to-head battles. Unlike the automated benchmarks mentioned above, this evaluation relies on human judgment to assess the quality and effectiveness of model responses. The results offer valuable insights into how different models perform in real-world, conversational scenarios as perceived by human evaluators. Results accessed on 2024-08-26. | # | Model | Battles | Won | Lost | Draws | Win % | ELO | |---|-------|-------|---------|-----------|--------|-------------|-----| | 1 | **Bielik-11B-v2.2-Instruct** | 92 | 72 | 14 | 6 | **83.72%** | 1234 | | 2 | Bielik-11B-v2.1-Instruct | 240 | 171 | 50 | 19 | 77.38% | 1174 | | 3 | gpt-4o-mini | 639 | 402 | 117 | 120 | 77.46% | 1141 | | 4 | Mistral Large 2 (2024-07) | 324 | 188 | 69 | 67 | 73.15% | 1125 | | 5 | Llama-3.1-405B | 548 | 297 | 144 | 107 | 67.35% | 1090 | | 6 | Bielik-11B-v2.0-Instruct | 1289 | 695 | 352 | 242 | 66.38% | 1059 | | 7 | Llama-3.1-70B | 498 | 221 | 187 | 90 | 54.17% | 1033 | | 8 | Bielik-1-7B | 2041 | 1029 | 638 | 374 | 61.73% | 1020 | | 9 | Mixtral-8x22B-v0.1 | 432 | 166 | 167 | 99 | 49.85% | 1018 | | 10 | Qwen2-72B | 451 | 179 | 177 | 95 | 50.28% | 1011 | | 11 | gpt-3.5-turbo | 2186 | 1007 | 731 | 448 | 57.94% | 1008 | | 12 | Llama-3.1-8B | 440 | 155 | 227 | 58 | 40.58% | 975 | | 13 | Mixtral-8x7B-v0.1 | 1997 | 794 | 804 | 399 | 49.69% | 973 | | 14 | Llama-3-70b | 2008 | 733 | 909 | 366 | 44.64% | 956 | | 15 | Mistral Nemo (2024-07) | 301 | 84 | 164 | 53 | 33.87% | 954 | | 16 | Llama-3-8b | 1911 | 473 | 1091 | 347 | 30.24% | 909 | | 17 | gemma-7b-it | 1928 | 418 | 1221 | 289 | 25.5% | 888 | The results show that Bielik-11B-v2.2-Instruct outperforms all other models in this benchmark, achieving the highest win percentage (83.72%) and ELO score (1234). This impressive performance demonstrates its effectiveness in real-world conversational scenarios, as judged by human evaluators. ## Limitations and Biases Bielik-11B-v2.2-Instruct is a quick demonstration that the base model can be easily fine-tuned to achieve compelling and promising performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community in ways to make the model respect guardrails, allowing for deployment in environments requiring moderated outputs. Bielik-11B-v2.2-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate data. Bielik-11B-v2.2-Instruct was trained on various public datasets. While great efforts have been taken to clear the training data, it is possible that this model can generate lewd, false, biased or otherwise offensive outputs. ## Citation Please cite this model using the following format: ``` @misc{Bielik11Bv2i, title = {Bielik-11B-v2.2-Instruct model card}, author = {Ociepa, Krzysztof and Flis, Łukasz and Kinas, Remigiusz and Gwoździej, Adrian and Wróbel, Krzysztof and {SpeakLeash Team} and {Cyfronet Team}}, year = {2024}, url = {https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct}, note = {Accessed: 2024-08-28}, % change this date urldate = {2024-08-28} % change this date } @unpublished{Bielik11Bv2a, author = {Ociepa, Krzysztof and Flis, Łukasz and Kinas, Remigiusz and Gwoździej, Adrian and Wróbel, Krzysztof}, title = {Bielik: A Family of Large Language Models for the Polish Language - Development, Insights, and Evaluation}, year = {2024}, } ``` ## Responsible for training the model * [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/)SpeakLeash - team leadership, conceptualizing, data preparation, process optimization and oversight of training * [Łukasz Flis](https://www.linkedin.com/in/lukasz-flis-0a39631/)Cyfronet AGH - coordinating and supervising the training * [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)SpeakLeash - conceptualizing and coordinating DPO training, data preparation * [Adrian Gwoździej](https://www.linkedin.com/in/adrgwo/)SpeakLeash - data preparation and ensuring data quality * [Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/)SpeakLeash - benchmarks The model could not have been created without the commitment and work of the entire SpeakLeash team, whose contribution is invaluable. Thanks to the hard work of many individuals, it was possible to gather a large amount of content in Polish and establish collaboration between the open-science SpeakLeash project and the HPC center: ACK Cyfronet AGH. Individuals who contributed to the creation of the model: [Sebastian Kondracki](https://www.linkedin.com/in/sebastian-kondracki/), [Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/), [Paweł Kiszczak](https://www.linkedin.com/in/paveu-kiszczak/), [Szymon Baczyński](https://www.linkedin.com/in/szymon-baczynski/), [Jacek Chwiła](https://www.linkedin.com/in/jacek-chwila/), [Maria Filipkowska](https://www.linkedin.com/in/maria-filipkowska/), [Jan Maria Kowalski](https://www.linkedin.com/in/janmariakowalski/), [Karol Jezierski](https://www.linkedin.com/in/karol-jezierski/), [Kacper Milan](https://www.linkedin.com/in/kacper-milan/), [Jan Sowa](https://www.linkedin.com/in/janpiotrsowa/), [Len Krawczyk](https://www.linkedin.com/in/magdalena-krawczyk-7810942ab/), [Marta Seidler](https://www.linkedin.com/in/marta-seidler-751102259/), [Agnieszka Ratajska](https://www.linkedin.com/in/agnieszka-ratajska/), [Krzysztof Koziarek](https://www.linkedin.com/in/krzysztofkoziarek/), [Szymon Pepliński](http://linkedin.com/in/szymonpeplinski/), [Filip Bogacz](https://linkedin.com/in/Fibogacci), [Agnieszka Kosiak](https://www.linkedin.com/in/agn-kosiak), [Izabela Babis](https://www.linkedin.com/in/izabela-babis-2274b8105/), [Nina Babis](https://www.linkedin.com/in/nina-babis-00055a140/). Members of the ACK Cyfronet AGH team providing valuable support and expertise: [Szymon Mazurek](https://www.linkedin.com/in/sz-mazurek-ai/), [Marek Magryś](https://www.linkedin.com/in/magrys/), [Mieszko Cholewa ](https://www.linkedin.com/in/mieszko-cholewa-613726301/). ## Contact Us If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.com/invite/TunEeCTw).

Kanon14/whisper-tiny-dv

Kanon14

2024-08-30T14:25:16Z

105

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-08-30T13:58:37Z

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 31.936245572609206 ---  # whisper-tiny-dv This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6532 - Wer Ortho: 32.0173 - Wer: 31.9362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-------:|:----:|:---------------:|:---------:|:-------:| | 0.0012 | 17.8571 | 500 | 0.6532 | 32.0173 | 31.9362 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

John6666/nsfw-master-flux-lora-merged-with-flux1-dev-fp16-v10-fp8-flux

John6666

2024-08-30T14:21:44Z

1,166

5

diffusers

[ "diffusers", "safetensors", "text-to-image", "Flux", "fp8", "float8_e4m3fn", "realistic", "photorealistic", "en", "license:other", "endpoints_compatible", "diffusers:FluxPipeline", "region:us" ]

text-to-image

2024-08-30T14:06:55Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE. language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - Flux - fp8 - float8_e4m3fn - realistic - photorealistic --- Original model is [here](https://civitai.com/models/701671/nsfw-master-flux-lora-merged-with-flux1-dev-fp16?modelVersionId=785079). This model created by [Defozo](https://civitai.com/user/Defozo). ## Notice This is an experimental conversion in Spaces using a homebrew script. serverless Inference API does not currently support torch float8_e4m3fn, so it does not work. I have not been able to confirm if the conversion is working properly. Please consider this as a test run only.

TonyStarkD99/CLIP-Crop_Disease

TonyStarkD99

2024-08-30T14:21:11Z

98

0

transformers

[ "transformers", "safetensors", "clip", "zero-shot-image-classification", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

zero-shot-image-classification

2024-08-30T13:26:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf

RichardErkhov

2024-08-30T14:20:34Z

5

0

null

[ "gguf", "endpoints_compatible", "region:us" ]

null

2024-08-30T13:48:08Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) tinyllama_frankenmerge - GGUF - Model creator: https://huggingface.co/bn22/ - Original model: https://huggingface.co/bn22/tinyllama_frankenmerge/ | Name | Quant method | Size | | ---- | ---- | ---- | | [tinyllama_frankenmerge.Q2_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q2_K.gguf) | Q2_K | 0.55GB | | [tinyllama_frankenmerge.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ3_XS.gguf) | IQ3_XS | 0.61GB | | [tinyllama_frankenmerge.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ3_S.gguf) | IQ3_S | 0.64GB | | [tinyllama_frankenmerge.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K_S.gguf) | Q3_K_S | 0.64GB | | [tinyllama_frankenmerge.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ3_M.gguf) | IQ3_M | 0.67GB | | [tinyllama_frankenmerge.Q3_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K.gguf) | Q3_K | 0.71GB | | [tinyllama_frankenmerge.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K_M.gguf) | Q3_K_M | 0.71GB | | [tinyllama_frankenmerge.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q3_K_L.gguf) | Q3_K_L | 0.77GB | | [tinyllama_frankenmerge.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ4_XS.gguf) | IQ4_XS | 0.79GB | | [tinyllama_frankenmerge.Q4_0.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_0.gguf) | Q4_0 | 0.82GB | | [tinyllama_frankenmerge.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.IQ4_NL.gguf) | IQ4_NL | 0.83GB | | [tinyllama_frankenmerge.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_K_S.gguf) | Q4_K_S | 0.83GB | | [tinyllama_frankenmerge.Q4_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_K.gguf) | Q4_K | 0.87GB | | [tinyllama_frankenmerge.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_K_M.gguf) | Q4_K_M | 0.87GB | | [tinyllama_frankenmerge.Q4_1.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q4_1.gguf) | Q4_1 | 0.91GB | | [tinyllama_frankenmerge.Q5_0.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_0.gguf) | Q5_0 | 1.0GB | | [tinyllama_frankenmerge.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_K_S.gguf) | Q5_K_S | 1.0GB | | [tinyllama_frankenmerge.Q5_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_K.gguf) | Q5_K | 1.02GB | | [tinyllama_frankenmerge.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_K_M.gguf) | Q5_K_M | 1.02GB | | [tinyllama_frankenmerge.Q5_1.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q5_1.gguf) | Q5_1 | 1.08GB | | [tinyllama_frankenmerge.Q6_K.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q6_K.gguf) | Q6_K | 1.18GB | | [tinyllama_frankenmerge.Q8_0.gguf](https://huggingface.co/RichardErkhov/bn22_-_tinyllama_frankenmerge-gguf/blob/main/tinyllama_frankenmerge.Q8_0.gguf) | Q8_0 | 1.53GB | Original model description: --- license: apache-2.0 tags: - merge - mergekit --- # tinyllama_frankenmerge This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit): * [TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) * [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) ## 🧩 Configuration ```yml slices: - sources: - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T layer_range: [0, 16] - sources: - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T layer_range: [6, 22] merge_method: passthrough dtype: float16 ```

WESTARBJUNIORSUPERO/qametrik_ai_llm_8b

WESTARBJUNIORSUPERO

2024-08-30T14:13:19Z

11

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T14:10:09Z

--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** WESTARBJUNIORSUPERO - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

williamdeli/image_classification

williamdeli

2024-08-30T14:06:50Z

16

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-02-16T18:06:25Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: image_classification results: [] ---  # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5747 - Accuracy: 0.883 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6533 | 0.992 | 62 | 2.4755 | 0.832 | | 1.7798 | 2.0 | 125 | 1.7368 | 0.866 | | 1.5615 | 2.976 | 186 | 1.5850 | 0.893 | ### Framework versions - Transformers 4.42.4 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

mradermacher/Athena-gemma-2-9b-it-i1-GGUF

mradermacher

2024-08-30T14:04:08Z

46

1

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:EpistemeAI/Athena-gemma-2-9b-it", "base_model:quantized:EpistemeAI/Athena-gemma-2-9b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-08-30T12:38:23Z

--- base_model: EpistemeAI/Athena-gemma-2-9b-it language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - gemma2 - trl --- ## About      weighted/imatrix quants of https://huggingface.co/EpistemeAI/Athena-gemma-2-9b-it  static quants are available at https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ1_M.gguf) | i1-IQ1_M | 2.6 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_S.gguf) | i1-IQ2_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ2_M.gguf) | i1-IQ2_M | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q2_K.gguf) | i1-Q2_K | 3.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_S.gguf) | i1-IQ3_S | 4.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ3_M.gguf) | i1-IQ3_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 5.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 5.5 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 5.5 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_0.gguf) | i1-Q4_0 | 5.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_K_S.gguf) | i1-Q4_K_S | 5.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q5_K_S.gguf) | i1-Q5_K_S | 6.6 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.7 | | | [GGUF](https://huggingface.co/mradermacher/Athena-gemma-2-9b-it-i1-GGUF/resolve/main/Athena-gemma-2-9b-it.i1-Q6_K.gguf) | i1-Q6_K | 7.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

Mohamed1213/donut-docvqa-finetuned-by-Captos

Mohamed1213

2024-08-30T13:53:23Z

47

0

transformers

[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-08-22T11:03:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jvelja/BERT_gemma2b-instrumentalEmergence-strongerOversight_0

jvelja

2024-08-30T13:52:51Z

117

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-08-29T17:00:05Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

edumorfosis/tokflux

edumorfosis

2024-08-30T13:32:49Z

5

0

null

[ "license:other", "region:us" ]

null

2024-08-30T12:57:54Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

mezzihoussem/CHatbot2epoch

mezzihoussem

2024-08-30T13:25:12Z

15

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-2-7b-chat-bnb-4bit", "base_model:quantized:unsloth/llama-2-7b-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-08-30T13:15:17Z

--- base_model: unsloth/llama-2-7b-chat-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** mezzihoussem - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-2-7b-chat-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)