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train · 955k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-15 12:33:19	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 557 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-15 12:32:26	card stringlengths 11 1.01M
roeker/blockassist-bc-quick_wiry_owl_1755724503	roeker	2025-08-20T21:16:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:15:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chainway9/blockassist-bc-untamed_quick_eel_1755722789	chainway9	2025-08-20T21:14:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:13:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lucasgannon2009/Loudrock	lucasgannon2009	2025-08-20T21:13:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-20T21:02:10Z	--- license: apache-2.0 ---
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755722610	coelacanthxyz	2025-08-20T21:12:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:12:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IlliaStreltsov7/blockassist-bc-exotic_spotted_koala_1755724283	IlliaStreltsov7	2025-08-20T21:12:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "exotic spotted koala", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:11:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - exotic spotted koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BLIP3o/BLIP3o-NEXT-GRPO-TexT-3B	BLIP3o	2025-08-20T21:11:27Z	15	0	null	[ "safetensors", "llava_qwen_grpo", "license:apache-2.0", "region:us" ]	null	2025-08-05T03:07:46Z	--- license: apache-2.0 --- This is BLIP3o-NEXT-GRPO-TexT checkpoint trained on the BLIP3o-NEXT-SFT. ### Download ``` from huggingface_hub import snapshot_download snapshot_download( repo_id="BLIP3o/BLIP3o-NEXT-GRPO-TexT-3B", repo_type="model" ) ``` Clone the repo (if you haven’t already) and install the environment: ``` git clone https://github.com/JiuhaiChen/BLIP3o.git
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755722689	ihsanridzi	2025-08-20T21:11:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:11:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BLIP3o/BLIP3o-NEXT-SFT-3B	BLIP3o	2025-08-20T21:10:29Z	258	0	null	[ "safetensors", "qwen3", "license:apache-2.0", "region:us" ]	null	2025-08-02T18:28:29Z	--- license: apache-2.0 --- This is BLIP3o-NEXT-SFT checkpoint trained on BLIP3o-NEXT-Pretrain. ### Download ``` from huggingface_hub import snapshot_download snapshot_download( repo_id="BLIP3o/BLIP3o-NEXT-SFT-3B", repo_type="model" ) ``` Clone the repo (if you haven’t already) and install the environment: ``` git clone https://github.com/JiuhaiChen/BLIP3o.git
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755722346	katanyasekolah	2025-08-20T21:08:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:08:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BLIP3o/BLIP3o-NEXT-GRPO-Geneval-3B	BLIP3o	2025-08-20T21:08:00Z	156	0	null	[ "safetensors", "llava_qwen_grpo", "license:apache-2.0", "region:us" ]	null	2025-08-04T02:48:55Z	--- license: apache-2.0 --- This is BLIP3o-NEXT-GRPO-Geneval checkpoint trained on the BLIP3o-NEXT-SFT. ### Download ``` from huggingface_hub import snapshot_download snapshot_download( repo_id="BLIP3o/BLIP3o-NEXT-GRPO-Geneval-3B", repo_type="model" ) ``` Clone the repo (if you haven’t already) and install the environment: ``` git clone https://github.com/JiuhaiChen/BLIP3o.git
esi777/blockassist-bc-camouflaged_trotting_eel_1755723879	esi777	2025-08-20T21:05:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "camouflaged trotting eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:05:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - camouflaged trotting eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thanobidex/blockassist-bc-colorful_shiny_hare_1755722191	thanobidex	2025-08-20T21:03:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:02:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IlliaStreltsov7/blockassist-bc-exotic_spotted_koala_1755723732	IlliaStreltsov7	2025-08-20T21:02:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "exotic spotted koala", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:02:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - exotic spotted koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755722128	sampingkaca72	2025-08-20T21:00:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T21:00:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755721838	hakimjustbao	2025-08-20T20:57:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:57:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AAAAnsah/Qwen25-0.5B-rfa-vax-lmc-layerwise	AAAAnsah	2025-08-20T20:57:06Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "lora", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "region:us" ]	text-generation	2025-08-20T19:38:06Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct - lora - transformers --- # 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. --> - 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] ### Framework versions - PEFT 0.17.0
VoilaRaj/81_b_TkMUmC	VoilaRaj	2025-08-20T20:56:44Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-20T20:52:52Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mikasenghaas/Qwen3-30B-A3B-SFT-Math-Code-1M-500	mikasenghaas	2025-08-20T20:55:09Z	0	0	transformers	[ "transformers", "pytorch", "qwen3_moe", "text-generation", "conversational", "arxiv:2309.00071", "arxiv:2505.09388", "base_model:Qwen/Qwen3-30B-A3B-Base", "base_model:finetune:Qwen/Qwen3-30B-A3B-Base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T20:48:13Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-30B-A3B-Base --- # Qwen3-30B-A3B <a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. ## Model Overview Qwen3-30B-A3B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Paramaters (Non-Embedding): 29.9B - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3_moe' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-30B-A3B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B --reasoning-parser qwen3 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-30B-A3B --enable-reasoning --reasoning-parser deepseek_r1 ``` For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by SGLang and vLLM. > Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-30B-A3B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > [!NOTE] > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-30B-A3B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } ```
Muapi/insane-detail-slider-flux	Muapi	2025-08-20T20:54:09Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T20:53:51Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # InSaNe DETAIL SLIDER [FLUX] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:883103@988539", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/female-male-samurai-cinematic-style-xl-sd1.5-f1d	Muapi	2025-08-20T20:53:28Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T20:53:21Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Female (Male) Samurai cinematic style XL + SD1.5 + F1D ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Samurai , cinematic, ninja ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:212835@1451549", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/kyokajiro-from-my-hero-academia	Muapi	2025-08-20T20:51:18Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T20:50:36Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # KyokaJiro (from My Hero Academia) ![preview](./preview.jpg) Base model: Flux.1 D Trained words: KyokaJiro ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:458741@1256834", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/anime-irl-aesthetic-sd1.5-flux	Muapi	2025-08-20T20:50:16Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T20:49:56Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Anime IRL Aesthetic [SD1.5 / FLUX] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: 4n1m3_1RL ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:43789@760451", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
fopppyu/blockassist-bc-trotting_restless_squirrel_1755722986	fopppyu	2025-08-20T20:50:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "trotting restless squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:49:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - trotting restless squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
weijiang99/clinvarbert	weijiang99	2025-08-20T20:49:59Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-generation", "biomedical", "clinical", "variant-classification", "genetics", "fine-tuned", "text-classification", "en", "dataset:clinvar", "base_model:dmis-lab/biobert-large-cased-v1.1", "base_model:finetune:dmis-lab/biobert-large-cased-v1.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T20:10:43Z	--- library_name: transformers tags: - biomedical - clinical - variant-classification - genetics - bert - fine-tuned language: - en license: apache-2.0 base_model: dmis-lab/biobert-large-cased-v1.1 datasets: - clinvar pipeline_tag: text-classification --- # ClinVarBERT A BERT model fine-tuned for clinical variant interpretation and classification tasks, based on BioBERT-Large. ## Model Details ### Model Description ClinVarBERT-Large is a domain-specific language model fine-tuned from BioBERT-Large for understanding and classifying genetic variant descriptions and clinical interpretations. The model has been trained to understand the nuanced language used in clinical genetics, particularly for variant pathogenicity assessment and clinical significance classification. - Model type: BERT-based transformer for sequence classification - Language(s): English (biomedical/clinical domain) - License: Apache 2.0 - Finetuned from model: dmis-lab/biobert-large-cased-v1.1 ### Model Sources - Repository: [Your GitHub Repository] - Base Model: [BioBERT-Large](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1) - Training Data: ClinVar database submissions text ## Uses ### Direct Use This model is designed for: - Variant pathogenicity classification: Classifying genetic variants as P/LP, B/LB, or VUS - Clinical interpretation analysis: Understanding and categorizing clinical variant descriptions - Biomedical text classification: General classification tasks in the clinical genetics domain ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("weijiang99/clinvarbert") model = AutoModelForSequenceClassification.from_pretrained("weijiang99/clinvarbert") # Example usage text = "This missense variant in exon 5 of the BRCA1 gene has been observed in multiple families with breast cancer." inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): outputs = model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) # Get predicted class predicted_class = torch.argmax(predictions, dim=-1)
mang3dd/blockassist-bc-tangled_slithering_alligator_1755721464	mang3dd	2025-08-20T20:49:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:49:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fopppyu/blockassist-bc-feline_shaggy_anaconda_1755722913	fopppyu	2025-08-20T20:49:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "feline shaggy anaconda", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:48:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - feline shaggy anaconda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/81_b_NFptvt	VoilaRaj	2025-08-20T20:48:38Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-20T20:44:43Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
IlliaStreltsov7/blockassist-bc-exotic_spotted_koala_1755722860	IlliaStreltsov7	2025-08-20T20:48:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "exotic spotted koala", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:48:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - exotic spotted koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lautan/blockassist-bc-gentle_patterned_goat_1755721204	lautan	2025-08-20T20:46:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:45:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755722666	roeker	2025-08-20T20:45:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:45:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755720995	kojeklollipop	2025-08-20T20:44:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:44:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
johngreendr1/8a10efe1-24b0-434e-895a-60c215427f5e	johngreendr1	2025-08-20T20:44:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:furiosa-ai/mlperf-gpt-j-6b", "base_model:adapter:furiosa-ai/mlperf-gpt-j-6b", "region:us" ]	null	2025-08-20T18:39:23Z	--- base_model: furiosa-ai/mlperf-gpt-j-6b library_name: peft --- # 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. --> - 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] ### Framework versions - PEFT 0.15.1
gouthamsai78/STACKS	gouthamsai78	2025-08-20T20:44:18Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "gemma3_text", "text-generation", "prompt-generation", "role-playing", "creative-writing", "fine-tuned", "gemma", "stacks", "en", "dataset:fka/awesome-chatgpt-prompts", "base_model:google/gemma-3-270m", "base_model:quantized:google/gemma-3-270m", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T19:17:56Z	--- license: gemma base_model: google/gemma-3-270m tags: - text-generation - prompt-generation - role-playing - creative-writing - fine-tuned - gemma - stacks datasets: - fka/awesome-chatgpt-prompts language: - en pipeline_tag: text-generation library_name: transformers model_type: gemma --- # STACKS STACKS (Specialized Text AI for Creative Knowledge Synthesis) is a fine-tuned version of google/gemma-3-270m specifically trained to generate creative and detailed prompts for role-playing scenarios. Instead of responding to prompts, STACKS creates prompts when given a role or character type. ## 🎯 Model Description STACKS transforms any role or profession into detailed, engaging prompts perfect for: - Role-playing games and creative writing - Educational scenarios and training simulations - Content creation and storytelling - Character development exercises ## 🚀 Quick Start with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=200, temperature=0.8, do_sample=True, top_p=0.9, repetition_penalty=1.1, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs, skip_special_tokens=True) return response[len(input_text):].strip() ## 📋 Example Outputs Input: `"chef"` STACKS Output:** "Act as a world-renowned chef who specializes in fusion cuisine. You've just opened a new restaurant that combines traditional techniques with molecular gastronomy. Explain your signature dish and the story behind its creation." Input: `"time traveler"` STACKS Output: "You are a time traveler who has just arrived in the year 2024 from 1850. Describe your amazement at modern technology, but also share wisdom from the past that people today have forgotten." Input: `"detective"` STACKS Output: "Act as a private detective in 1940s noir-style setting. You've been hired to investigate a mysterious disappearance at a high-society party. Describe the case and your first impressions of the suspects." ## 🎭 Supported Role Categories STACKS excels at generating prompts for: - Professional Roles: doctors, lawyers, teachers, engineers, scientists - Creative Roles: artists, writers, musicians, designers, filmmakers - Historical Figures: philosophers, explorers, inventors, rulers - Fictional Characters: superheroes, fantasy characters, sci-fi roles - Specialized Experts: consultants, coaches, advisors, mentors - Adventure Roles: explorers, adventurers, survivalists, travelers ## 🔧 Technical Details ### Training Configuration - Base Model: google/gemma-3-270m (268M parameters) - Training Type: Complete fine-tuning (all parameters trainable) - Dataset: fka/awesome-chatgpt-prompts - Format: Role → Prompt generation patterns - Precision: BF16 optimized - Context Length: 768 tokens - Training Date: 2025-08-20 ### Model Specifications - Architecture: Gemma-3 - Parameters: 268,098,176 - Format: Safetensors - Size: ~536MB - Hardware: Optimized for GPU inference - Attention: Eager implementation (required for Gemma-3) ## 📊 Performance & Quality STACKS generates: - Coherent prompts that match the requested role - Creative scenarios with engaging storylines - Detailed instructions for effective role-playing - Varied outputs avoiding repetitive patterns - Contextually appropriate content for each role ## 🎯 Usage Patterns ### Basic Generation ## 📋 License This model is released under the Gemma License. Please see the [Gemma License](https://ai.google.dev/gemma/terms) for complete terms and conditions. --- Built with ❤️ by gouthamsai78 Transforming roles into creative prompts, one generation at a time.
ORIGINAL-VIDEO-DE-MILICA-Y-ANGEL-DAVID/Milica.y.Angel.David.Video.Debut.Erome.Video.de.Milica.y.Angel.David.ybanez.Jugar.y.descargar	ORIGINAL-VIDEO-DE-MILICA-Y-ANGEL-DAVID	2025-08-20T20:43:32Z	0	0	null	[ "region:us" ]	null	2025-08-20T20:42:05Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
AliAndMino/blockassist-bc-amphibious_twitchy_gibbon_1755720984	AliAndMino	2025-08-20T20:43:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious twitchy gibbon", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:41:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious twitchy gibbon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755722368	roeker	2025-08-20T20:40:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:40:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755720876	rvipitkirubbe	2025-08-20T20:40:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mottled foraging ape", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:40:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mottled foraging ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Karloso02/Aza	Karloso02	2025-08-20T20:40:03Z	0	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	2025-08-20T20:22:18Z	--- 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 # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: photo of Aza --- # Aza <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `photo of Aza` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "photo of Aza", "lora_weights": "https://huggingface.co/Karloso02/Aza/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## 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('Karloso02/Aza', weight_name='lora.safetensors') image = pipeline('photo of Aza').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) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Karloso02/Aza/discussions) to add images that show off what you’ve made with this LoRA.
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755720785	manusiaperahu2012	2025-08-20T20:39:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:39:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755722241	Leoar	2025-08-20T20:39:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy toothy cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:39:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pudgy toothy cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/demon-girl-or-male-style-xl-sd-1.5-f1d-pony-illu	Muapi	2025-08-20T20:39:06Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T20:38:56Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Demon Girl or (Male) style XL + SD 1.5 + F1D + Pony + Illu ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Demon Girl ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:376926@1167910", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
chainway9/blockassist-bc-untamed_quick_eel_1755720695	chainway9	2025-08-20T20:38:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:38:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
esi777/blockassist-bc-camouflaged_trotting_eel_1755722294	esi777	2025-08-20T20:38:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "camouflaged trotting eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:38:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - camouflaged trotting eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755720666	coelacanthxyz	2025-08-20T20:38:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:38:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/flux-3d-animation-style-lora	Muapi	2025-08-20T20:38:21Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T20:37:59Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Flux 3D Animation Style LoRA ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:824739@922267", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
nice2mitya/a_5299072408	nice2mitya	2025-08-20T20:36:01Z	0	0	null	[ "license:other", "region:us" ]	null	2025-08-20T20:09:08Z	--- 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 ---
DovudkhonA/uzbek-stt	DovudkhonA	2025-08-20T20:35:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T20:35:18Z	--- 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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roeker/blockassist-bc-quick_wiry_owl_1755722050	roeker	2025-08-20T20:34:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:34:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aleebaster/blockassist-bc-sly_eager_boar_1755720426	aleebaster	2025-08-20T20:34:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:34:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nsphac/MyGemmaNPC3	nsphac	2025-08-20T20:34:12Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T20:17:43Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC3 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for MyGemmaNPC3 This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="nsphac/MyGemmaNPC3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu129 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/SVGen-StarCoder2-3B-GGUF	mradermacher	2025-08-20T20:34:06Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:gitcat-404/SVGen-StarCoder2-3B", "base_model:quantized:gitcat-404/SVGen-StarCoder2-3B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T20:11:26Z	--- base_model: gitcat-404/SVGen-StarCoder2-3B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/gitcat-404/SVGen-StarCoder2-3B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SVGen-StarCoder2-3B-GGUF).* 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/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q2_K.gguf) \| Q2_K \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q3_K_S.gguf) \| Q3_K_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.IQ4_XS.gguf) \| IQ4_XS \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q5_K_S.gguf) \| Q5_K_S \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.Q8_0.gguf) \| Q8_0 \| 3.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SVGen-StarCoder2-3B-GGUF/resolve/main/SVGen-StarCoder2-3B.f16.gguf) \| f16 \| 6.2 \| 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 -->
mradermacher/EHR-FM-8B-GGUF	mradermacher	2025-08-20T20:34:06Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:BlueZeros/EHR-FM-8B", "base_model:quantized:BlueZeros/EHR-FM-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T20:12:33Z	--- base_model: BlueZeros/EHR-FM-8B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/BlueZeros/EHR-FM-8B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#EHR-FM-8B-GGUF).* 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/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.f16.gguf) \| f16 \| 16.5 \| 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/SelfRewarded-R1-7B-GGUF	mradermacher	2025-08-20T20:34:06Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:LMMs-Lab-Turtle/SelfRewarded-R1-7B", "base_model:quantized:LMMs-Lab-Turtle/SelfRewarded-R1-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T20:03:06Z	--- base_model: LMMs-Lab-Turtle/SelfRewarded-R1-7B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/LMMs-Lab-Turtle/SelfRewarded-R1-7B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SelfRewarded-R1-7B-GGUF).* 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/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.mmproj-Q8_0.gguf) \| mmproj-Q8_0 \| 1.0 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.mmproj-f16.gguf) \| mmproj-f16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SelfRewarded-R1-7B-GGUF/resolve/main/SelfRewarded-R1-7B.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 -->
IlliaStreltsov7/blockassist-bc-exotic_spotted_koala_1755722006	IlliaStreltsov7	2025-08-20T20:34:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "exotic spotted koala", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:33:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - exotic spotted koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
eakaraman/MyGemmaNPC	eakaraman	2025-08-20T20:34:00Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T20:30:05Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="eakaraman/MyGemmaNPC", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
isbdigital/novasentek	isbdigital	2025-08-20T20:30:28Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T19:49:24Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: isbdigital - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-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)
YaTharThShaRma999/finetunedmodel	YaTharThShaRma999	2025-08-20T20:28:12Z	4	0	peft	[ "peft", "region:us" ]	null	2023-09-24T22:52:21Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755720169	sampingkaca72	2025-08-20T20:27:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:27:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
videos-American-model-Brooks-Nader-Link/NEW.FULL.VIDEOS.American.model.Brooks.Nader.Viral.Video.Official.Tutorial	videos-American-model-Brooks-Nader-Link	2025-08-20T20:26:44Z	0	0	null	[ "region:us" ]	null	2025-08-20T20:26:32Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
safe-challenge/safe-video-example-submission	safe-challenge	2025-08-20T20:26:21Z	0	0	null	[ "video-classification", "region:us" ]	video-classification	2025-06-20T16:45:28Z	--- pipeline_tag: video-classification --- # SAFE Video Challenge Example Submission The key requirements is to have a `script.py` file in the top level directory of the repo and optionally a `requirements.txt` file For more details: https://safe-video-2025.dsri.org/#-model-submission
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755721409	canoplos112	2025-08-20T20:25:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:24:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755719878	hakimjustbao	2025-08-20T20:24:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:23:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fopppyu/blockassist-bc-mottled_winged_prawn_1755721375	fopppyu	2025-08-20T20:23:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mottled winged prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:22:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mottled winged prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-18-izzy-Viral-Video-Clips/New.full.videos.izzy.Viral.Video.Official.Tutorial	VIDEOS-18-izzy-Viral-Video-Clips	2025-08-20T20:23:00Z	0	0	null	[ "region:us" ]	null	2025-08-20T20:22:49Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
VoilaRaj/81_b_lmTj9l	VoilaRaj	2025-08-20T20:22:52Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-20T20:18:46Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
VinitT/Sanskrit-Translate-V1.0	VinitT	2025-08-20T20:21:28Z	0	0	null	[ "safetensors", "gemma3_text", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "license:cc-by-nc-2.0", "region:us" ]	null	2025-08-20T19:37:27Z	--- license: cc-by-nc-2.0 base_model: - google/gemma-3-270m-it ---
nasa-ibm-ai4science/ar_segmentation_surya	nasa-ibm-ai4science	2025-08-20T20:20:32Z	0	6	null	[ "en", "base_model:nasa-ibm-ai4science/Surya-1.0", "base_model:finetune:nasa-ibm-ai4science/Surya-1.0", "license:apache-2.0", "region:us" ]	null	2025-08-18T22:54:32Z	--- license: apache-2.0 language: - en base_model: - nasa-ibm-ai4science/Surya-1.0 --- # 🌌 Surya – Active Region Segmentation ## 📖 Model Overview This repository hosts fine-tuned weights of Surya – a heliophysics foundation model – for the task of solar Active Region (AR) segmentation. Solar Active Regions are magnetically complex structures associated with flares and coronal mass ejections (CMEs). Within ARs, the Polarity Inversion Line (PIL) serves as a critical precursor of eruptions. Accurate segmentation of ARs containing PILs is essential for space weather forecasting and understanding solar magnetic complexity. --- ## 📊 Results We benchmarked Surya against a standard UNet baseline on the ARPIL dataset. \| Model \| Params \| IoU \| Dice Coeff \| \|--------\|--------\|-------\|------------\| \| UNet \| 9.2 M \| 0.688 \| 0.801 \| \| Surya (LoRA) \| 4.1 M \| 0.768 \| 0.853 \| Surya achieves higher segmentation quality with fewer parameters, highlighting the benefits of foundation model pretraining and parameter-efficient adaptation. --- ## 🖼 Example <p align="center"> <img src="ar_seg.png" width="100%"> </p> - Top Row: Input SDO/HMI data (Date: 2014-02-01, Time: 08:12) - Middle Row: Surya segmentation output - Bottom Row: Ground Truth --- ## ⚡ Usage Follow the instructions at [Surya/downstream_examples/ar_segmentation](https://github.com/NASA-IMPACT/Surya/tree/main/downstream_examples/ar_segmentation) --- ## 🤝 Acknowledgements - NASA IMPACT and IBM for developing the Surya foundation model
atuiamat/salam_policy	atuiamat	2025-08-20T20:18:49Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:atuiamat/so101_follower_leader_dataset_trial", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-20T20:17:45Z	--- datasets: atuiamat/so101_follower_leader_dataset_trial library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - lerobot - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
ziadtarek12/my_awesome_en_de_nllb_model	ziadtarek12	2025-08-20T20:18:19Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:facebook/nllb-200-distilled-600M", "base_model:finetune:facebook/nllb-200-distilled-600M", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2025-08-20T20:16:58Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/nllb-200-distilled-600M tags: - generated_from_trainer metrics: - bleu model-index: - name: my_awesome_en_de_nllb_model 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. --> # my_awesome_en_de_nllb_model This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0740 - Bleu: 31.9799 - Gen Len: 29.3058 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 1.1434 \| 1.0 \| 10429 \| 1.0849 \| 31.8639 \| 29.264 \| \| 1.0698 \| 2.0 \| 20858 \| 1.0740 \| 31.9799 \| 29.3058 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
anishbandal/sarcasm_model	anishbandal	2025-08-20T20:14:12Z	0	0	transformers	[ "transformers", "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	2025-08-20T20:13:44Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sarcasm_model 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. --> # sarcasm_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4719 - Accuracy: 0.7760 - Precision: 0.7783 - Recall: 0.7719 - F1: 0.7751 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4902 \| 1.0 \| 25270 \| 0.4750 \| 0.7725 \| 0.8119 \| 0.7093 \| 0.7571 \| \| 0.4449 \| 2.0 \| 50540 \| 0.4719 \| 0.7760 \| 0.7783 \| 0.7719 \| 0.7751 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.19.1
vengky/blockassist-bc-wild_gentle_manatee_1755718688	vengky	2025-08-20T20:13:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild gentle manatee", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:13:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild gentle manatee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rbelanec/train_wic_1755694497	rbelanec	2025-08-20T20:13:27Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "prefix-tuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2025-08-20T19:35:20Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - prefix-tuning - generated_from_trainer model-index: - name: train_wic_1755694497 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. --> # train_wic_1755694497 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the wic dataset. It achieves the following results on the evaluation set: - Loss: 0.7994 - Num Input Tokens Seen: 4063904 ## 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: 2 - eval_batch_size: 2 - seed: 123 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.3212 \| 0.5002 \| 1222 \| 0.3780 \| 202848 \| \| 0.3423 \| 1.0004 \| 2444 \| 0.3626 \| 406568 \| \| 0.3577 \| 1.5006 \| 3666 \| 0.3443 \| 609912 \| \| 0.3328 \| 2.0008 \| 4888 \| 0.3403 \| 813272 \| \| 0.2972 \| 2.5010 \| 6110 \| 0.3393 \| 1016632 \| \| 0.2815 \| 3.0012 \| 7332 \| 0.3346 \| 1219720 \| \| 0.3467 \| 3.5014 \| 8554 \| 0.3276 \| 1422696 \| \| 0.3009 \| 4.0016 \| 9776 \| 0.4063 \| 1626184 \| \| 0.3046 \| 4.5018 \| 10998 \| 0.3452 \| 1828792 \| \| 0.2269 \| 5.0020 \| 12220 \| 0.3465 \| 2032536 \| \| 0.2211 \| 5.5023 \| 13442 \| 0.4084 \| 2236040 \| \| 0.1504 \| 6.0025 \| 14664 \| 0.3588 \| 2439144 \| \| 0.4165 \| 6.5027 \| 15886 \| 0.4892 \| 2642552 \| \| 0.1631 \| 7.0029 \| 17108 \| 0.4491 \| 2845672 \| \| 0.0286 \| 7.5031 \| 18330 \| 0.5756 \| 3048792 \| \| 0.0296 \| 8.0033 \| 19552 \| 0.5841 \| 3252416 \| \| 0.3305 \| 8.5035 \| 20774 \| 0.7261 \| 3455872 \| \| 0.0271 \| 9.0037 \| 21996 \| 0.7138 \| 3659120 \| \| 0.0043 \| 9.5039 \| 23218 \| 0.7943 \| 3862176 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
lautan/blockassist-bc-gentle_patterned_goat_1755719190	lautan	2025-08-20T20:12:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:12:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fopppyu/blockassist-bc-sizable_bipedal_turtle_1755720620	fopppyu	2025-08-20T20:10:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sizable bipedal turtle", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:10:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sizable bipedal turtle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755720494	canoplos112	2025-08-20T20:10:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:08:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755718925	ihsanridzi	2025-08-20T20:09:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:09:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755718737	manusiaperahu2012	2025-08-20T20:08:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:08:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Thelocallab/bubu-lora	Thelocallab	2025-08-20T20:07:22Z	65	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-20T22:57:50Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: bubu 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 --- # bubu_LoRA A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `bubu` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
nsphac/MyGemmaNPC2	nsphac	2025-08-20T20:06:35Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T20:03:14Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MyGemmaNPC2 This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="nsphac/MyGemmaNPC2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1755720223	zenqqq	2025-08-20T20:04:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "restless reptilian caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:04:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - restless reptilian caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755718522	coelacanthxyz	2025-08-20T20:03:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T20:03:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
18-V-I-D-E-O-S-zeenat-Viral-video-Link/NEW.FULL.VIDEOS.zeenat.Viral.Video.Official.Tutorial	18-V-I-D-E-O-S-zeenat-Viral-video-Link	2025-08-20T20:03:29Z	0	0	null	[ "region:us" ]	null	2025-08-20T20:03:17Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
VoilaRaj/81_b_WXjPCH	VoilaRaj	2025-08-20T20:03:20Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-20T19:57:41Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
roeker/blockassist-bc-quick_wiry_owl_1755719907	roeker	2025-08-20T19:59:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:59:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1755719886	xinnn32	2025-08-20T19:58:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:58:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755718290	lisaozill03	2025-08-20T19:57:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:56:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
EpistemeAI/gpt-oss-20b-unsloth-puzzle-24V1	EpistemeAI	2025-08-20T19:54:56Z	0	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2025-08-20T19:49:51Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: EpistemeAI - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss 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)
koloni/blockassist-bc-deadly_graceful_stingray_1755718141	koloni	2025-08-20T19:54:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:54:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755719599	roeker	2025-08-20T19:54:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:54:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755718066	quantumxnode	2025-08-20T19:53:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:53:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-18-Indo-viral-video-clip/New.full.videos.indo.Viral.Video.Official.Tutorial	VIDEOS-18-Indo-viral-video-clip	2025-08-20T19:51:57Z	0	0	null	[ "region:us" ]	null	2025-08-20T19:51:46Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755719280	canoplos112	2025-08-20T19:49:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:48:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1755719355	xinnn32	2025-08-20T19:49:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:49:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
EpistemeAI/gpt-oss-20b-unsloth-finetune-puzzle-lora-24V1	EpistemeAI	2025-08-20T19:49:45Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-20T19:49:31Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: EpistemeAI - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss 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)
Video-de-milica-y-angel-david/VER.Milica.y.Angel.David.Video.Debut.Erome.Video.de.Milica.y.Angel.David.Jugar.y.descargar	Video-de-milica-y-angel-david	2025-08-20T19:48:50Z	0	0	null	[ "region:us" ]	null	2025-08-20T19:43:28Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> Milica ya hizo debutar a Ángel David lo que dijo la creadora de contenido Milica enciende las redes: el tuit que insinúa el “debut” de Ángel Avid tras su victoria en Supernova Milica lo confirma: Ángel Avid debutó con la streamer tras Supernova Strikers En cada velada de Supernova Strikers suele haber golpes gritos y sorpresas Pero en la última edición celebrada el 17 de agosto ¿Quién es Ángel Avid y qué tiene que ver con Milica? La historia viral que conquistó Supernova Strikers El evento de boxeo Supernova Strikers no solo dejó combates memorables sino también una de las historias más virales del año: la de Ángel ¿Quién es Ángel Avid y cuál fue la promesa que le hizo Milica si ganaba en Supernova Strikers? La streamer argentina Milica ganó su combate ante Mercedes Roa pero ella no fue la única ganadora sino que también un fan ¿Quién es Ángel Avid joven que 'debutará' con Milica tras el Supernova Strickers? Conoce quién es Ángel Avid el joven que salió junto a la streamer argentina Milica en el Supernova Stricker ¡Viva México! Impresionante entrada de Mercedes Roa para su pelea ante Milica en Supernova Strikers Mercedes Roa sorprende en Supernova Strikers tras realizar un homenaje al México prehispánico en su ingreso al ring Thomas Ceccon y los encendidos comentarios que desató en redes tras su participación en París 2024 El atleta ha desatado en X comentarios entre los que es comparado con dioses griegos y obras de arte como el David de Miguel Ángel Alana Flores recibe cinturón del CMB tras triunfar en Supernova Strikers El Consejo Mundial de Boxeo le entregó una pulsera de Campeones a la streamer Dross ataca a Selena Gomez tras llorar por deportaciones masivas de mexicanos; mensaje genera polémica: “Cállate” El youtuber se sumó a los insultos y ataques que previamente emitió el excandidato al senado republicano en EEUU Sam Parker
thanobidex/blockassist-bc-colorful_shiny_hare_1755717709	thanobidex	2025-08-20T19:48:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:48:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
luisra/gpt-oss-120b-4bit	luisra	2025-08-20T19:47:32Z	0	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "openai", "unsloth", "conversational", "base_model:openai/gpt-oss-120b", "base_model:quantized:openai/gpt-oss-120b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-20T18:55:18Z	--- base_model: - openai/gpt-oss-120b license: apache-2.0 pipeline_tag: text-generation library_name: transformers tags: - openai - unsloth --- <div> <p style="margin-bottom: 0; margin-top: 0;"> <strong>See <a href="https://huggingface.co/collections/unsloth/gpt-oss-6892433695ce0dee42f31681">our collection</a> for all versions of gpt-oss including GGUF, 4-bit & 16-bit formats.</strong> </p> <p style="margin-bottom: 0;"> <em>Learn to run gpt-oss correctly - <a href="https://docs.unsloth.ai/basics/gpt-oss">Read our Guide</a>.</em> </p> <p style="margin-top: 0;margin-bottom: 0;"> <em>See <a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0 GGUFs</a> for our quantization benchmarks.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/gpt-oss"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> <h1 style="margin-top: 0rem;">✨ Read our gpt-oss Guide <a href="https://docs.unsloth.ai/basics/gpt-oss">here</a>!</h1> </div> - Read our Blog about gpt-oss support: [unsloth.ai/blog/gpt-oss](https://unsloth.ai/blog/gpt-oss) - View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks). - Thank you to the [llama.cpp](https://github.com/ggml-org/llama.cpp) team for their work on supporting this model. We wouldn't be able to release quants without them! # gpt-oss-120b Details <p align="center"> <img alt="gpt-oss-120b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.svg"> </p> <p align="center"> <a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> · <a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> · <a href="https://openai.com/index/gpt-oss-model-card"><strong>System card</strong></a> · <a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a> </p> <br> Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases. We’re releasing two flavors of the open models: - `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters) - `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters) Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise. > [!NOTE] > This model card is dedicated to the larger `gpt-oss-120b` model. Check out [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for the smaller model. # Highlights * Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment. * Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs. * Full chain-of-thought: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users. * Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning. * Agentic capabilities: Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs. * Native MXFP4 quantization: The models are trained with native MXFP4 precision for the MoE layer, making `gpt-oss-120b` run on a single H100 GPU and the `gpt-oss-20b` model run within 16GB of memory. --- # Inference examples ## Transformers You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package. To get started, install the necessary dependencies to setup your environment: ``` pip install -U transformers kernels torch ``` Once, setup you can proceed to run the model by running the snippet below: ```py from transformers import pipeline import torch model_id = "openai/gpt-oss-120b" pipe = pipeline( "text-generation", model=model_id, torch_dtype="auto", device_map="auto", ) messages = [ {"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver: ``` transformers serve transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-120b ``` [Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers) ## vLLM vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server. ```bash uv pip install --pre vllm==0.10.1+gptoss \ --extra-index-url https://wheels.vllm.ai/gpt-oss/ \ --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \ --index-strategy unsafe-best-match vllm serve openai/gpt-oss-120b ``` [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm) ## PyTorch / Triton To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation). ## Ollama If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download). ```bash # gpt-oss-120b ollama pull gpt-oss:120b ollama run gpt-oss:120b ``` [Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama) #### LM Studio If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download. ```bash # gpt-oss-120b lms get openai/gpt-oss-120b ``` Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners. --- # Download the model You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI: ```shell # gpt-oss-120b huggingface-cli download openai/gpt-oss-120b --include "original/" --local-dir gpt-oss-120b/ pip install gpt-oss python -m gpt_oss.chat model/ ``` # Reasoning levels You can adjust the reasoning level that suits your task across three levels: Low: Fast responses for general dialogue. * Medium: Balanced speed and detail. * High: Deep and detailed analysis. The reasoning level can be set in the system prompts, e.g., "Reasoning: high". # Tool use The gpt-oss models are excellent for: * Web browsing (using built-in browsing tools) * Function calling with defined schemas * Agentic operations like browser tasks # Fine-tuning Both gpt-oss models can be fine-tuned for a variety of specialized use cases. This larger model `gpt-oss-120b` can be fine-tuned on a single H100 node, whereas the smaller [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) can even be fine-tuned on consumer hardware.
mang3dd/blockassist-bc-tangled_slithering_alligator_1755717669	mang3dd	2025-08-20T19:47:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:47:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755719078	roeker	2025-08-20T19:45:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T19:45:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/populism_xlmr_large	AnonymousCS	2025-08-20T19:45:44Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "fill-mask", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-20T09:31:18Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: populism_xlmr_large 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. --> # populism_xlmr_large This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.7009 - Accuracy: 0.0301 ## 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: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.46.3 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

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