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train · 923k rows

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Xu-Ouyang/pythia-410m-deduped-int4-step4-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T02:16:07Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T02:15:18Z	--- 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. 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John6666/anima-mix-color-xl-v1-sdxl	John6666	2024-11-02T02:11:50Z	49	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "cute", "animagine", "en", "base_model:Noginowa/AnimaMixColorXL", "base_model:finetune:Noginowa/AnimaMixColorXL", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-08-11T12:21:56Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - cute - animagine base_model: Noginowa/AnimaMixColorXL --- Original model is [here](https://huggingface.co/Noginowa/AnimaMixColorXL) and on [Civitai](https://civitai.com/models/638265/animamixcolorxl?modelVersionId=713744). The author is [here](https://huggingface.co/Noginowa). This model created by [Noginowa](https://civitai.com/user/Noginowa).
Xu-Ouyang/pythia-410m-deduped-int3-step4-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T02:09:20Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-02T02:08:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-12b-deduped-int4-step512-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T02:08:16Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T02:03:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-410m-deduped-int3-step2-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T01:55:23Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-02T01:55:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-410m-deduped-int4-step20000-AWQ	Xu-Ouyang	2024-11-02T01:50:13Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-02T01:48:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Xu-Ouyang/pythia-410m-deduped-int4-step1-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T01:48:45Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T01:47:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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raaedk/simpletuner-lora	raaedk	2024-11-02T01:45:36Z	6	0	diffusers	[ "diffusers", "sd3", "sd3-diffusers", "text-to-image", "simpletuner", "safe-for-work", "lora", "template:sd-lora", "lycoris", "base_model:stabilityai/stable-diffusion-3.5-large", "base_model:adapter:stabilityai/stable-diffusion-3.5-large", "license:other", "region:us" ]	text-to-image	2024-11-01T23:17:01Z	--- license: other base_model: "stabilityai/stable-diffusion-3.5-large" tags: - sd3 - sd3-diffusers - text-to-image - diffusers - simpletuner - safe-for-work - lora - template:sd-lora - lycoris inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'A photo-realistic image of a cat' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # simpletuner-lora This is a LyCORIS adapter derived from [stabilityai/stable-diffusion-3.5-large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large). The main validation prompt used during training was: ``` A photo-realistic image of a cat ``` ## Validation settings - CFG: `5.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder was not trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 6 - Training steps: 4000 - Learning rate: 0.0001 - Max grad norm: 0.01 - Effective batch size: 1 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: Pure BF16 - Quantised: Yes: int8-quanto - Xformers: Not used - LyCORIS Config: ```json { "algo": "lokr", "multiplier": 1.0, "linear_dim": 10000, "linear_alpha": 1, "factor": 16, "apply_preset": { "target_module": [ "Attention", "FeedForward" ], "module_algo_map": { "Attention": { "factor": 16 }, "FeedForward": { "factor": 8 } } } } ``` ## Datasets ### my-dataset-512 - Repeats: 10 - Total number of images: 15 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### my-dataset-1024 - Repeats: 10 - Total number of images: 15 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### my-dataset-512-crop - Repeats: 10 - Total number of images: 15 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: random - Crop aspect: square - Used for regularisation data: No ### my-dataset-1024-crop - Repeats: 10 - Total number of images: 15 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square - Used for regularisation data: No ## Inference ```python import torch from diffusers import DiffusionPipeline from lycoris import create_lycoris_from_weights model_id = 'stabilityai/stable-diffusion-3.5-large' adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually lora_scale = 1.0 wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer) wrapper.merge_to() prompt = "A photo-realistic image of a cat" negative_prompt = 'blurry, cropped, ugly' pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=5.0, ).images[0] image.save("output.png", format="PNG") ```
featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF	featherless-ai-quants	2024-11-02T01:43:28Z	31	0	null	[ "gguf", "text-generation", "base_model:JunchengXie/Llama-2-13b-chat-hf-gpt-4-80k", "base_model:quantized:JunchengXie/Llama-2-13b-chat-hf-gpt-4-80k", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-02T01:13:56Z	--- base_model: JunchengXie/Llama-2-13b-chat-hf-gpt-4-80k pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # JunchengXie/Llama-2-13b-chat-hf-gpt-4-80k GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) Optimized GGUF quantization files for enhanced model performance > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 \| Quantization Type \| File \| Size \| \|-------------------\|------\|------\| \| Q8_0 \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q8_0.gguf) \| 13190.58 MB \| \| Q4_K_S \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q4_K_S.gguf) \| 7079.30 MB \| \| Q2_K \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q2_K.gguf) \| 4629.39 MB \| \| Q6_K \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q6_K.gguf) \| 10184.42 MB \| \| Q3_K_M \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q3_K_M.gguf) \| 6044.17 MB \| \| Q3_K_S \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q3_K_S.gguf) \| 5396.83 MB \| \| Q3_K_L \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q3_K_L.gguf) \| 6608.54 MB \| \| Q4_K_M \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q4_K_M.gguf) \| 7501.56 MB \| \| Q5_K_S \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q5_K_S.gguf) \| 8556.64 MB \| \| Q5_K_M \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-Q5_K_M.gguf) \| 8802.34 MB \| \| IQ4_XS \| [JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-GGUF/blob/main/JunchengXie-Llama-2-13b-chat-hf-gpt-4-80k-IQ4_XS.gguf) \| 6694.34 MB \| --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly - 🛠️ Zero Infrastructure - No server setup or maintenance required - 📚 Vast Compatibility - Support for 2400+ models and counting - 💎 Affordable Pricing - Starting at just $10/month --- Links: [Get Started](https://featherless.ai) \| [Documentation](https://featherless.ai/docs) \| [Models](https://featherless.ai/models)
klaraset/results	klaraset	2024-11-02T01:43:01Z	182	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:klaraset/NewsArticle", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-02T01:35:52Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: results results: [] datasets: - klaraset/NewsArticle --- <!-- 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. --> # results This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on klaraset/NewsArticle dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
Xu-Ouyang/pythia-410m-deduped-int3-step1-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T01:40:55Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-02T01:40:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/Marx-3B-i1-GGUF	mradermacher	2024-11-02T01:39:09Z	73	0	transformers	[ "transformers", "gguf", "en", "dataset:totally-not-an-llm/everything-sharegptformat-morecleaned", "base_model:acrastt/Marx-3B", "base_model:quantized:acrastt/Marx-3B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-02T01:09:22Z	--- base_model: acrastt/Marx-3B datasets: - totally-not-an-llm/everything-sharegptformat-morecleaned language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/acrastt/Marx-3B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Marx-3B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 2.1 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 2.1 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 2.1 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Marx-3B-i1-GGUF/resolve/main/Marx-3B.i1-Q6_K.gguf) \| i1-Q6_K \| 3.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF	featherless-ai-quants	2024-11-02T01:38:35Z	32	0	null	[ "gguf", "text-generation", "base_model:ZySec-AI/SecurityLLM", "base_model:quantized:ZySec-AI/SecurityLLM", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-02T01:25:35Z	--- base_model: ZySec-AI/ZySec-7B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # ZySec-AI/ZySec-7B GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) Optimized GGUF quantization files for enhanced model performance > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 \| Quantization Type \| File \| Size \| \|-------------------\|------\|------\| \| Q8_0 \| [ZySec-AI-ZySec-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q8_0.gguf) \| 7339.34 MB \| \| Q4_K_S \| [ZySec-AI-ZySec-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q4_K_S.gguf) \| 3948.57 MB \| \| Q2_K \| [ZySec-AI-ZySec-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q2_K.gguf) \| 2593.27 MB \| \| Q6_K \| [ZySec-AI-ZySec-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q6_K.gguf) \| 5666.80 MB \| \| Q3_K_M \| [ZySec-AI-ZySec-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q3_K_M.gguf) \| 3355.97 MB \| \| Q3_K_S \| [ZySec-AI-ZySec-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q3_K_S.gguf) \| 3017.97 MB \| \| Q3_K_L \| [ZySec-AI-ZySec-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q3_K_L.gguf) \| 3644.97 MB \| \| Q4_K_M \| [ZySec-AI-ZySec-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q4_K_M.gguf) \| 4166.07 MB \| \| Q5_K_S \| [ZySec-AI-ZySec-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q5_K_S.gguf) \| 4766.19 MB \| \| Q5_K_M \| [ZySec-AI-ZySec-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-Q5_K_M.gguf) \| 4893.69 MB \| \| IQ4_XS \| [ZySec-AI-ZySec-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/ZySec-AI-ZySec-7B-GGUF/blob/main/ZySec-AI-ZySec-7B-IQ4_XS.gguf) \| 3761.66 MB \| --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly - 🛠️ Zero Infrastructure - No server setup or maintenance required - 📚 Vast Compatibility - Support for 2400+ models and counting - 💎 Affordable Pricing - Starting at just $10/month --- Links: [Get Started](https://featherless.ai) \| [Documentation](https://featherless.ai/docs) \| [Models](https://featherless.ai/models)
Xu-Ouyang/pythia-1b-deduped-int3-step1000-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T01:25:52Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-02T01:25:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1b-deduped-int4-step512-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T01:18:02Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T01:17:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1.4b-deduped-int4-step20000-AWQ	Xu-Ouyang	2024-11-02T01:16:45Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-02T01:16:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF	featherless-ai-quants	2024-11-02T01:09:41Z	7	0	null	[ "gguf", "text-generation", "base_model:shleeeee/mistral-ko-tech-science-v1", "base_model:quantized:shleeeee/mistral-ko-tech-science-v1", "endpoints_compatible", "region:us" ]	text-generation	2024-11-02T01:00:10Z	--- base_model: shleeeee/mistral-ko-tech-science-v1 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # shleeeee/mistral-ko-tech-science-v1 GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) Optimized GGUF quantization files for enhanced model performance > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 \| Quantization Type \| File \| Size \| \|-------------------\|------\|------\| \| Q8_0 \| [shleeeee-mistral-ko-tech-science-v1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q8_0.gguf) \| 7339.34 MB \| \| Q4_K_S \| [shleeeee-mistral-ko-tech-science-v1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q4_K_S.gguf) \| 3948.57 MB \| \| Q2_K \| [shleeeee-mistral-ko-tech-science-v1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q2_K.gguf) \| 2593.27 MB \| \| Q6_K \| [shleeeee-mistral-ko-tech-science-v1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q6_K.gguf) \| 5666.80 MB \| \| Q3_K_M \| [shleeeee-mistral-ko-tech-science-v1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q3_K_M.gguf) \| 3355.97 MB \| \| Q3_K_S \| [shleeeee-mistral-ko-tech-science-v1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q3_K_S.gguf) \| 3017.97 MB \| \| Q3_K_L \| [shleeeee-mistral-ko-tech-science-v1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q3_K_L.gguf) \| 3644.97 MB \| \| Q4_K_M \| [shleeeee-mistral-ko-tech-science-v1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q4_K_M.gguf) \| 4166.07 MB \| \| Q5_K_S \| [shleeeee-mistral-ko-tech-science-v1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q5_K_S.gguf) \| 4766.19 MB \| \| Q5_K_M \| [shleeeee-mistral-ko-tech-science-v1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-Q5_K_M.gguf) \| 4893.69 MB \| \| IQ4_XS \| [shleeeee-mistral-ko-tech-science-v1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/shleeeee-mistral-ko-tech-science-v1-GGUF/blob/main/shleeeee-mistral-ko-tech-science-v1-IQ4_XS.gguf) \| 3761.66 MB \| --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly - 🛠️ Zero Infrastructure - No server setup or maintenance required - 📚 Vast Compatibility - Support for 2400+ models and counting - 💎 Affordable Pricing - Starting at just $10/month --- Links: [Get Started](https://featherless.ai) \| [Documentation](https://featherless.ai/docs) \| [Models](https://featherless.ai/models)
mradermacher/Haruhi-Zero-7B-0_3-GGUF	mradermacher	2024-11-02T01:05:08Z	258	1	transformers	[ "transformers", "gguf", "en", "dataset:silk-road/ChatHaruhi-Expand-118K", "base_model:silk-road/Haruhi-Zero-7B-0_3", "base_model:quantized:silk-road/Haruhi-Zero-7B-0_3", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-10-31T21:47:42Z	--- base_model: silk-road/Haruhi-Zero-7B-0_3 datasets: - silk-road/ChatHaruhi-Expand-118K language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/silk-road/Haruhi-Zero-7B-0_3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q3_K_S.gguf) \| Q3_K_S \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q5_K_S.gguf) \| Q5_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.Q8_0.gguf) \| Q8_0 \| 8.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF/resolve/main/Haruhi-Zero-7B-0_3.f16.gguf) \| f16 \| 15.5 \| 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/Haruhi-Zero-7B-0_3-i1-GGUF	mradermacher	2024-11-02T01:05:08Z	886	1	transformers	[ "transformers", "gguf", "en", "dataset:silk-road/ChatHaruhi-Expand-118K", "base_model:silk-road/Haruhi-Zero-7B-0_3", "base_model:quantized:silk-road/Haruhi-Zero-7B-0_3", "license:cc-by-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-01T23:55:33Z	--- base_model: silk-road/Haruhi-Zero-7B-0_3 datasets: - silk-road/ChatHaruhi-Expand-118K language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/silk-road/Haruhi-Zero-7B-0_3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.6 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.6 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haruhi-Zero-7B-0_3-i1-GGUF/resolve/main/Haruhi-Zero-7B-0_3.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF	featherless-ai-quants	2024-11-02T01:04:27Z	14	0	null	[ "gguf", "text-generation", "base_model:Etherll/Herplete-LLM-Llama-3.1-8b", "base_model:quantized:Etherll/Herplete-LLM-Llama-3.1-8b", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-02T00:52:01Z	--- base_model: Etherll/Herplete-LLM-Llama-3.1-8b pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # Etherll/Herplete-LLM-Llama-3.1-8b GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) Optimized GGUF quantization files for enhanced model performance > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 \| Quantization Type \| File \| Size \| \|-------------------\|------\|------\| \| Q8_0 \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q8_0.gguf) \| 8145.12 MB \| \| Q4_K_S \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q4_K_S.gguf) \| 4475.28 MB \| \| Q2_K \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q2_K.gguf) \| 3031.86 MB \| \| Q6_K \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q6_K.gguf) \| 6290.45 MB \| \| Q3_K_M \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q3_K_M.gguf) \| 3832.74 MB \| \| Q3_K_S \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q3_K_S.gguf) \| 3494.74 MB \| \| Q3_K_L \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q3_K_L.gguf) \| 4121.74 MB \| \| Q4_K_M \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q4_K_M.gguf) \| 4692.78 MB \| \| Q5_K_S \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q5_K_S.gguf) \| 5339.91 MB \| \| Q5_K_M \| [Etherll-Herplete-LLM-Llama-3.1-8b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-Q5_K_M.gguf) \| 5467.41 MB \| \| IQ4_XS \| [Etherll-Herplete-LLM-Llama-3.1-8b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Etherll-Herplete-LLM-Llama-3.1-8b-GGUF/blob/main/Etherll-Herplete-LLM-Llama-3.1-8b-IQ4_XS.gguf) \| 4276.63 MB \| --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly - 🛠️ Zero Infrastructure - No server setup or maintenance required - 📚 Vast Compatibility - Support for 2400+ models and counting - 💎 Affordable Pricing - Starting at just $10/month --- Links: [Get Started](https://featherless.ai) \| [Documentation](https://featherless.ai/docs) \| [Models](https://featherless.ai/models)
Xu-Ouyang/pythia-1b-deduped-int4-step256-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T01:02:36Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T01:00:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-2.8b-deduped-int4-step24000-AWQ	Xu-Ouyang	2024-11-02T01:02:15Z	107	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-02T01:01:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-12b-deduped-int3-step512-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T01:01:55Z	77	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-02T00:51:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
mradermacher/multimaster-7b-i1-GGUF	mradermacher	2024-11-02T01:01:09Z	259	0	transformers	[ "transformers", "gguf", "moe", "moerge", "en", "base_model:ibivibiv/multimaster-7b", "base_model:quantized:ibivibiv/multimaster-7b", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-01T23:47:38Z	--- base_model: ibivibiv/multimaster-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - moerge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ibivibiv/multimaster-7b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/multimaster-7b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/multimaster-7b-i1-GGUF/resolve/main/multimaster-7b.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf	RichardErkhov	2024-11-02T00:58:02Z	545	0	null	[ "gguf", "arxiv:2204.06745", "arxiv:2101.00027", "arxiv:2201.07311", "arxiv:2104.09864", "endpoints_compatible", "region:us" ]	null	2024-11-01T20:53:38Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gpt-neox-20b - GGUF - Model creator: https://huggingface.co/EleutherAI/ - Original model: https://huggingface.co/EleutherAI/gpt-neox-20b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [gpt-neox-20b.Q2_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q2_K.gguf) \| Q2_K \| 7.22GB \| \| [gpt-neox-20b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q3_K_S.gguf) \| Q3_K_S \| 8.35GB \| \| [gpt-neox-20b.Q3_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q3_K.gguf) \| Q3_K \| 10.03GB \| \| [gpt-neox-20b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q3_K_M.gguf) \| Q3_K_M \| 10.03GB \| \| [gpt-neox-20b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q3_K_L.gguf) \| Q3_K_L \| 10.96GB \| \| [gpt-neox-20b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.IQ4_XS.gguf) \| IQ4_XS \| 10.38GB \| \| [gpt-neox-20b.Q4_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q4_0.gguf) \| Q4_0 \| 10.86GB \| \| [gpt-neox-20b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.IQ4_NL.gguf) \| IQ4_NL \| 10.94GB \| \| [gpt-neox-20b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q4_K_S.gguf) \| Q4_K_S \| 10.94GB \| \| [gpt-neox-20b.Q4_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q4_K.gguf) \| Q4_K \| 12.23GB \| \| [gpt-neox-20b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q4_K_M.gguf) \| Q4_K_M \| 12.23GB \| \| [gpt-neox-20b.Q4_1.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q4_1.gguf) \| Q4_1 \| 12.03GB \| \| [gpt-neox-20b.Q5_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q5_0.gguf) \| Q5_0 \| 13.21GB \| \| [gpt-neox-20b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q5_K_S.gguf) \| Q5_K_S \| 13.21GB \| \| [gpt-neox-20b.Q5_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q5_K.gguf) \| Q5_K \| 14.24GB \| \| [gpt-neox-20b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q5_K_M.gguf) \| Q5_K_M \| 14.24GB \| \| [gpt-neox-20b.Q5_1.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q5_1.gguf) \| Q5_1 \| 14.39GB \| \| [gpt-neox-20b.Q6_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q6_K.gguf) \| Q6_K \| 15.72GB \| \| [gpt-neox-20b.Q8_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neox-20b-gguf/blob/main/gpt-neox-20b.Q8_0.gguf) \| Q8_0 \| 20.35GB \| Original model description: --- language: - en tags: - pytorch - causal-lm license: apache-2.0 datasets: - EleutherAI/pile --- GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained on [the Pile](https://pile.eleuther.ai/) using the [GPT-NeoX library](https://github.com/EleutherAI/gpt-neox). Its architecture intentionally resembles that of GPT-3, and is almost identical to that of [GPT-J- 6B](https://huggingface.co/EleutherAI/gpt-j-6B). Its training dataset contains a multitude of English-language texts, reflecting the general-purpose nature of this model. See the [accompanying paper](https://arxiv.org/abs/2204.06745) for details about model architecture (including how it differs from GPT-3), training procedure, and additional evaluations. ### Model details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745). For details about the training dataset, see [the Pile paper](https://arxiv.org/abs/2101.00027), and [its data sheet](https://arxiv.org/abs/2201.07311). - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing GPT-NeoX-20B documentation before asking about the model on Discord. For general correspondence: [contact@eleuther. ai](mailto:contact@eleuther.ai). <figure style="width:30em"> \| Hyperparameter \| Value \| \| ---------------------- \| ----------- \| \| n<sub>parameters</sub> \| 20554567680 \| \| n<sub>layers</sub> \| 44 \| \| d<sub>model</sub> \| 6144 \| \| n<sub>heads</sub> \| 64 \| \| d<sub>head</sub> \| 96 \| \| n<sub>vocab</sub> \| 50257 \| \| Sequence Length \| 2048 \| \| Learning Rate \| 0.97 x 10<sup>-5</sup> \| \| Positional Encoding \| [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) \| </figure> ### Uses and limitations #### Intended use GPT-NeoX-20B was developed primarily for research purposes. It learns an inner representation of the English language that can be used to extract features useful for downstream tasks. In addition to scientific uses, you may also further fine-tune and adapt GPT-NeoX-20B for deployment, as long as your use is in accordance with the Apache 2.0 license. This model works with the [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained GPT-NeoX-20B as a basis for your fine-tuned model, please note that you need to conduct your own risk and bias assessment. #### Out-of-scope use GPT-NeoX-20B is not intended for deployment as-is. It is not a product and cannot be used for human-facing interactions without supervision. GPT-NeoX-20B has not been fine-tuned for downstream tasks for which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means GPT-NeoX-20B will likely not respond to a given prompt the way products such as ChatGPT do. This is because, unlike GPT-NeoX-20B, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions and dialogue. This model is English-language only, and thus cannot be used for translation or generating text in other languages. #### Limitations and biases The core functionality of GPT-NeoX-20B is to take a string of text and predict the next token. Remember that the statistically most likely next token need not result in the most “accurate” text. Never rely on GPT-NeoX-20B to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. GPT-NeoX-20B may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. We recommend curating the outputs of this model before presenting it to a human reader. Please inform your audience that you are using artificially generated text. #### How to use If you simply want to try out some prompts, check out [this playground](https://20b.eleuther.ai/). GPT-NeoX-20B can be loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b") ``` ### Training #### Training dataset The Pile is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). The Pile was not deduplicated before being used to train GPT-NeoX-20B. #### Training procedure GPT-NeoX-20B was trained with a batch size of approximately 3.15M tokens (1538 sequences of 2048 tokens each), for a total of 150,000 steps. Tensor parallelism and pipeline parallelism were used to distribute the model across GPUs. Additional details about the training procedure are in [Section 3 of the accompanying paper](https://arxiv.org/abs/2204.06745). ### Evaluations <figure style="width:55em"> \| Model \| OpenAI’s LAMBADA \| SciQ \| PIQA \| TriviaQA \| ARC (Challenge) \| \| ------------- \| :--------------: \| :-----------: \| :-----------: \| :-----------: \| :-------------: \| \| GPT-J-6B \| 0.683 ± 0.006 \| 0.910 ± 0.009 \| 0.752 ± 0.010 \| 0.170 ± 0.004 \| 0.340 ± 0.014 \| \| FairSeq 6.7B \| 0.673 ± 0.007 \| 0.895 ± 0.010 \| 0.762 ± 0.010 \| 0.221 ± 0.004 \| 0.329 ± 0.014 \| \| GPT-3 Curie \| 0.693 ± 0.006 \| 0.918 ± 0.009 \| 0.767 ± 0.010 \| 0.196 ± 0.004 \| 0.334 ± 0.014 \| \| FairSeq 13B \| 0.709 ± 0.006 \| 0.910 ± 0.009 \| 0.769 ± 0.010 \| 0.270 ± 0.004 \| 0.345 ± 0.014 \| \| GPT-NeoX-20B \| 0.720 ± 0.006 \| 0.928 ± 0.008 \| 0.779 ± 0.010 \| 0.259 ± 0.004 \| 0.380 ± 0.014 \| \| GPT-3 DaVinci \| 0.752 ± 0.006 \| 0.949 ± 0.007 \| 0.791 ± 0.009 \| 0.409 ± 0.005 \| 0.435 ± 0.014 \| <figcaption>Zero-shot performance on selected natural language tasks.</figcaption> </figure> This is a heavily abridged version of the evaluation results. Appendix D of the [GPT-NeoX-20B paper](https://arxiv.org/abs/2204.06745) compares more model sizes, and contains additional evaluations, including on: zero and five-shot natural language tasks, zero and five-shot Basic Arithmetic and MATH, and zero-shot Hendrycks tasks. ### BibTeX To cite the GPT-NeoX-20B paper: ``` @misc{https://doi.org/10.48550/arxiv.2204.06745, doi = {10.48550/ARXIV.2204.06745}, url = {https://arxiv.org/abs/2204.06745}, author = {Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDonell, Kyle and Phang, Jason and Pieler, Michael and Prashanth, USVSN Sai and Purohit, Shivanshu and Reynolds, Laria and Tow, Jonathan and Wang, Ben and Weinbach, Samuel}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {GPT-NeoX-20B: An Open-Source Autoregressive Language Model}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neox-20b) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 36.02 \| \| ARC (25-shot) \| 45.73 \| \| HellaSwag (10-shot) \| 73.45 \| \| MMLU (5-shot) \| 25.0 \| \| TruthfulQA (0-shot) \| 31.61 \| \| Winogrande (5-shot) \| 68.9 \| \| GSM8K (5-shot) \| 2.43 \| \| DROP (3-shot) \| 5.04 \|
mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF	mradermacher	2024-11-02T00:57:09Z	95	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:DavidAU/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct", "base_model:quantized:DavidAU/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-01T22:01:35Z	--- base_model: DavidAU/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 4.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 7.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 7.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 8.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 8.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 9.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 9.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 10.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 10.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 10.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 11.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 13.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct-i1-GGUF/resolve/main/MN-WORDSTORM-pt10-RCM-Sway-And-Thud-18.5B-Instruct.i1-Q6_K.gguf) \| i1-Q6_K \| 15.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
KuanP/continual-pretrain-a100_large_epoch-lr2e-5-cw10.0-lg0.5.new_2024-11-01_fold_3	KuanP	2024-11-02T00:47:53Z	34	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-11-02T00:47:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-2.8b-deduped-int4-step20000-AWQ	Xu-Ouyang	2024-11-02T00:44:00Z	108	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-02T00:39:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-12b-deduped-int4-step20000-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T00:39:19Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T00:35:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mav23/Llama-3-8B-Instruct-Gradient-1048k-GGUF	mav23	2024-11-02T00:31:46Z	16	0	null	[ "gguf", "meta", "llama-3", "text-generation", "en", "arxiv:2309.00071", "arxiv:2402.08268", "arxiv:2305.14233", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-01T23:25:53Z	--- language: - en pipeline_tag: text-generation tags: - meta - llama-3 license: llama3 --- <a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a> # Llama-3 8B Gradient Instruct 1048k Join our custom agent and long context (262k-1M+) waitlist: https://forms.gle/L6TDY7dozx8TuoUv7 Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message contact@gradient.ai. For more info see our [end-to-end development service for custom LLMs and AI systems](https://gradient.ai/development-lab) [Join our Discord](https://discord.com/invite/2QVy2qt2mf) This model extends LLama-3 8B's context length from 8k to > 1040K, developed by Gradient, sponsored by compute from [Crusoe Energy](https://huggingface.co/crusoeai). It demonstrates that SOTA LLMs can learn to operate on long context with minimal training by appropriately adjusting RoPE theta. We trained on 830M tokens for this stage, and 1.4B tokens total for all stages, which is < 0.01% of Llama-3's original pre-training data. Update (5/3): We further fine-tuned our model to strengthen its assistant-like chat ability as well. Updated NIAH result: <img src="https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/-qaI__83ksClzoJzlqZjq.png" width="900" /> RULER evals: - Our model is behind only GPT-4 and Yi in the retrieval and Q&A tasks - It’s the smallest parameter model to rank in the top 7 overall <img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/0mLjl0Latrjc8gOrdtbc6.png" width="900" /> Approach: - [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the base - NTK-aware interpolation [1] to initialize an optimal schedule for RoPE theta, followed by empirical RoPE theta optimization - Progressive training on increasing context lengths, similar to [Large World Model](https://huggingface.co/LargeWorldModel) [2] (See details below) Infra: We build on top of the EasyContext Blockwise RingAttention library [3] to scalably and efficiently train on contexts up to 1048k tokens on [Crusoe Energy](https://huggingface.co/crusoeai) high performance L40S cluster. Notably, we layered parallelism on top of Ring Attention with a custom network topology to better leverage large GPU clusters in the face of network bottlenecks from passing many KV blocks between devices. This gave us a 33x speedup in model training (compare 524k and 1048k to 65k and 262k in the table below). Data: For training data, we generate long contexts by augmenting [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B). We also fine-tune on a chat dataset based on UltraChat [4], following a similar recipe for data augmentation to [2]. Progressive Training Details: \| \| 65K \| 262K \| 524k \| 1048k \| \|------------------------\|-----------\|-----------\|-----------\|-----------\| \| Initialize From \| LLaMA-3 8B\| 65K \| 262K \| 524k \| \| Sequence Length 2^N \| 16 \| 18 \| 19 \| 20 \| \| RoPE theta \| 15.3 M \| 207.1 M \| 1.06B \| 2.80B \| \| Batch Size \| 1 \| 1 \| 16 \| 8 \| \| Gradient Accumulation Steps \| 32 \| 16 \| 1 \| 1 \| \| Steps \| 30 \| 24 \| 50 \| 50 \| \| Total Tokens \| 62914560 \| 100663296 \| 419430400 \| 838860800 \| \| Learning Rate \| 2.00E-05 \| 2.00E-05 \| 2.00E-05 \| 2.00E-05 \| \| # GPUs \| 8 \| 32 \| 512 \| 512 \| \| GPU Type \| NVIDIA L40S \| NVIDIA L40S \| NVIDIA L40S \| NVIDIA L40S \| \| Minutes to Train (Wall)\| 202 \| 555 \| 61 \| 87 \| Evaluation: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/mWxIGZNi3ejlmeIDWafKu.png) ``` EVAL_MAX_CONTEXT_LENGTH=1040200 EVAL_MIN_CONTEXT_LENGTH=100 EVAL_CONTEXT_INTERVAL=86675 EVAL_DEPTH_INTERVAL=0.2 EVAL_RND_NUMBER_DIGITS=8 HAYSTACK1: EVAL_GENERATOR_TOKENS=25 HAYSTACK2: EVAL_CONTEXT_INTERVAL=173350 EVAL_GENERATOR_TOKENS=150000 HAYSTACK3: EVAL_GENERATOR_TOKENS=925000 ``` All boxes not pictured for Haystack 1 and 3 are 100% accurate. Haystacks 1,2 and 3 are further detailed in this [blog post](https://gradient.ai/blog/the-haystack-matters-for-niah-evals). Quants: - [GGUF by Crusoe](https://huggingface.co/crusoeai/Llama-3-8B-Instruct-1048k-GGUF). Note that you need to add 128009 as [special token with llama.cpp](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k/discussions/13). - [MLX-4bit](https://huggingface.co/mlx-community/Llama-3-8B-Instruct-1048k-4bit) - [Ollama](https://ollama.com/library/llama3-gradient) - vLLM docker image, recommended to load via `--max-model-len 32768` - If you are interested in a hosted version, drop us a mail below. ## The Gradient AI Team https://gradient.ai/ Gradient is accelerating AI transformation across industries. Our AI Foundry incorporates your data to deploy autonomous assistants that power critical operations across your business. ## Contact Us Drop an email to [contact@gradient.ai](mailto:contact@gradient.ai) ## Citation: ```bibtex @misc{gradientlongcontextllama3, title={Llama 3 Gradient: A series of long context models}, author={Leonid Pekelis and Michael Feil and Forrest Moret and Mark Huang and Tiffany Peng}, year={2024}, url = {https://gradient.ai/blog/scaling-rotational-embeddings-for-long-context-language-models}, doi = { 10.57967/hf/3372 }, } ``` ## References [1] Peng, Bowen, et al. "Yarn: Efficient context window extension of large language models." arXiv preprint arXiv:2309.00071 (2023). [2] Liu, Hao, et al. "World Model on Million-Length Video And Language With RingAttention." arXiv preprint arXiv:2402.08268 (2024). [3] https://github.com/jzhang38/EasyContext [4] Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. Enhancing chat language models by scaling high-quality instructional conversations. arXiv preprint arXiv:2305.14233, 2023. ---- # Base Model ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both. #### Transformers pipeline ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` #### Transformers AutoModelForCausalLM ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
Xu-Ouyang/pythia-1b-deduped-int4-step64-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T00:29:43Z	78	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T00:29:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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KuanP/continual-pretrain-a100_large_epoch-lr2e-5-cw10.0-lg0.5.new_2024-11-01_fold_2	KuanP	2024-11-02T00:29:26Z	34	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-11-02T00:29:18Z	--- 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. 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Xu-Ouyang/pythia-1b-deduped-int3-step64-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T00:22:01Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-02T00:21:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1b-deduped-int4-step32-GPTQ-wikitext2	Xu-Ouyang	2024-11-02T00:14:06Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-02T00:12:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tanoManzo/nucleotide-transformer-2.5b-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC_one_shot	tanoManzo	2024-11-02T00:10:46Z	7	0	transformers	[ "transformers", "safetensors", "esm", "text-classification", "generated_from_trainer", "base_model:InstaDeepAI/nucleotide-transformer-2.5b-multi-species", "base_model:finetune:InstaDeepAI/nucleotide-transformer-2.5b-multi-species", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T15:10:35Z	--- library_name: transformers license: cc-by-nc-sa-4.0 base_model: InstaDeepAI/nucleotide-transformer-2.5b-multi-species tags: - generated_from_trainer metrics: - precision - recall - accuracy model-index: - name: nucleotide-transformer-2.5b-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC_one_shot 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. --> # nucleotide-transformer-2.5b-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC_one_shot This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-multi-species) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4395 - F1 Score: 0.8571 - Precision: 0.75 - Recall: 1.0 - Accuracy: 0.8519 - Auc: 0.8944 - Prc: 0.8290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Precision \| Recall \| Accuracy \| Auc \| Prc \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:--------:\|:------:\|:------:\| \| 0.1154 \| 18.5185 \| 500 \| 1.4395 \| 0.8571 \| 0.75 \| 1.0 \| 0.8519 \| 0.8944 \| 0.8290 \| ### Framework versions - Transformers 4.46.0.dev0 - Pytorch 2.4.1+cu121 - Datasets 2.18.0 - Tokenizers 0.20.0
Xu-Ouyang/pythia-6.9b-deduped-int4-step24000-AWQ	Xu-Ouyang	2024-11-02T00:06:09Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T23:57:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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glif-loradex-trainer/x_bulbul_x_HOR_berlin	glif-loradex-trainer	2024-11-02T00:04:11Z	9	1	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2024-11-02T00:03:18Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1730505740277__000003000_0.jpg text: trump dj at hor - output: url: samples/1730505762109__000003000_1.jpg text: cat dj at hor - output: url: samples/1730505783904__000003000_2.jpg text: clown dj at hor base_model: black-forest-labs/FLUX.1-dev trigger: hor instance_prompt: hor 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 --- # HOR_berlin Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `x_bulbul_x`. <Gallery /> ## Trigger words You should use `hor` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/x_bulbul_x_HOR_berlin/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
Xu-Ouyang/pythia-1b-deduped-int4-step16-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T23:56:03Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T23:55:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1b-deduped-int3-step16-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T23:48:32Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T23:48:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1b-deduped-int4-step8-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T23:40:40Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T23:40:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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exala/db_aca2_4.2	exala	2024-11-01T23:38:39Z	216	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T23:38:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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meelu/CerebrasGPT111M_morf_bpe_tokenizer_mixed.440M_50257_OnlyEmb_False_20241102_003443	meelu	2024-11-01T23:35:57Z	214	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T23:35:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-12b-deduped-int3-step20000-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T23:33:28Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T23:22:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1b-deduped-int3-step8-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T23:33:16Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T23:32:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cgoosen/Prompt-Guard-finetuned-ctf-86M	cgoosen	2024-11-01T23:31:13Z	31	0	null	[ "safetensors", "deberta-v2", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "region:us" ]	null	2024-10-25T12:27:12Z	--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: Prompt-Guard-finetuned-ctf-86M 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. --> # Prompt-Guard-finetuned-ctf-86M This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0155 - Accuracy: 0.9972 - Precision: 0.9972 - Recall: 0.9972 - F1: 0.9972 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.0364 \| 1.0 \| 2344 \| 0.0224 \| 0.9964 \| 0.9964 \| 0.9964 \| 0.9964 \| \| 0.038 \| 2.0 \| 4688 \| 0.0405 \| 0.9893 \| 0.9907 \| 0.9893 \| 0.9897 \| \| 0.0126 \| 3.0 \| 7032 \| 0.0211 \| 0.9962 \| 0.9962 \| 0.9962 \| 0.9962 \| \| 0.0077 \| 4.0 \| 9376 \| 0.0206 \| 0.9966 \| 0.9966 \| 0.9966 \| 0.9966 \| \| 0.0038 \| 5.0 \| 11720 \| 0.0155 \| 0.9972 \| 0.9972 \| 0.9972 \| 0.9972 \| \| 0.0015 \| 6.0 \| 14064 \| 0.0201 \| 0.9972 \| 0.9972 \| 0.9972 \| 0.9972 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.5.0+cu124 - Datasets 2.18.0 - Tokenizers 0.19.1
jiviteshjn/Llama-3.2-1B-Instruct-Q5_K_M-GGUF	jiviteshjn	2024-11-01T23:30:04Z	6	0	transformers	[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-1B-Instruct", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-01T23:29:58Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \n“Licensee” or “you” means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo jiviteshjn/Llama-3.2-1B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-1b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jiviteshjn/Llama-3.2-1B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-1b-instruct-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo jiviteshjn/Llama-3.2-1B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-1b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jiviteshjn/Llama-3.2-1B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-1b-instruct-q5_k_m.gguf -c 2048 ```
bartowski/AMD-OLMo-1B-SFT-DPO-GGUF	bartowski	2024-11-01T23:28:13Z	227	4	null	[ "gguf", "text-generation", "dataset:allenai/dolma", "base_model:amd/AMD-OLMo-1B-SFT-DPO", "base_model:quantized:amd/AMD-OLMo-1B-SFT-DPO", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-01T23:18:46Z	--- base_model: amd/AMD-OLMo-1B-SFT-DPO datasets: - allenai/dolma license: apache-2.0 pipeline_tag: text-generation quantized_by: bartowski --- ## Llamacpp imatrix Quantizations of AMD-OLMo-1B-SFT-DPO Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3991">b3991</a> for quantization. Original model: https://huggingface.co/amd/AMD-OLMo-1B-SFT-DPO All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) ## Prompt format ``` <\|system\|> {system_prompt} <\|user\|> {prompt} <\|assistant\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Split \| Description \| \| -------- \| ---------- \| --------- \| ----- \| ----------- \| \| [AMD-OLMo-1B-SFT-DPO-f16.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-f16.gguf) \| f16 \| 2.36GB \| false \| Full F16 weights. \| \| [AMD-OLMo-1B-SFT-DPO-Q8_0.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q8_0.gguf) \| Q8_0 \| 1.25GB \| false \| Extremely high quality, generally unneeded but max available quant. \| \| [AMD-OLMo-1B-SFT-DPO-Q6_K_L.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q6_K_L.gguf) \| Q6_K_L \| 0.99GB \| false \| Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q6_K.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q6_K.gguf) \| Q6_K \| 0.97GB \| false \| Very high quality, near perfect, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q5_K_L.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q5_K_L.gguf) \| Q5_K_L \| 0.87GB \| false \| Uses Q8_0 for embed and output weights. High quality, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q5_K_M.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q5_K_M.gguf) \| Q5_K_M \| 0.85GB \| false \| High quality, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q5_K_S.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q5_K_S.gguf) \| Q5_K_S \| 0.82GB \| false \| High quality, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q4_K_L.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q4_K_L.gguf) \| Q4_K_L \| 0.76GB \| false \| Uses Q8_0 for embed and output weights. Good quality, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q4_K_M.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q4_K_M.gguf) \| Q4_K_M \| 0.73GB \| false \| Good quality, default size for must use cases, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q4_K_S.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q4_K_S.gguf) \| Q4_K_S \| 0.70GB \| false \| Slightly lower quality with more space savings, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q4_0_8_8.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q4_0_8_8.gguf) \| Q4_0_8_8 \| 0.69GB \| false \| Optimized for ARM inference. Requires 'sve' support (see link below). Don't use on Mac or Windows. \| \| [AMD-OLMo-1B-SFT-DPO-Q4_0_4_8.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q4_0_4_8.gguf) \| Q4_0_4_8 \| 0.69GB \| false \| Optimized for ARM inference. Requires 'i8mm' support (see link below). Don't use on Mac or Windows. \| \| [AMD-OLMo-1B-SFT-DPO-Q4_0_4_4.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q4_0_4_4.gguf) \| Q4_0_4_4 \| 0.69GB \| false \| Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. Don't use on Mac or Windows. \| \| [AMD-OLMo-1B-SFT-DPO-Q4_0.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q4_0.gguf) \| Q4_0 \| 0.69GB \| false \| Legacy format, generally not worth using over similarly sized formats \| \| [AMD-OLMo-1B-SFT-DPO-Q3_K_XL.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q3_K_XL.gguf) \| Q3_K_XL \| 0.68GB \| false \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \| \| [AMD-OLMo-1B-SFT-DPO-IQ4_XS.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-IQ4_XS.gguf) \| IQ4_XS \| 0.66GB \| false \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [AMD-OLMo-1B-SFT-DPO-Q3_K_L.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-Q3_K_L.gguf) \| Q3_K_L \| 0.65GB \| false \| Lower quality but usable, good for low RAM availability. \| \| [AMD-OLMo-1B-SFT-DPO-IQ3_M.gguf](https://huggingface.co/bartowski/AMD-OLMo-1B-SFT-DPO-GGUF/blob/main/AMD-OLMo-1B-SFT-DPO-IQ3_M.gguf) \| IQ3_M \| 0.57GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. Thanks! ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/AMD-OLMo-1B-SFT-DPO-GGUF --include "AMD-OLMo-1B-SFT-DPO-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/AMD-OLMo-1B-SFT-DPO-GGUF --include "AMD-OLMo-1B-SFT-DPO-Q8_0/" --local-dir ./ ``` You can either specify a new local-dir (AMD-OLMo-1B-SFT-DPO-Q8_0) or download them all in place (./) ## Q4_0_X_X These are NOT* for Metal (Apple) offloading, only ARM chips. If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660) To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!). ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset Thank you ZeroWw for the inspiration to experiment with embed/output Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Xu-Ouyang/pythia-1b-deduped-int4-step4-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T23:25:22Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T23:25:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lmstudio-community/AMD-OLMo-1B-SFT-GGUF	lmstudio-community	2024-11-01T23:19:36Z	19	1	null	[ "gguf", "text-generation", "dataset:allenai/dolma", "base_model:amd/AMD-OLMo-1B-SFT", "base_model:quantized:amd/AMD-OLMo-1B-SFT", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T23:17:52Z	--- base_model: amd/AMD-OLMo-1B-SFT datasets: - allenai/dolma license: apache-2.0 pipeline_tag: text-generation quantized_by: bartowski --- ## 💫 Community Model> AMD OLMo 1B SFT by Amd 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [amd](https://huggingface.co/amd)<br> Original model: [AMD-OLMo-1B-SFT](https://huggingface.co/amd/AMD-OLMo-1B-SFT)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3991](https://github.com/ggerganov/llama.cpp/releases/tag/b3991)<br> ## Technical Details AMD-OLMo-1B is based on the model architecture and training set up of fully open source 1 billion version of OLMo-1B Context length of 2048 Vocab size of 50,280 Pretrained on 1.3 trillion tokens from https://huggingface.co/datasets/allenai/dolma Supervised fine-tuned (SFT) on [Tulu V2](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) dataset (1st phase) and then [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5), [WebInstructSub](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub), and [Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback) datasets (2nd phase). ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
mav23/granite-8b-code-instruct-4k-GGUF	mav23	2024-11-01T23:14:43Z	73	0	transformers	[ "transformers", "gguf", "code", "granite", "text-generation", "dataset:bigcode/commitpackft", "dataset:TIGER-Lab/MathInstruct", "dataset:meta-math/MetaMathQA", "dataset:glaiveai/glaive-code-assistant-v3", "dataset:glaive-function-calling-v2", "dataset:bugdaryan/sql-create-context-instruction", "dataset:garage-bAInd/Open-Platypus", "dataset:nvidia/HelpSteer", "arxiv:2405.04324", "base_model:ibm-granite/granite-8b-code-base-4k", "base_model:quantized:ibm-granite/granite-8b-code-base-4k", "license:apache-2.0", "model-index", "region:us", "conversational" ]	text-generation	2024-11-01T22:14:21Z	--- pipeline_tag: text-generation base_model: ibm-granite/granite-8b-code-base-4k inference: false license: apache-2.0 datasets: - bigcode/commitpackft - TIGER-Lab/MathInstruct - meta-math/MetaMathQA - glaiveai/glaive-code-assistant-v3 - glaive-function-calling-v2 - bugdaryan/sql-create-context-instruction - garage-bAInd/Open-Platypus - nvidia/HelpSteer metrics: - code_eval library_name: transformers tags: - code - granite model-index: - name: granite-8b-code-instruct-4k results: - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Python) metrics: - name: pass@1 type: pass@1 value: 57.9 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(JavaScript) metrics: - name: pass@1 type: pass@1 value: 52.4 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Java) metrics: - name: pass@1 type: pass@1 value: 58.5 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Go) metrics: - name: pass@1 type: pass@1 value: 43.3 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(C++) metrics: - name: pass@1 type: pass@1 value: 48.2 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Rust) metrics: - name: pass@1 type: pass@1 value: 37.2 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(Python) metrics: - name: pass@1 type: pass@1 value: 53.0 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(JavaScript) metrics: - name: pass@1 type: pass@1 value: 42.7 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(Java) metrics: - name: pass@1 type: pass@1 value: 52.4 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(Go) metrics: - name: pass@1 type: pass@1 value: 36.6 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(C++) metrics: - name: pass@1 type: pass@1 value: 43.9 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(Rust) metrics: - name: pass@1 type: pass@1 value: 16.5 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalFix(Python) metrics: - name: pass@1 type: pass@1 value: 39.6 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalFix(JavaScript) metrics: - name: pass@1 type: pass@1 value: 40.9 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalFix(Java) metrics: - name: pass@1 type: pass@1 value: 48.2 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalFix(Go) metrics: - name: pass@1 type: pass@1 value: 41.5 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalFix(C++) metrics: - name: pass@1 type: pass@1 value: 39.0 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalFix(Rust) metrics: - name: pass@1 type: pass@1 value: 32.9 veriefied: false --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62cd5057674cdb524450093d/1hzxoPwqkBJXshKVVe6_9.png) # Granite-8B-Code-Instruct-4K ## Model Summary Granite-8B-Code-Instruct-4K is a 8B parameter model fine tuned from Granite-8B-Code-Base-4K on a combination of permissively licensed instruction data to enhance instruction following capabilities including logical reasoning and problem-solving skills. - Developers: IBM Research - GitHub Repository: [ibm-granite/granite-code-models](https://github.com/ibm-granite/granite-code-models) - Paper: [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://arxiv.org/abs/2405.04324) - Release Date: May 6th, 2024 - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). ## Usage ### Intended use The model is designed to respond to coding related instructions and can be used to build coding assistants. <!-- TO DO: Check starcoder2 instruct code example that includes the template https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 --> ### Generation This is a simple example of how to use Granite-8B-Code-Instruct-4K model. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # or "cpu" model_path = "ibm-granite/granite-8b-code-instruct-4k" tokenizer = AutoTokenizer.from_pretrained(model_path) # drop device_map if running on CPU model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) model.eval() # change input text as desired chat = [ { "role": "user", "content": "Write a code to find the maximum value in a list of numbers." }, ] chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) # tokenize the text input_tokens = tokenizer(chat, return_tensors="pt") # transfer tokenized inputs to the device for i in input_tokens: input_tokens[i] = input_tokens[i].to(device) # generate output tokens output = model.generate(*input_tokens, max_new_tokens=100) # decode output tokens into text output = tokenizer.batch_decode(output) # loop over the batch to print, in this example the batch size is 1 for i in output: print(i) ``` <!-- TO DO: Check this part --> ## Training Data Granite Code Instruct models are trained on the following types of data. Code Commits Datasets: we sourced code commits data from the [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) dataset, a filtered version of the full CommitPack dataset. From CommitPackFT dataset, we only consider data for 92 programming languages. Our inclusion criteria boils down to selecting programming languages common across CommitPackFT and the 116 languages that we considered to pretrain the code-base model (Granite-8B-Code-Base). * Math Datasets: We consider two high-quality math datasets, [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) and [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA). Due to license issues, we filtered out GSM8K-RFT and Camel-Math from MathInstruct dataset. * Code Instruction Datasets: We use [Glaive-Code-Assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3), [Glaive-Function-Calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [NL2SQL11](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction) and a small collection of synthetic API calling datasets. * Language Instruction Datasets: We include high-quality datasets such as [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) and an open license-filtered version of [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). We also include a collection of hardcoded prompts to ensure our model generates correct outputs given inquiries about its name or developers. ## Infrastructure We train the Granite Code models using two of IBM's super computing clusters, namely Vela and Blue Vela, both outfitted with NVIDIA A100 and H100 GPUs respectively. These clusters provide a scalable and efficient infrastructure for training our models over thousands of GPUs. ## Ethical Considerations and Limitations Granite code instruct models are primarily finetuned using instruction-response pairs across a specific set of programming languages. Thus, their performance may be limited with out-of-domain programming languages. In this situation, it is beneficial providing few-shot examples to steer the model's output. Moreover, developers should perform safety testing and target-specific tuning before deploying these models on critical applications. The model also inherits ethical considerations and limitations from its base model. For more information, please refer to [Granite-8B-Code-Base-4K](https://huggingface.co/ibm-granite/granite-8b-code-base-4k) model card.
Xu-Ouyang/pythia-1b-deduped-int4-step1-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T22:49:36Z	77	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T22:47:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bikkibhagya/gita-text-generation-gpt2	bikkibhagya	2024-11-01T22:42:28Z	145	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T22:41:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1b-deduped-int3-step1-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T22:40:31Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T22:40:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-12b-deduped-int3-step256-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T22:32:19Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T22:22:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/MiniMA-2-3B-GGUF	mradermacher	2024-11-01T22:29:06Z	21	0	transformers	[ "transformers", "gguf", "en", "zh", "dataset:EleutherAI/pile", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:p208p2002/wudao", "base_model:GeneZC/MiniMA-2-3B", "base_model:quantized:GeneZC/MiniMA-2-3B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-11-01T17:36:27Z	--- base_model: GeneZC/MiniMA-2-3B datasets: - EleutherAI/pile - togethercomputer/RedPajama-Data-1T - p208p2002/wudao language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/GeneZC/MiniMA-2-3B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MiniMA-2-3B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q2_K.gguf) \| Q2_K \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q3_K_S.gguf) \| Q3_K_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q3_K_M.gguf) \| Q3_K_M \| 1.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q3_K_L.gguf) \| Q3_K_L \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.IQ4_XS.gguf) \| IQ4_XS \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q4_K_M.gguf) \| Q4_K_M \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q5_K_S.gguf) \| Q5_K_S \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniMA-2-3B-GGUF/resolve/main/MiniMA-2-3B.Q8_0.gguf) \| Q8_0 \| 3.3 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Paradigmo/aashwin-lora-2	Paradigmo	2024-11-01T22:27:27Z	5	1	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-01T21:51:06Z	--- 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: Aashwin_Shrivastava --- # Aashwin Lora 2 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Aashwin_Shrivastava` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Paradigmo/aashwin-lora-2', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
Xu-Ouyang/pythia-1.4b-deduped-int3-step1000-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T22:20:12Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T22:19:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf	RichardErkhov	2024-11-01T22:09:42Z	34	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-01T20:34:31Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Falcon2-5.5B-Dutch - GGUF - Model creator: https://huggingface.co/ssmits/ - Original model: https://huggingface.co/ssmits/Falcon2-5.5B-Dutch/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Falcon2-5.5B-Dutch.Q2_K.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q2_K.gguf) \| Q2_K \| 2.03GB \| \| [Falcon2-5.5B-Dutch.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q3_K_S.gguf) \| Q3_K_S \| 2.35GB \| \| [Falcon2-5.5B-Dutch.Q3_K.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q3_K.gguf) \| Q3_K \| 2.56GB \| \| [Falcon2-5.5B-Dutch.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q3_K_M.gguf) \| Q3_K_M \| 2.56GB \| \| [Falcon2-5.5B-Dutch.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q3_K_L.gguf) \| Q3_K_L \| 2.72GB \| \| [Falcon2-5.5B-Dutch.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.IQ4_XS.gguf) \| IQ4_XS \| 2.87GB \| \| [Falcon2-5.5B-Dutch.Q4_0.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q4_0.gguf) \| Q4_0 \| 2.99GB \| \| [Falcon2-5.5B-Dutch.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.IQ4_NL.gguf) \| IQ4_NL \| 3.01GB \| \| [Falcon2-5.5B-Dutch.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q4_K_S.gguf) \| Q4_K_S \| 2.99GB \| \| [Falcon2-5.5B-Dutch.Q4_K.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q4_K.gguf) \| Q4_K \| 3.19GB \| \| [Falcon2-5.5B-Dutch.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q4_K_M.gguf) \| Q4_K_M \| 3.19GB \| \| [Falcon2-5.5B-Dutch.Q4_1.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q4_1.gguf) \| Q4_1 \| 3.29GB \| \| [Falcon2-5.5B-Dutch.Q5_0.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q5_0.gguf) \| Q5_0 \| 3.6GB \| \| [Falcon2-5.5B-Dutch.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q5_K_S.gguf) \| Q5_K_S \| 3.6GB \| \| [Falcon2-5.5B-Dutch.Q5_K.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q5_K.gguf) \| Q5_K \| 3.8GB \| \| [Falcon2-5.5B-Dutch.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q5_K_M.gguf) \| Q5_K_M \| 3.8GB \| \| [Falcon2-5.5B-Dutch.Q5_1.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q5_1.gguf) \| Q5_1 \| 3.9GB \| \| [Falcon2-5.5B-Dutch.Q6_K.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q6_K.gguf) \| Q6_K \| 4.24GB \| \| [Falcon2-5.5B-Dutch.Q8_0.gguf](https://huggingface.co/RichardErkhov/ssmits_-_Falcon2-5.5B-Dutch-gguf/blob/main/Falcon2-5.5B-Dutch.Q8_0.gguf) \| Q8_0 \| 5.41GB \| Original model description: --- base_model: - tiiuae/falcon-11B library_name: transformers tags: - mergekit - merge - lazymergekit license: apache-2.0 language: - nl --- ## Why prune? Even though [Falcon-11B](https://huggingface.co/tiiuae/falcon-11B) is trained on 5T tokens, it is still undertrained, as can be seen by this graph: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/660c0a02cf274b3ab77dd6b7/QeaL9bOrPskustzFpjMUP.png) This is why the choice is made to prune 50% of the layers. Note that \~1B of continued pre-training (\~1M rows of 1k tokens) is still required to restore the perplexity of this model in the desired language. I'm planning on doing that for certain languages, depending on how much compute will be available. # sliced This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [tiiuae/falcon-11B](https://huggingface.co/tiiuae/falcon-11B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: tiiuae/falcon-11B layer_range: [0, 25] - sources: - model: tiiuae/falcon-11B layer_range: [56, 59] merge_method: passthrough dtype: bfloat16 ``` [PruneMe](https://github.com/arcee-ai/PruneMe) has been utilized using the wikimedia/wikipedia Dutch (nl) subset by investigating layer similarity with 2000 samples. The layer ranges for pruning were determined based on this analysis to maintain performance while reducing model size. ![Layer Similarity Plot](https://cdn-uploads.huggingface.co/production/uploads/660c0a02cf274b3ab77dd6b7/PF3SzEhQRJPXyYi2KqS1A.png) ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "ssmits/Falcon2-5.5B-Dutch" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, ) sequences = pipeline( "Can you explain the concepts of Quantum Computing?", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 Falcon LLMs require PyTorch 2.0 for use with `transformers`! For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). ## Direct Use Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) ## Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon2-5.5B is trained mostly on English, but also German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ## Recommendations We recommend users of Falcon2-5.5B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
MaziyarPanahi/Heart_Stolen-8B-task-GGUF	MaziyarPanahi	2024-11-01T22:03:55Z	56	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:allknowingroger/Heart_Stolen-8B-task", "base_model:quantized:allknowingroger/Heart_Stolen-8B-task", "region:us", "conversational" ]	text-generation	2024-11-01T21:41:28Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: Heart_Stolen-8B-task-GGUF base_model: allknowingroger/Heart_Stolen-8B-task inference: false model_creator: allknowingroger pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Heart_Stolen-8B-task-GGUF](https://huggingface.co/MaziyarPanahi/Heart_Stolen-8B-task-GGUF) - Model creator: [allknowingroger](https://huggingface.co/allknowingroger) - Original model: [allknowingroger/Heart_Stolen-8B-task](https://huggingface.co/allknowingroger/Heart_Stolen-8B-task) ## Description [MaziyarPanahi/Heart_Stolen-8B-task-GGUF](https://huggingface.co/MaziyarPanahi/Heart_Stolen-8B-task-GGUF) contains GGUF format model files for [allknowingroger/Heart_Stolen-8B-task](https://huggingface.co/allknowingroger/Heart_Stolen-8B-task). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
mradermacher/Puffin-Qwen2.5-TIES-GGUF	mradermacher	2024-11-01T22:02:06Z	66	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:tuanpasg/Puffin-Qwen2.5-TIES", "base_model:quantized:tuanpasg/Puffin-Qwen2.5-TIES", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-01T17:27:00Z	--- base_model: tuanpasg/Puffin-Qwen2.5-TIES language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/tuanpasg/Puffin-Qwen2.5-TIES <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q5_K_S.gguf) \| Q5_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q6_K.gguf) \| Q6_K \| 1.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.Q8_0.gguf) \| Q8_0 \| 2.0 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Puffin-Qwen2.5-TIES-GGUF/resolve/main/Puffin-Qwen2.5-TIES.f16.gguf) \| f16 \| 3.7 \| 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 -->
DeZoomer/AnyaTaylorJoy-FluxLora	DeZoomer	2024-11-01T21:51:33Z	1,889	4	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "stable-diffusion", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-01T21:49:47Z	--- tags: - text-to-image - flux - lora - diffusers - stable-diffusion widget: - text: '-' output: url: images/233152_-1_0_image_4_share_00001.webp - text: '-' output: url: images/232002_-1_0_image_4_share_00001.webp - text: '-' output: url: images/232002_-1_0_image_4_share_00003.webp - text: '-' output: url: images/232003_-1_0_image_4_share_00001.webp - text: '-' output: url: images/232003_-1_0_image_4_share_00002.webp - text: '-' output: url: images/232003_-1_0_image_4_share_00003.webp - text: '-' output: url: images/232004_-1_0_image_4_share_00001.webp - text: '-' output: url: images/233151_-1_0_image_4_share_00002.webp - text: '-' output: url: images/233151_-1_0_image_4_share_00003.webp - text: '-' output: url: images/232002_-1_0_image_4_share_00002.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: null 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 inference: parameters: width: 768 height: 1024 --- # Anya Taylor-Joy \| Flux <Gallery /> ## Model description Trained locally with 20 publicly accessible images using AI-Toolkit (Flux.1 Dev). Use with LoRA strength between 0.8-1.2 and FluxGuidance between 3-4. No keywords needed. Example prompt (ComfyUI): Portrait photo of a woman in a garden. Want a custom/private LoRA? Good news—commissions are open! Request yours here: [https://ko-fi.com/de_zoomer/commissions](https://ko-fi.com/de_zoomer/commissions). ## Background I've been deeply exploring how to create LoRAs with 100% accuracy to the original character. My focus is on quality, which is why my files tend to be heavier than others. After creating over 100+ LoRAs for testing, using both Kohya and AI-Toolkit since day one, I've consistently stayed up to date with the latest releases, exchanging knowledge in their communities. My expertise is mainly with characters, so I’m not as familiar with LoRAs for style or anime, although the process might not differ too much. If you want your own custom LoRa, feel free to message me! Commissions are open—check out my Ko-fi link above. Enjoy using my LoRAs and have fun! ## Download model Weights for this model are available in Safetensors format. [Download](/DeZoomer/AnyaTaylorJoy-FluxLora/tree/main) them in the Files & versions tab.
DeZoomer/AlexandraDaddario-FluxLora	DeZoomer	2024-11-01T21:48:14Z	195	1	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "stable-diffusion", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-01T21:44:23Z	--- tags: - text-to-image - flux - lora - diffusers - stable-diffusion widget: - text: '-' output: url: images/193143_-1_0_image_4_share_00002.webp - text: '-' output: url: images/193143_-1_0_image_4_share_00003.webp - text: '-' output: url: images/194215_-1_0_image_4_share_00002.webp - text: '-' output: url: images/193117_-1_0_image_4_share_00001.webp - text: '-' output: url: images/193143_-1_0_image_4_share_00001.webp - text: '-' output: url: images/193144_-1_0_image_4_share_00001.webp - text: '-' output: url: images/193144_-1_0_image_4_share_00002.webp - text: '-' output: url: images/194214_-1_0_image_4_share_00001.webp - text: '-' output: url: images/194214_-1_0_image_4_share_00003.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: null 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 inference: parameters: width: 768 height: 1024 --- # Alexandra Daddario \| Flux <Gallery /> ## Model description Trained locally with 20 publicly accessible images using AI-Toolkit (Flux.1 Dev). Use with LoRA strength between 0.8-1.2 and FluxGuidance between 3-4. No keywords needed. Example prompt (ComfyUI): Portrait photo of a woman in a garden. Want a custom/private LoRA? Good news—commissions are open! Request yours here: [https://ko-fi.com/de_zoomer/commissions](https://ko-fi.com/de_zoomer/commissions). ## Background I've been deeply exploring how to create LoRAs with 100% accuracy to the original character. My focus is on quality, which is why my files tend to be heavier than others. After creating over 100+ LoRAs for testing, using both Kohya and AI-Toolkit since day one, I've consistently stayed up to date with the latest releases, exchanging knowledge in their communities. My expertise is mainly with characters, so I’m not as familiar with LoRAs for style or anime, although the process might not differ too much. If you want your own custom LoRa, feel free to message me! Commissions are open—check out my Ko-fi link above. Enjoy using my LoRAs and have fun! ## Download model Weights for this model are available in Safetensors format. [Download](/DeZoomer/AlexandraDaddario-FluxLora/tree/main) them in the Files & versions tab.
exala/db_aca2_4.1	exala	2024-11-01T21:45:46Z	105	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T21:45:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Xu-Ouyang/pythia-12b-deduped-int4-step20000-AWQ	Xu-Ouyang	2024-11-01T21:44:01Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T21:42:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-1.4b-deduped-int4-step256-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T21:41:21Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T21:40:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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deepnet/SN9-C4-llama-HK1-3	deepnet	2024-11-01T21:39:32Z	34	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T21:33:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DeZoomer/SydneySweeney-FluxLora	DeZoomer	2024-11-01T21:38:25Z	839	1	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "stable-diffusion", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-01T21:36:32Z	--- tags: - text-to-image - flux - lora - diffusers - stable-diffusion widget: - text: '-' output: url: images/220120_-1_0_image_4_share_00004.webp - text: '-' output: url: images/220121_-1_0_image_4_share_00001.webp - text: '-' output: url: images/220117_-1_0_image_4_share_00002.webp - text: '-' output: url: images/220117_-1_0_image_4_share_00003.webp - text: '-' output: url: images/220118_-1_0_image_4_share_00002.webp - text: '-' output: url: images/220118_-1_0_image_4_share_00004.webp - text: '-' output: url: images/220119_-1_0_image_4_share_00004.webp - text: '-' output: url: images/220120_-1_0_image_4_share_00001.webp - text: '-' output: url: images/220120_-1_0_image_4_share_00003.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: null 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 inference: parameters: width: 768 height: 1024 --- # Sydney Sweeney \| Flux <Gallery /> ## Model description Trained locally with 20 publicly accessible images using AI-Toolkit (Flux.1 Dev). Use with LoRA strength between 0.8-1.2 and FluxGuidance between 3-4. No keywords needed. Example prompt (ComfyUI): Portrait photo of a woman in a garden. Want a custom/private LoRA? Good news—commissions are open! Request yours here: [https://ko-fi.com/de_zoomer/commissions](https://ko-fi.com/de_zoomer/commissions). ## Background I've been deeply exploring how to create LoRAs with 100% accuracy to the original character. My focus is on quality, which is why my files tend to be heavier than others. After creating over 100+ LoRAs for testing, using both Kohya and AI-Toolkit since day one, I've consistently stayed up to date with the latest releases, exchanging knowledge in their communities. My expertise is mainly with characters, so I’m not as familiar with LoRAs for style or anime, although the process might not differ too much. If you want your own custom LoRa, feel free to message me! Commissions are open—check out my Ko-fi link above. Enjoy using my LoRAs and have fun! ## Download model Weights for this model are available in Safetensors format. [Download](/DeZoomer/SydneySweeney-FluxLora/tree/main) them in the Files & versions tab.
DeZoomer/EmiliaClarke-FluxLora	DeZoomer	2024-11-01T21:31:29Z	263	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "stable-diffusion", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-01T21:30:01Z	--- tags: - text-to-image - flux - lora - diffusers - stable-diffusion widget: - text: '-' output: url: images/204502_-1_0_image_4_share_00001.webp - text: '-' output: url: images/204505_-1_0_image_4_share_00001.webp - text: '-' output: url: images/204505_-1_0_image_4_share_00002.webp - text: '-' output: url: images/204506_-1_0_image_4_share_00001.webp - text: '-' output: url: images/204506_-1_0_image_4_share_00002.webp - text: '-' output: url: images/204506_-1_0_image_4_share_00004.webp - text: '-' output: url: images/204507_-1_0_image_4_share_00001.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: null 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 inference: parameters: width: 768 height: 1024 --- # Emilia Clarke \| Flux <Gallery /> ## Model description Trained locally with 20 publicly accessible images using AI-Toolkit (Flux.1 Dev). Use with LoRA strength between 0.8-1.2 and FluxGuidance between 3-4. No keywords needed. Example prompt (ComfyUI): Portrait photo of a woman in a garden. Want a custom/private LoRA? Good news—commissions are open! Request yours here: [https://ko-fi.com/de_zoomer/commissions](https://ko-fi.com/de_zoomer/commissions). ## Background I've been deeply exploring how to create LoRAs with 100% accuracy to the original character. My focus is on quality, which is why my files tend to be heavier than others. After creating over 100+ LoRAs for testing, using both Kohya and AI-Toolkit since day one, I've consistently stayed up to date with the latest releases, exchanging knowledge in their communities. My expertise is mainly with characters, so I’m not as familiar with LoRAs for style or anime, although the process might not differ too much. If you want your own custom LoRa, feel free to message me! Commissions are open—check out my Ko-fi link above. Enjoy using my LoRAs and have fun! ## Download model Weights for this model are available in Safetensors format. [Download](/DeZoomer/EmiliaClarke-FluxLora/tree/main) them in the Files & versions tab.
Xu-Ouyang/pythia-1.4b-deduped-int3-step256-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T21:29:18Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T21:28:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MaziyarPanahi/Llama-3.2-3B-Booval-GGUF	MaziyarPanahi	2024-11-01T21:23:51Z	56	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:bunnycore/Llama-3.2-3B-Booval", "base_model:quantized:bunnycore/Llama-3.2-3B-Booval", "region:us", "conversational" ]	text-generation	2024-11-01T21:14:30Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: Llama-3.2-3B-Booval-GGUF base_model: bunnycore/Llama-3.2-3B-Booval inference: false model_creator: bunnycore pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Llama-3.2-3B-Booval-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3.2-3B-Booval-GGUF) - Model creator: [bunnycore](https://huggingface.co/bunnycore) - Original model: [bunnycore/Llama-3.2-3B-Booval](https://huggingface.co/bunnycore/Llama-3.2-3B-Booval) ## Description [MaziyarPanahi/Llama-3.2-3B-Booval-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3.2-3B-Booval-GGUF) contains GGUF format model files for [bunnycore/Llama-3.2-3B-Booval](https://huggingface.co/bunnycore/Llama-3.2-3B-Booval). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
DeZoomer/MarinaRuyBarbosa-FluxLora	DeZoomer	2024-11-01T21:17:48Z	7	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "stable-diffusion", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-01T21:16:12Z	--- tags: - text-to-image - flux - lora - diffusers - stable-diffusion widget: - text: '-' output: url: images/220826_-1_0_image_4_share_00001.webp - text: '-' output: url: images/220826_-1_0_image_4_share_00002.webp - text: '-' output: url: images/220827_-1_0_image_4_share_00004.webp - text: '-' output: url: images/220828_-1_0_image_4_share_00003.webp - text: '-' output: url: images/220829_-1_0_image_4_share_00001.webp - text: '-' output: url: images/220829_-1_0_image_4_share_00002.webp - text: '-' output: url: images/220829_-1_0_image_4_share_00003.webp - text: '-' output: url: images/220829_-1_0_image_4_share_00005.webp - text: '-' output: url: images/220830_-1_0_image_4_share_00001.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: null 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 inference: parameters: width: 768 height: 1024 --- # Marina Ruy Barbosa \| Flux <Gallery /> ## Model description Trained locally with 20 publicly accessible images using AI-Toolkit (Flux.1 Dev). Use with LoRA strength between 0.8-1.2 and FluxGuidance between 3-4. No keywords needed. Example prompt (ComfyUI): Portrait photo of a woman in a garden. Want a custom/private LoRA? Good news—commissions are open! Request yours here: [https://ko-fi.com/de_zoomer/commissions](https://ko-fi.com/de_zoomer/commissions). ## Background I've been deeply exploring how to create LoRAs with 100% accuracy to the original character. My focus is on quality, which is why my files tend to be heavier than others. After creating over 100+ LoRAs for testing, using both Kohya and AI-Toolkit since day one, I've consistently stayed up to date with the latest releases, exchanging knowledge in their communities. My expertise is mainly with characters, so I’m not as familiar with LoRAs for style or anime, although the process might not differ too much. If you want your own custom LoRa, feel free to message me! Commissions are open—check out my Ko-fi link above. Enjoy using my LoRAs and have fun! ## Download model Weights for this model are available in Safetensors format. [Download](/DeZoomer/MarinaRuyBarbosa-FluxLora/tree/main) them in the Files & versions tab.
Xu-Ouyang/pythia-1.4b-deduped-int4-step128-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T21:16:25Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T21:14:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
shellzero/gemma2-2b-ft-law-data-tag-generation	shellzero	2024-11-01T21:10:56Z	6	0	mlx	[ "mlx", "safetensors", "gemma2", "legal", "en", "dataset:ymoslem/Law-StackExchange", "base_model:google/gemma-2-2b", "base_model:finetune:google/gemma-2-2b", "license:mit", "region:us" ]	null	2024-10-29T20:50:46Z	--- license: mit datasets: - ymoslem/Law-StackExchange language: - en metrics: - f1 base_model: - google/gemma-2-2b library_name: mlx tags: - legal widget: - text: \| <start_of_turn>user ## Instructions You are a helpful AI assistant. ## User How to make scrambled eggs?<end_of_turn> <start_of_turn>model --- # shellzero/gemma2-2b-ft-law-data-tag-generation This model was converted to MLX format from [`google/gemma-7b-it`](). Refer to the [original model card](https://huggingface.co/google/gemma-7b-it) for more details on the model. ```zsh pip install mlx-lm ``` The model was LoRA fine-tuned on the [ymoslem/Law-StackExchange](https://huggingface.co/datasets/ymoslem/Law-StackExchange) and Synthetic data generated from GPT-4o and GPT-35-Turbo using the format below, for 1500 steps using `mlx`. This fine tune was one of the best runs with our data and achieved high F1 score on our eval dataset. (Part of the Nvidia hackathon) ```python def format_prompt(system_prompt: str, title: str, question: str) -> str: "Format the question to the format of the dataset we fine-tuned to." return """<bos><start_of_turn>user ## Instructions {} ## User TITLE: {} QUESTION: {}<end_of_turn> <start_of_turn>model """.format( system_prompt, title, question ) ``` Here's an example of the system_prompt from the dataset: ```text Read the following title and question about a legal issue and assign the most appropriate tag to it. All tags must be in lowercase, ordered lexicographically and separated by commas. ``` ## Loading the model using `mlx_lm` ```python from mlx_lm import generate, load model, tokenizer = load("shellzero/gemma2-2b-ft-law-data-tag-generation") response = generate( model, tokenizer, prompt=format_prompt(system_prompt, question), verbose=True, # Set to True to see the prompt and response temp=0.0, max_tokens=32, ) ```
Wehere/text	Wehere	2024-11-01T21:03:22Z	8	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "region:us" ]	null	2024-10-24T09:48:42Z	--- base_model: meta-llama/Llama-3.2-11B-Vision-Instruct 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. 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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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Xu-Ouyang/pythia-1.4b-deduped-int3-step128-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T21:02:09Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T21:01:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Jeasun/detr-resnet-50_finetuned_cppe5	Jeasun	2024-11-01T21:01:08Z	219	0	transformers	[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2024-11-01T20:48:50Z	--- library_name: transformers license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer model-index: - name: detr-resnet-50_finetuned_cppe5 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. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
jlousada315/distilhubert-finetuned-gtzan	jlousada315	2024-11-01T20:51:15Z	163	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2024-06-23T21:19:37Z	--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.83 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6889 - Accuracy: 0.83 ## 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: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1788 \| 1.0 \| 45 \| 2.0607 \| 0.41 \| \| 1.573 \| 2.0 \| 90 \| 1.5523 \| 0.49 \| \| 1.2957 \| 3.0 \| 135 \| 1.2926 \| 0.6 \| \| 1.0198 \| 4.0 \| 180 \| 1.0833 \| 0.74 \| \| 0.9007 \| 5.0 \| 225 \| 0.9275 \| 0.79 \| \| 0.7798 \| 6.0 \| 270 \| 0.8880 \| 0.76 \| \| 0.744 \| 7.0 \| 315 \| 0.7562 \| 0.84 \| \| 0.5967 \| 8.0 \| 360 \| 0.7294 \| 0.8 \| \| 0.5833 \| 9.0 \| 405 \| 0.7123 \| 0.8 \| \| 0.6378 \| 10.0 \| 450 \| 0.6889 \| 0.83 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1
Kraken/Koda-LoRuffington	Kraken	2024-11-01T20:49:29Z	12	1	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2024-11-01T20:48:31Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/ffa67262-772e-4096-a483-718a6191c7fa.png - text: '-' output: url: images/d533b6f7-f67b-4a36-89db-7797d8411680.png - text: '-' output: url: images/895720ef-4861-45a0-a2a8-b38d0fdd6b8d.png - text: '-' output: url: images/9a2e4723-7763-4a43-97ed-f912e1657abd.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Koda license: unknown --- # Koda-LoRuffington <Gallery /> ## Model description A meme generator for the Koda Fluffington Rune ## Trigger words You should use `Koda` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Kraken/Koda-LoRuffington/tree/main) them in the Files & versions tab.
Xu-Ouyang/pythia-1.4b-deduped-int4-step64-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T20:48:54Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T20:48:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
Xu-Ouyang/pythia-1.4b-deduped-int3-step64-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T20:38:15Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T20:36:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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glif-loradex-trainer/maxxd4240_PleinAir	glif-loradex-trainer	2024-11-01T20:32:59Z	46	2	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2024-11-01T20:32:17Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1730493000723__000003000_0.jpg text: italian cafe P1e!n - output: url: samples/1730493024360__000003000_1.jpg text: london streets P1e!n - output: url: samples/1730493047956__000003000_2.jpg text: statue of liberty P1e!n - output: url: samples/1730493071568__000003000_3.jpg text: rice fields P1e!n - output: url: samples/1730493095161__000003000_4.jpg text: hindu temple P1e!n - output: url: samples/1730493118971__000003000_5.jpg text: buddha statue in templeP1e!n base_model: black-forest-labs/FLUX.1-dev trigger: P1e!n instance_prompt: P1e!n 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 --- # PleinAir Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `maxxd4240`. <Gallery /> ## Trigger words You should use `P1e!n` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/maxxd4240_PleinAir/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
henilp105/InjecAgent-Llama-3.1-8B-Instruct-optim-10	henilp105	2024-11-01T20:22:55Z	5	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	null	2024-11-01T13:09:54Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct 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. 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(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 0.13.2
SicariusSicariiStuff/TheDrummer_Behemoth-123B-v1.1_FP8	SicariusSicariiStuff	2024-11-01T20:18:21Z	7	0	null	[ "safetensors", "mistral", "license:apache-2.0", "compressed-tensors", "region:us" ]	null	2024-11-01T19:40:48Z	--- license: apache-2.0 ---
tommyadams/unsloth-llama-model	tommyadams	2024-11-01T20:06:59Z	143	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T20:01:57Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: tommyadams - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
Xu-Ouyang/pythia-1.4b-deduped-int4-step16-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T19:59:26Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T19:57:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Xu-Ouyang/pythia-1.4b-deduped-int3-step16-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T19:46:53Z	78	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T19:45:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lucataco/SD3.5-Large-yarn-2	lucataco	2024-11-01T19:44:00Z	6	2	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "replicate", "template:sd-lora", "sd3.5-large", "sd3.5", "sd3.5-diffusers", "base_model:stabilityai/stable-diffusion-3.5-large", "base_model:adapter:stabilityai/stable-diffusion-3.5-large", "license:other", "region:us" ]	text-to-image	2024-11-01T19:21:43Z	--- license: other library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - replicate - template:sd-lora - sd3.5-large - sd3.5 - sd3.5-diffusers base_model: stabilityai/stable-diffusion-3.5-large instance_prompt: Frog, yarn art style widget: - text: >- Frog, yarn art style output: url: https://replicate.delivery/yhqm/WKTZ1ZnQRYZ4H9nYCfNwL34ZfGeTLkg7iBemxmIAeoXDhwldC/out-0.webp --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SD3.5-Large DreamBooth LoRA - lucataco/SD3.5-Large-yarn-2 <Gallery /> ## Model description These are lucataco/SD3.5-Large-yarn-2 DreamBooth LoRA weights for stable-diffusion-3.5-large. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md). Was LoRA for the text encoder enabled? True. ## Trigger words You should use `Frog, yarn art style` to trigger the image generation. ## Download model [Download the .safetensors LoRA](lucataco/SD3.5-Large-yarn-2/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained(stable-diffusion-3.5-large, torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('lucataco/SD3.5-Large-yarn-2', weight_name='pytorch_lora_weights.safetensors') image = pipeline('Frog, yarn art style').images[0] ``` ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`diffusers_lora_weights.safetensors` here 💾](/lucataco/SD3.5-Large-yarn-2/blob/main/diffusers_lora_weights.safetensors)*. - Rename it and place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). 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) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md). ## Training details Trained on Replicate using: [lucataco/stable-diffusion-3.5-large-lora-trainer](https://replicate.com/lucataco/stable-diffusion-3.5-large-lora-trainer) ## Notes This is an attempt at the diffusers example in trainining the [text_encoder](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md#text-encoder-training)
MaziyarPanahi/llama-v1-GGUF	MaziyarPanahi	2024-11-01T19:43:46Z	179	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:nerdyface/llama-v1", "base_model:quantized:nerdyface/llama-v1", "region:us", "conversational" ]	text-generation	2024-11-01T19:40:21Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama-v1-GGUF base_model: nerdyface/llama-v1 inference: false model_creator: nerdyface pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama-v1-GGUF](https://huggingface.co/MaziyarPanahi/llama-v1-GGUF) - Model creator: [nerdyface](https://huggingface.co/nerdyface) - Original model: [nerdyface/llama-v1](https://huggingface.co/nerdyface/llama-v1) ## Description [MaziyarPanahi/llama-v1-GGUF](https://huggingface.co/MaziyarPanahi/llama-v1-GGUF) contains GGUF format model files for [nerdyface/llama-v1](https://huggingface.co/nerdyface/llama-v1). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
JasonBounre/with_cnn_summary_train_1	JasonBounre	2024-11-01T19:26:05Z	11	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	2024-10-30T11:59:36Z	--- base_model: meta-llama/Meta-Llama-3-8B library_name: peft license: llama3 tags: - generated_from_trainer model-index: - name: with_cnn_summary_train_1 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. --> # with_cnn_summary_train_1 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4987 ## 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:---------------:\| \| 1.3255 \| 0.9991 \| 579 \| 1.2416 \| \| 1.2371 \| 2.0 \| 1159 \| 1.2072 \| \| 1.1535 \| 2.9991 \| 1738 \| 1.2081 \| \| 1.0356 \| 4.0 \| 2318 \| 1.2215 \| \| 0.9171 \| 4.9991 \| 2897 \| 1.2679 \| \| 0.7819 \| 6.0 \| 3477 \| 1.3105 \| \| 0.6835 \| 6.9991 \| 4056 \| 1.3958 \| \| 0.6058 \| 8.0 \| 4636 \| 1.4633 \| \| 0.4779 \| 8.9991 \| 5215 \| 1.5415 \| \| 0.3952 \| 10.0 \| 5795 \| 1.6919 \| \| 0.3454 \| 10.9991 \| 6374 \| 1.8169 \| \| 0.2743 \| 12.0 \| 6954 \| 1.9270 \| \| 0.2336 \| 12.9991 \| 7533 \| 1.9968 \| \| 0.2069 \| 14.0 \| 8113 \| 2.1474 \| \| 0.165 \| 14.9991 \| 8692 \| 2.2376 \| \| 0.1495 \| 16.0 \| 9272 \| 2.3137 \| \| 0.132 \| 16.9991 \| 9851 \| 2.3877 \| \| 0.125 \| 18.0 \| 10431 \| 2.4865 \| \| 0.1132 \| 18.9991 \| 11010 \| 2.5050 \| \| 0.114 \| 19.9827 \| 11580 \| 2.4987 \| ### Framework versions - PEFT 0.13.1 - Transformers 4.43.3 - Pytorch 2.4.0+cu121 - Datasets 3.0.1 - Tokenizers 0.19.1
Xu-Ouyang/pythia-1.4b-deduped-int3-step8-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T19:19:47Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T19:17:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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sap-ai-research/BERT-base-uncased-SCD-ACL2022	sap-ai-research	2024-11-01T19:19:31Z	121	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-15T23:30:55Z	--- license: apache-2.0 ---
graphitesin/gita-text-generation-gpt2	graphitesin	2024-11-01T19:10:38Z	146	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T19:10:18Z	--- 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. 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creatorchain/Pudgy	creatorchain	2024-11-01T18:53:35Z	7	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:mit", "region:us" ]	text-to-image	2024-11-01T18:52:07Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/pudgy_120.png - text: '-' output: url: images/pudgy_121.png - text: '-' output: url: images/pudgy_122.jpg - text: '-' output: url: images/pudgy_123.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: pudgy meme license: mit --- # Pudgy <Gallery /> ## Trigger words You should use `pudgy meme` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/creatorchain/Pudgy/tree/main) them in the Files & versions tab.
Xu-Ouyang/pythia-1.4b-deduped-int3-step4-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T18:52:09Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T18:50:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
creatorchain/Ponke	creatorchain	2024-11-01T18:49:55Z	9	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:mit", "region:us" ]	text-to-image	2024-11-01T18:48:25Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/ponke_66.jpg - text: '-' output: url: images/ponke_71.jpg - text: '-' output: url: images/ponke_72.jpg - text: '-' output: url: images/ponke_78.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: ponke meme license: mit --- # Ponke <Gallery /> ## Trigger words You should use `ponke meme` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/creatorchain/Ponke/tree/main) them in the Files & versions tab.
MaziyarPanahi/Llama-3.2-3B-Instruct-HateXplain-GGUF	MaziyarPanahi	2024-11-01T18:43:51Z	48	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:Samsoup/Llama-3.2-3B-Instruct-HateXplain", "base_model:quantized:Samsoup/Llama-3.2-3B-Instruct-HateXplain", "region:us", "conversational" ]	text-generation	2024-11-01T18:33:32Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: Llama-3.2-3B-Instruct-HateXplain-GGUF base_model: Samsoup/Llama-3.2-3B-Instruct-HateXplain inference: false model_creator: Samsoup pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Llama-3.2-3B-Instruct-HateXplain-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3.2-3B-Instruct-HateXplain-GGUF) - Model creator: [Samsoup](https://huggingface.co/Samsoup) - Original model: [Samsoup/Llama-3.2-3B-Instruct-HateXplain](https://huggingface.co/Samsoup/Llama-3.2-3B-Instruct-HateXplain) ## Description [MaziyarPanahi/Llama-3.2-3B-Instruct-HateXplain-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3.2-3B-Instruct-HateXplain-GGUF) contains GGUF format model files for [Samsoup/Llama-3.2-3B-Instruct-HateXplain](https://huggingface.co/Samsoup/Llama-3.2-3B-Instruct-HateXplain). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
creatorchain/Miggles	creatorchain	2024-11-01T18:42:25Z	5	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:mit", "region:us" ]	text-to-image	2024-11-01T18:41:05Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/miggles_80.jpg - text: '-' output: url: images/miggles_81.jpg - text: '-' output: url: images/miggles_82.jpg - text: '-' output: url: images/miggles_83.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: miggles meme license: mit --- # Miggles <Gallery /> ## Trigger words You should use `miggles meme` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/creatorchain/Miggles/tree/main) them in the Files & versions tab.
creatorchain/Bera	creatorchain	2024-11-01T18:38:49Z	5	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:mit", "region:us" ]	text-to-image	2024-11-01T18:37:24Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/bera_39.jpg - text: '-' output: url: images/bera_40.png - text: '-' output: url: images/bera_43.png - text: '-' output: url: images/bera_49.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: bera meme license: mit --- # Bera <Gallery /> ## Trigger words You should use `bera meme` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/creatorchain/Bera/tree/main) them in the Files & versions tab.
Xu-Ouyang/pythia-1.4b-deduped-int4-step2-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T18:38:32Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T18:36:15Z	--- 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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MatthewFrank/distilroberta-base_pytorch_5k_V01	MatthewFrank	2024-11-01T18:33:40Z	110	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T18:33:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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