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MinaMila/llama_instbase_LoRa_GermanCredit_ep9_55	MinaMila	2025-05-21T21:45:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-20T15:52:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
lborunda/lora_model_weighted	lborunda	2025-05-21T21:45:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T08:16:55Z	--- base_model: unsloth/mistral-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lborunda - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vertings6/74035697-22e9-4e69-8559-e653b40c4261	vertings6	2025-05-21T21:44:17Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/llama-2-7b", "base_model:quantized:unsloth/llama-2-7b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T21:08:40Z	--- base_model: unsloth/llama-2-7b library_name: transformers model_name: 74035697-22e9-4e69-8559-e653b40c4261 tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 74035697-22e9-4e69-8559-e653b40c4261 This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vertings6/74035697-22e9-4e69-8559-e653b40c4261", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/dappyqtq) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_1250-seed_42	rosieyzh	2025-05-21T21:43:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:37:45Z	--- 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]
ivolegrey/Digital_Impressionist_Illustrious	ivolegrey	2025-05-21T21:42:09Z	9	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "impressionist", "illustrious", "painting", "rough", "style", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:adapter:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:mit", "region:us" ]	text-to-image	2025-03-01T22:35:22Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora - impressionist - illustrious - painting - rough - style widget: - text: >- digital painting, impressionist style, bold brush strokes, skyline silhouette, futuristic, colorful neon lights, white background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00167_.png - text: >- digital painting, impressionist style, bold brush strokes, fantasy landscape gigantic trees, colorful vegetation, cliffs, floating islands, waterfalls, colorful sky, epic clouds, planet with rings parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00193_.png - text: >- digital painting, impressionist style, bold brush strokes, fantasy landscape gigantic trees, colorful vegetation, cliffs, floating islands, waterfalls, colorful sky, epic clouds, planet with rings parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00199_.png - text: >- digital painting, impressionist style, bold brush strokes, fantasy landscape gigantic trees, colorful vegetation, cliffs, floating islands, waterfalls, colorful sky, epic clouds, planet with rings parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00201_.png - text: >- digital painting, impressionist style, bold brush strokes, fantasy landscape gigantic trees, colorful vegetation, cliffs, floating islands, waterfalls, colorful sky, epic clouds, planet with rings parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00205_.png - text: >- digital painting, impressionist style, bold brush strokes, silhouette, 1girl, simple background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name output: url: images/Digital_Impressionist_Illustrious_00040_.png - text: >- digital painting, impressionist style, bold brush strokes, silhouette, 1girl, simple background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name output: url: images/Digital_Impressionist_Illustrious_00053_.png - text: >- digital painting, impressionist style, bold brush strokes, 1girl, beautiful face, naked, colorful, pink nipple, simple background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00244_.png - text: >- digital painting, impressionist style, bold brush strokes, 1girl, beautiful face, naked, colorful, pink nipple, simple background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00246_.png - text: >- digital painting, impressionist style, bold brush strokes, 1girl, naked, perfect rounded butt, colorful, simple background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00263_.png - text: >- digital painting, impressionist style, bold brush strokes, 1girl, naked, perfect rounded butt, colorful, simple background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00265_.png - text: >- digital painting, impressionist style, bold brush strokes, 1girl, naked, perfect rounded butt, colorful, simple background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, pen, brush output: url: images/Digital_Impressionist_Illustrious_00268_.png - text: >- digital painting, impressionist style, bold brush strokes, 7girls, perfect body, thin waist, large breasts, large nipples, completely covered in colorful liquid paint, artistic pose, simple colorful background parameters: negative_prompt: >- worst quality, bad quality, jpeg artifacts, bad hands, bad finger, bad anatomy, watermark, artist name, silhouette output: url: images/Digital_Impressionist_Illustrious_00029_.png base_model: - OnomaAIResearch/Illustrious-xl-early-release-v0 instance_prompt: null license: mit --- # Digital Impressionist for Illustrious <Gallery /> ## Model description This LoRA creates a digital painting in an impressionistic style. It was trained on 1000 images generated with Microsoft's Copilot Designer. The images contained elements of sci-fi, steampunk, fantasy and minimalistic silhouette art. There are no trigger words, but starting your prompt with "A digital impressionist painting with bold brush strokes about ... " should do the job. And for Illustrious "digital painting, bold brush strokes, impressionist style". # Recommended settings - Sampler: Euler normal - Steps: 25< - Guidance: 4-7 (less creates a more "rough" style) ## Download model Weights for this model are available in Safetensors format. [Download](/ivolegrey/Digital_Impressionist_Illustrious/tree/main) them in the Files & versions tab.
iTroned/self_iterative_v2_stance_iteration_1	iTroned	2025-05-21T21:41:41Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-05-15T03:51:36Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: self_iterative_v2_stance_iteration_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. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/vc8gvd7c) [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/vc8gvd7c) [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/vc8gvd7c) [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/vc8gvd7c) [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/vc8gvd7c) [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/vc8gvd7c) # self_iterative_v2_stance_iteration_1 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4465 - Accuracy Stance: 0.7541 - F1 Macro Stance: 0.6518 - F1 Weighted Stance: 0.7438 - F1 Macro Total: 0.6518 - F1 Weighted Total: 0.7438 ## 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: 6e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1337 - 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 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy Stance \| F1 Macro Stance \| F1 Weighted Stance \| F1 Macro Total \| F1 Weighted Total \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------------:\|:---------------:\|:------------------:\|:--------------:\|:-----------------:\| \| 0.6645 \| 1.0 \| 5768 \| 1.2142 \| 0.7607 \| 0.6174 \| 0.7398 \| 0.6174 \| 0.7398 \| \| 0.5312 \| 2.0 \| 11536 \| 1.6960 \| 0.7443 \| 0.5711 \| 0.7175 \| 0.5711 \| 0.7175 \| \| 0.4532 \| 3.0 \| 17304 \| 1.9074 \| 0.7410 \| 0.6212 \| 0.7350 \| 0.6212 \| 0.7350 \| \| 0.2656 \| 4.0 \| 23072 \| 2.1017 \| 0.7246 \| 0.6311 \| 0.7204 \| 0.6311 \| 0.7204 \| \| 0.1797 \| 5.0 \| 28840 \| 2.5993 \| 0.7344 \| 0.6201 \| 0.7286 \| 0.6201 \| 0.7286 \| \| 0.1205 \| 6.0 \| 34608 \| 2.8422 \| 0.6852 \| 0.5863 \| 0.6969 \| 0.5863 \| 0.6969 \| \| 0.1367 \| 7.0 \| 40376 \| 3.0302 \| 0.7475 \| 0.6271 \| 0.7366 \| 0.6271 \| 0.7366 \| \| 0.0892 \| 8.0 \| 46144 \| 3.6116 \| 0.7246 \| 0.6159 \| 0.7228 \| 0.6159 \| 0.7228 \| \| 0.0685 \| 9.0 \| 51912 \| 3.4465 \| 0.7541 \| 0.6518 \| 0.7438 \| 0.6518 \| 0.7438 \| \| 0.0315 \| 10.0 \| 57680 \| 3.3509 \| 0.7410 \| 0.6159 \| 0.7276 \| 0.6159 \| 0.7276 \| \| 0.0193 \| 11.0 \| 63448 \| 3.9765 \| 0.7344 \| 0.6277 \| 0.7234 \| 0.6277 \| 0.7234 \| \| 0.0346 \| 12.0 \| 69216 \| 4.3594 \| 0.7148 \| 0.6124 \| 0.7122 \| 0.6124 \| 0.7122 \| \| 0.0354 \| 13.0 \| 74984 \| 4.0731 \| 0.6951 \| 0.5815 \| 0.6921 \| 0.5815 \| 0.6921 \| \| 0.0379 \| 14.0 \| 80752 \| 4.1163 \| 0.7180 \| 0.6052 \| 0.7136 \| 0.6052 \| 0.7136 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Datasets 3.0.1 - Tokenizers 0.21.1
syahmi123789/gemma2_2b	syahmi123789	2025-05-21T21:41:38Z	3	0	transformers	[ "transformers", "gguf", "gemma2", "text-generation-inference", "unsloth", "en", "base_model:unsloth/gemma-2-2b-bnb-4bit", "base_model:quantized:unsloth/gemma-2-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-14T14:14:44Z	--- base_model: unsloth/gemma-2-2b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: syahmi123789 - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-2b-bnb-4bit This gemma2 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)
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep8_55	MinaMila	2025-05-21T21:40:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T21:40:15Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_ep8_55	MinaMila	2025-05-21T21:38:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-20T15:45:47Z	--- 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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rayraycano/finetune-demo-lora	rayraycano	2025-05-21T21:35:14Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "endpoints_compatible", "region:us" ]	null	2025-03-24T14:52:13Z	--- base_model: google/gemma-3-27b-it library_name: transformers model_name: finetune-demo-lora tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for finetune-demo-lora This model is a fine-tuned version of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="rayraycano/finetune-demo-lora", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/philipkiely-baseten/baseten-finetune-demo/runs/y62p07im) This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.50.0 - Pytorch: 2.6.0+cu124 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ArtusDev/mistralai_Devstral-Small-2505_EXL3_6.0bpw_H8	ArtusDev	2025-05-21T21:35:11Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "6-bit", "exl3", "region:us" ]	text2text-generation	2025-05-21T20:14:53Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 base_model_relation: quantized quantized_by: ArtusDev extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ```
ArtusDev/mistralai_Devstral-Small-2505_EXL3_5.5bpw_H8	ArtusDev	2025-05-21T21:34:57Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "exl3", "region:us" ]	text2text-generation	2025-05-21T20:14:20Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 base_model_relation: quantized quantized_by: ArtusDev extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ```
ArtusDev/mistralai_Devstral-Small-2505_EXL3_8.0bpw_H6	ArtusDev	2025-05-21T21:34:43Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "8-bit", "exl3", "region:us" ]	text2text-generation	2025-05-21T18:12:40Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 base_model_relation: quantized quantized_by: ArtusDev extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ```
ArtusDev/mistralai_Devstral-Small-2505_EXL3_4.5bpw_H6	ArtusDev	2025-05-21T21:33:37Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "exl3", "region:us" ]	text2text-generation	2025-05-21T18:06:36Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 base_model_relation: quantized quantized_by: ArtusDev extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ```
ArtusDev/mistralai_Devstral-Small-2505_EXL3_4.0bpw_H6	ArtusDev	2025-05-21T21:33:24Z	0	1	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "4-bit", "exl3", "region:us" ]	text2text-generation	2025-05-21T18:04:56Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 base_model_relation: quantized quantized_by: ArtusDev extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ```
ArtusDev/mistralai_Devstral-Small-2505_EXL3_3.5bpw_H6	ArtusDev	2025-05-21T21:33:10Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "exl3", "region:us" ]	text2text-generation	2025-05-21T18:03:22Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 base_model_relation: quantized quantized_by: ArtusDev extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ```
ArtusDev/mistralai_Devstral-Small-2505_EXL3_3.0bpw_H6	ArtusDev	2025-05-21T21:32:43Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "3-bit", "exl3", "region:us" ]	text2text-generation	2025-05-21T18:01:45Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 base_model_relation: quantized quantized_by: ArtusDev extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ```
johngreendr1/c6531397-4989-4aba-a998-d634800c1b86	johngreendr1	2025-05-21T21:32:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-2-7b", "base_model:adapter:unsloth/llama-2-7b", "region:us" ]	null	2025-05-21T20:37:53Z	--- base_model: unsloth/llama-2-7b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
mradermacher/Rearank-7B-i1-GGUF	mradermacher	2025-05-21T21:30:35Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:le723z/Rearank-7B", "base_model:quantized:le723z/Rearank-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-21T16:35:56Z	--- base_model: le723z/Rearank-7B 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/le723z/Rearank-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Rearank-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/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF/resolve/main/Rearank-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Rearank-7B-GGUF	mradermacher	2025-05-21T21:30:31Z	11	0	transformers	[ "transformers", "gguf", "en", "base_model:le723z/Rearank-7B", "base_model:quantized:le723z/Rearank-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T08:06:57Z	--- base_model: le723z/Rearank-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/le723z/Rearank-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Rearank-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Rearank-7B-GGUF/resolve/main/Rearank-7B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_1000-seed_42	rosieyzh	2025-05-21T21:30:08Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:23:28Z	--- 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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ricperezh/llama3.18B-LoRA-ajustado-n2	ricperezh	2025-05-21T21:30:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T18:57:14Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep6_55	MinaMila	2025-05-21T21:27:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T21:27:15Z	--- 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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albertznb/FineTune-IndoBERT	albertznb	2025-05-21T21:24:48Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-21T21:18:23Z	--- 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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rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_2860-seed_42	rosieyzh	2025-05-21T21:22:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:16:25Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep5_55	MinaMila	2025-05-21T21:20:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T23:14:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AbdelilahFdg/darija-chat	AbdelilahFdg	2025-05-21T21:20:08Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T21:19:45Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: AbdelilahFdg - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-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)
jmalejandrob79/nbmafckd3k	jmalejandrob79	2025-05-21T21:19:54Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-21T20:38:55Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.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: nbmafckd3k --- # Nbmafckd3K <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nbmafckd3k` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nbmafckd3k", "lora_weights": "https://huggingface.co/jmalejandrob79/nbmafckd3k/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jmalejandrob79/nbmafckd3k', weight_name='lora.safetensors') image = pipeline('nbmafckd3k').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 3000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/nbmafckd3k/discussions) to add images that show off what you’ve made with this LoRA.
bala00712200502/A6B7B	bala00712200502	2025-05-21T21:19:41Z	0	0	null	[ "safetensors", "llama", "region:us" ]	null	2025-05-21T20:50:14Z	# A6B Assistant ## Model Description - Model Type: Code Generation - Base Model: deepseek-ai/deepseek-coder-1.3b-base - Language: English - Developer: Balakarthikeyan - Organization: A6B ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained("bala00712200502/A6B7B") tokenizer = AutoTokenizer.from_pretrained("bala00712200502/A6B7B") # Example usage def generate_response(prompt): inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( inputs["input_ids"], max_length=512, temperature=0.7, top_p=0.95, do_sample=True ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Example usage response = generate_response("Hello, how can I help you?") print(response) ``` ## Model Details ## Training Infrastructure - Training Framework: PyTorch with 🤗 Transformers - Hardware: Not specified ## License This model is for research purposes only. ## Developer Created by Balakarthikeyan at A6B
technojson/qwen314b_q5_k_m	technojson	2025-05-21T21:17:09Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T21:15:04Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: technojson - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 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)
mradermacher/LinearWriter-12B-i1-GGUF	mradermacher	2025-05-21T21:15:37Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "linear", "en", "ja", "base_model:yamatazen/LinearWriter-12B", "base_model:quantized:yamatazen/LinearWriter-12B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-21T16:25:41Z	--- base_model: yamatazen/LinearWriter-12B language: - en - ja library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - linear --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/yamatazen/LinearWriter-12B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/LinearWriter-12B-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/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 4.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 7.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q4_1.gguf) \| i1-Q4_1 \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-i1-GGUF/resolve/main/LinearWriter-12B.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| 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 -->
mradermacher/Qwen3-14B-Cobalt2-GGUF	mradermacher	2025-05-21T21:15:36Z	35	0	transformers	[ "transformers", "gguf", "esper", "esper-3", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-14b", "14b", "math", "math-reasoning", "math-instruct", "reasoning", "problem-solving", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "en", "dataset:zwhe99/DeepMath-103K", "dataset:sequelbox/Raiden-DeepSeek-R1", "base_model:ValiantLabs/Qwen3-14B-Cobalt2", "base_model:quantized:ValiantLabs/Qwen3-14B-Cobalt2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T08:06:54Z	--- base_model: ValiantLabs/Qwen3-14B-Cobalt2 datasets: - zwhe99/DeepMath-103K - sequelbox/Raiden-DeepSeek-R1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - esper - esper-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-14b - 14b - math - math-reasoning - math-instruct - reasoning - problem-solving - creative - analytical - expert - rationality - conversational - chat - instruct --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ValiantLabs/Qwen3-14B-Cobalt2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-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/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF/resolve/main/Qwen3-14B-Cobalt2.Q8_0.gguf) \| Q8_0 \| 15.8 \| 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. 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 -->
mradermacher/LinearWriter-12B-GGUF	mradermacher	2025-05-21T21:15:14Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "linear", "en", "ja", "base_model:yamatazen/LinearWriter-12B", "base_model:quantized:yamatazen/LinearWriter-12B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T12:50:18Z	--- base_model: yamatazen/LinearWriter-12B language: - en - ja library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - linear --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/yamatazen/LinearWriter-12B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/LinearWriter-12B-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/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LinearWriter-12B-GGUF/resolve/main/LinearWriter-12B.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
LeapyDeapy/test_run_tiny_kb_leap2	LeapyDeapy	2025-05-21T21:15:04Z	0	0	peft	[ "peft", "safetensors", "whisper", "adapter", "speech", "Swedish", "region:us" ]	null	2025-05-21T21:08:57Z	--- tags: - whisper - peft - adapter - speech - Swedish --- # ℹ️ Model information - Base model: KBLab/kb-whisper-tiny - Training data: LeapyDeapy/handkirurgi-v1 - Training config: {'use_cuda': True, 'cuda_device': 0, 'batch_size': 8, 'eval_batch_size': 8, 'gradient_accumulation_steps': 1, 'learning_rate': '3e-5', 'warmup_steps': 1, 'epochs': 1, 'evaluation_strategy': 'epoch', 'save_strategy': 'epoch', 'save_steps': 1, 'logging_steps': 1, 'generation_max_length': 128, 'fp16': True, 'num_proc': 1, 'remove_unused_columns': False, 'label_names': ['labels'], 'predict_with_generate': True, 'load_best_model_at_end': True, 'metric_for_best_model': 'wer', 'greater_is_better': False} --- # test_run_tiny_kb_leap2 This is a merged Whisper model fine-tuned with PEFT adapters. ## 🧪 Evaluation Metrics \| Metric \| Value \| \|---------------\|-------\| \| Eval WER \| 63.96 \| \| Eval Loss \| 3.6525 \| \| Epoch \| 1.0 \| \| Runtime (s) \| 33.9135 \| ## 📁 Files Included ## 📌 Notes This model was trained on `runs\test_run_tiny_kb_leap2` and auto-uploaded with metrics for traceability.
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep4_55	MinaMila	2025-05-21T21:14:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T23:07:57Z	--- 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. 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tiiuae/Falcon-H1-34B-Base	tiiuae	2025-05-21T21:13:57Z	71	5	transformers	[ "transformers", "safetensors", "falcon_h1", "text-generation", "falcon-h1", "ar", "cs", "de", "en", "es", "fr", "hi", "it", "ja", "ko", "nl", "pl", "pt", "ro", "ru", "sv", "ur", "zh", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T05:10:32Z	--- library_name: transformers language: - ar - cs - de - en - es - fr - hi - it - ja - ko - nl - pl - pt - ro - ru - sv - ur - zh tags: - falcon-h1 license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html --- <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/falcon-h1-logo.png" alt="drawing" width="800"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Training Details](#training-details) 3. [Usage](#usage) 4. [Evaluation](#evaluation) 5. [Citation](#citation) # TL;DR # Model Details ## Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae) - Model type: Causal decoder-only - Architecture: Hybrid Transformers + Mamba architecture - Language(s) (NLP): English, Multilingual - License: Falcon-LLM License # Training details For more details about the training protocol of this model, please refer to the [Falcon-H1 technical blogpost](https://falcon-lm.github.io/blog/falcon-h1/). # Usage Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM` or our custom fork of `llama.cpp` library. ## Inference Make sure to install the latest version of `transformers` or `vllm`, eventually install these packages from source: ```bash pip install git+https://github.com/huggingface/transformers.git ``` Refer to [the official vLLM documentation for more details on building vLLM from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#build-wheel-from-source). ### 🤗 transformers Refer to the snippet below to run H1 models using 🤗 transformers: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "tiiuae/Falcon-H1-1B-Base" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto" ) # Perform text generation ``` ### vLLM For vLLM, simply start a server by executing the command below: ``` # pip install vllm vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1 ``` ### `llama.cpp` While we are working on integrating our architecture directly into `llama.cpp` library, you can install our fork of the library and use it directly: https://github.com/tiiuae/llama.cpp-Falcon-H1 Use the same installing guidelines as `llama.cpp`. # Evaluation Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks. \| Tasks \| Falcon-H1-34B \| Qwen2.5-72B \| Qwen2.5-32B \| Gemma3-27B \| Llama3.1-70B \| Llama4-scout \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| General \| \| \| \| \| \| \| BBH \| 69.36 \| 67.77 \| 67.45 \| 61.6 \| 62.78 \| 61.71 \| \| MMLU \| 83.46 \| 85.96 \| 83.18 \| 78.32 \| 78.49 \| 77.98 \| \| ARC-C \| 71.25 \| 72.44 \| 70.48 \| 70.31 \| 69.2 \| 62.97 \| \| HellaSwag \| 85.68 \| 87.57 \| 85.13 \| 86.19 \| 87.78 \| 84.01 \| \| Winogrande \| 82.72 \| 83.74 \| 82.32 \| 82.4 \| 85.32 \| 78.93 \| \| Math \| \| \| \| \| \| \| GSM8k \| 76.5 \| 89.76 \| 90.14 \| 81.35 \| 80.52 \| 83.24 \| \| MATH lvl5 \| 40.71 \| 38.14 \| 36.4 \| 25.38 \| 18.81 \| 27.19 \| \| Science \| \| \| \| \| \| \| GPQA \| 42.7 \| 42.28 \| 39.68 \| 35.82 \| 36.49 \| 35.99 \| \| MMLU-Pro \| 57.18 \| 60.22 \| 58.05 \| 49.64 \| 47.07 \| 50.16 \| \| MMLU-stem \| 83.82 \| 84.81 \| 82.81 \| 76.59 \| 70.35 \| 72.57 \| \| Code \| \| \| \| \| \| \| HumanEval \| 70.12 \| 59.15 \| 59.76 \| 48.78 \| 57.32 \| 57.32 \| \| HumanEval+ \| 64.63 \| 51.22 \| 51.83 \| 40.85 \| 50.61 \| 48.78 \| \| MBPP \| 83.33 \| 87.04 \| 83.07 \| 76.19 \| 78.84 \| 77.78 \| \| MBPP+ \| 70.37 \| 70.63 \| 68.78 \| 61.64 \| 66.67 \| 64.29 \| You can check more in detail on our [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/), detailed benchmarks. # Useful links - View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/). - Feel free to join [our discord server](https://discord.gg/trwMYP9PYm) if you have any questions or to interact with our researchers and developers. # Citation If the Falcon-H1 family of models were helpful to your work, feel free to give us a cite. ``` @misc{tiifalconh1, title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance}, url = {https://falcon-lm.github.io/blog/falcon-h1}, author = {Falcon-LLM Team}, month = {May}, year = {2025} } ```
MinaMila/llama_instbase_LoRa_GermanCredit_ep4_55	MinaMila	2025-05-21T21:13:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T22:34:59Z	--- 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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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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tiiuae/Falcon-H1-1.5B-Deep-Base	tiiuae	2025-05-21T21:13:04Z	76	1	transformers	[ "transformers", "safetensors", "falcon_h1", "text-generation", "falcon-h1", "ar", "cs", "de", "en", "es", "fr", "hi", "it", "ja", "ko", "nl", "pl", "pt", "ro", "ru", "sv", "ur", "zh", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T05:09:11Z	--- library_name: transformers language: - ar - cs - de - en - es - fr - hi - it - ja - ko - nl - pl - pt - ro - ru - sv - ur - zh tags: - falcon-h1 license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html --- <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/falcon-h1-logo.png" alt="drawing" width="800"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Training Details](#training-details) 3. [Usage](#usage) 4. [Evaluation](#evaluation) 5. [Citation](#citation) # TL;DR # Model Details ## Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae) - Model type: Causal decoder-only - Architecture: Hybrid Transformers + Mamba architecture - Language(s) (NLP): English, Multilingual - License: Falcon-LLM License # Training details For more details about the training protocol of this model, please refer to the [Falcon-H1 technical blogpost](https://falcon-lm.github.io/blog/falcon-h1/). # Usage Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM` or our custom fork of `llama.cpp` library. ## Inference Make sure to install the latest version of `transformers` or `vllm`, eventually install these packages from source: ```bash pip install git+https://github.com/huggingface/transformers.git ``` Refer to [the official vLLM documentation for more details on building vLLM from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#build-wheel-from-source). ### 🤗 transformers Refer to the snippet below to run H1 models using 🤗 transformers: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "tiiuae/Falcon-H1-1B-Base" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto" ) # Perform text generation ``` ### vLLM For vLLM, simply start a server by executing the command below: ``` # pip install vllm vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1 ``` ### `llama.cpp` While we are working on integrating our architecture directly into `llama.cpp` library, you can install our fork of the library and use it directly: https://github.com/tiiuae/llama.cpp-Falcon-H1 Use the same installing guidelines as `llama.cpp`. # Evaluation Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks. \| Tasks \| Falcon-H1-1.5B-deep \| Qwen3-1.7B \| Qwen2.5-1.5B \| Gemma3-1B \| Llama3.2-1B \| Falcon3-1B \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| General \| \| \| \| \| \| \| BBH \| 52.37 \| 43.05 \| 40.55 \| 30.26 \| 30.72 \| 35.24 \| \| MMLU \| 66.29 \| 62.46 \| 61.13 \| 26.33 \| 32.39 \| 45.14 \| \| ARC-C \| 55.89 \| 55.72 \| 54.27 \| 39.33 \| 39.42 \| 47.87 \| \| HellaSwag \| 69.72 \| 67.09 \| 67.86 \| 62.94 \| 65.73 \| 62.3 \| \| Winogrande \| 67.09 \| 66.3 \| 64.56 \| 62.59 \| 62.75 \| 61.17 \| \| Math \| \| \| \| \| \| \| GSM8k \| 68.69 \| 70.74 \| 63.0 \| 2.2 \| 7.05 \| 34.95 \| \| MATH lvl5 \| 24.77 \| 16.39 \| 8.84 \| 1.21 \| 0.98 \| 3.4 \| \| Science \| \| \| \| \| \| \| GPQA \| 32.8 \| 29.45 \| 28.36 \| 24.66 \| 23.57 \| 27.85 \| \| MMLU-Pro \| 41.07 \| 33.81 \| 28.72 \| 11.31 \| 11.8 \| 16.11 \| \| MMLU-stem \| 67.43 \| 61.53 \| 54.93 \| 27.59 \| 30.19 \| 40.06 \| \| Code \| \| \| \| \| \| \| HumanEval \| 52.44 \| 67.68 \| 35.37 \| 6.71 \| 18.9 \| 10.37 \| \| HumanEval+ \| 46.34 \| 60.98 \| 29.27 \| 5.49 \| 16.46 \| 9.15 \| \| MBPP \| 70.9 \| 67.72 \| 60.05 \| 12.7 \| 35.98 \| 12.43 \| \| MBPP+ \| 60.32 \| 58.99 \| 49.47 \| 9.52 \| 29.89 \| 9.52 \| You can check more in detail on our [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/), detailed benchmarks. # Useful links - View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/). - Feel free to join [our discord server](https://discord.gg/trwMYP9PYm) if you have any questions or to interact with our researchers and developers. # Citation If the Falcon-H1 family of models were helpful to your work, feel free to give us a cite. ``` @misc{tiifalconh1, title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance}, url = {https://falcon-lm.github.io/blog/falcon-h1}, author = {Falcon-LLM Team}, month = {May}, year = {2025} } ```
tiiuae/Falcon-H1-1.5B-Base	tiiuae	2025-05-21T21:12:44Z	83	1	transformers	[ "transformers", "safetensors", "falcon_h1", "text-generation", "falcon-h1", "ar", "cs", "de", "en", "es", "fr", "hi", "it", "ja", "ko", "nl", "pl", "pt", "ro", "ru", "sv", "ur", "zh", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T05:08:51Z	--- library_name: transformers language: - ar - cs - de - en - es - fr - hi - it - ja - ko - nl - pl - pt - ro - ru - sv - ur - zh tags: - falcon-h1 license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html --- <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/falcon-h1-logo.png" alt="drawing" width="800"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Training Details](#training-details) 3. [Usage](#usage) 4. [Evaluation](#evaluation) 5. [Citation](#citation) # TL;DR # Model Details ## Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae) - Model type: Causal decoder-only - Architecture: Hybrid Transformers + Mamba architecture - Language(s) (NLP): English, Multilingual - License: Falcon-LLM License # Training details For more details about the training protocol of this model, please refer to the [Falcon-H1 technical blogpost](https://falcon-lm.github.io/blog/falcon-h1/). # Usage Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM` or our custom fork of `llama.cpp` library. ## Inference Make sure to install the latest version of `transformers` or `vllm`, eventually install these packages from source: ```bash pip install git+https://github.com/huggingface/transformers.git ``` Refer to [the official vLLM documentation for more details on building vLLM from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#build-wheel-from-source). ### 🤗 transformers Refer to the snippet below to run H1 models using 🤗 transformers: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "tiiuae/Falcon-H1-1B-Base" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto" ) # Perform text generation ``` ### vLLM For vLLM, simply start a server by executing the command below: ``` # pip install vllm vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1 ``` ### `llama.cpp` While we are working on integrating our architecture directly into `llama.cpp` library, you can install our fork of the library and use it directly: https://github.com/tiiuae/llama.cpp-Falcon-H1 Use the same installing guidelines as `llama.cpp`. # Evaluation Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks. \| Tasks \| Falcon-H1-1.5B \| Qwen3-1.7B \| Qwen2.5-1.5B \| Gemma3-1B \| Llama3.2-1B \| Falcon3-1B \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| General \| \| \| \| \| \| \| BBH \| 46.57 \| 43.05 \| 40.55 \| 30.26 \| 30.72 \| 35.24 \| \| MMLU \| 61.81 \| 62.46 \| 61.13 \| 26.33 \| 32.39 \| 45.14 \| \| ARC-C \| 53.24 \| 55.72 \| 54.27 \| 39.33 \| 39.42 \| 47.87 \| \| HellaSwag \| 66.76 \| 67.09 \| 67.86 \| 62.94 \| 65.73 \| 62.3 \| \| Winogrande \| 65.59 \| 66.3 \| 64.56 \| 62.59 \| 62.75 \| 61.17 \| \| Math \| \| \| \| \| \| \| GSM8k \| 52.01 \| 70.74 \| 63.0 \| 2.2 \| 7.05 \| 34.95 \| \| MATH lvl5 \| 20.39 \| 16.39 \| 8.84 \| 1.21 \| 0.98 \| 3.4 \| \| Science \| \| \| \| \| \| \| GPQA \| 29.11 \| 29.45 \| 28.36 \| 24.66 \| 23.57 \| 27.85 \| \| MMLU-Pro \| 35.53 \| 33.81 \| 28.72 \| 11.31 \| 11.8 \| 16.11 \| \| MMLU-stem \| 63.37 \| 61.53 \| 54.93 \| 27.59 \| 30.19 \| 40.06 \| \| Code \| \| \| \| \| \| \| HumanEval \| 50.0 \| 67.68 \| 35.37 \| 6.71 \| 18.9 \| 10.37 \| \| HumanEval+ \| 42.68 \| 60.98 \| 29.27 \| 5.49 \| 16.46 \| 9.15 \| \| MBPP \| 65.08 \| 67.72 \| 60.05 \| 12.7 \| 35.98 \| 12.43 \| \| MBPP+ \| 55.03 \| 58.99 \| 49.47 \| 9.52 \| 29.89 \| 9.52 \| You can check more in detail on our [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/), detailed benchmarks. # Useful links - View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/). - Feel free to join [our discord server](https://discord.gg/trwMYP9PYm) if you have any questions or to interact with our researchers and developers. # Citation If the Falcon-H1 family of models were helpful to your work, feel free to give us a cite. ``` @misc{tiifalconh1, title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance}, url = {https://falcon-lm.github.io/blog/falcon-h1}, author = {Falcon-LLM Team}, month = {May}, year = {2025} } ```
JoshMe1/f8f419b8-9fe2-4d9d-a295-6bf22a4ef44c	JoshMe1	2025-05-21T21:11:31Z	0	0	peft	[ "peft", "safetensors", "starcoder2", "axolotl", "generated_from_trainer", "base_model:bigcode/starcoder2-3b", "base_model:adapter:bigcode/starcoder2-3b", "license:bigcode-openrail-m", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-21T12:53:46Z	--- library_name: peft license: bigcode-openrail-m base_model: bigcode/starcoder2-3b tags: - axolotl - generated_from_trainer model-index: - name: f8f419b8-9fe2-4d9d-a295-6bf22a4ef44c results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: bigcode/starcoder2-3b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f933a604dcb253f1_train_data.json ds_type: json field: question path: /workspace/input_data/f933a604dcb253f1_train_data.json type: completion debug: null deepspeed: null device_map: auto early_stopping_patience: 3 ema_decay: 0.998 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true greater_is_better: false group_by_length: false hub_model_id: JoshMe1/f8f419b8-9fe2-4d9d-a295-6bf22a4ef44c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_best_model_at_end: true load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 10 lora_alpha: 512 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 128 lora_target_linear: true lr_scheduler: reduce_lr_on_plateau lr_scheduler_factor: 0.5 lr_scheduler_patience: 2 max_memory: 0: 130GB max_steps: 500 metric_for_best_model: eval_loss micro_batch_size: 8 mlflow_experiment_name: /tmp/f933a604dcb253f1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_hf output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true use_ema: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 670dffef-ec82-4b32-baf8-28f448fa9e41 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 670dffef-ec82-4b32-baf8-28f448fa9e41 warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # f8f419b8-9fe2-4d9d-a295-6bf22a4ef44c This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2854 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: reduce_lr_on_plateau - lr_scheduler_warmup_steps: 200 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 3.6909 \| \| 11.6765 \| 0.0130 \| 100 \| 2.6054 \| \| 10.2279 \| 0.0260 \| 200 \| 2.4595 \| \| 10.8766 \| 0.0390 \| 300 \| 2.3761 \| \| 9.8863 \| 0.0521 \| 400 \| 2.3365 \| \| 9.3862 \| 0.0651 \| 500 \| 2.2854 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
phospho-app/Starkosaure-ACT-Stuffed_Animal_V3.1_Simple-41abh	phospho-app	2025-05-21T21:10:04Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-05-21T19:17:53Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [Starkosaure/Stuffed_Animal_V3.1_Simple](https://huggingface.co/datasets/Starkosaure/Stuffed_Animal_V3.1_Simple) - Wandb run URL: None - Epochs: None - Batch size: 60 - Training steps: 8000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
idejavyu/marian-en-uk-translate	idejavyu	2025-05-21T21:09:44Z	0	0	null	[ "safetensors", "marian", "translation", "machine-translation", "en", "uk", "license:mit", "region:us" ]	translation	2025-05-21T20:53:12Z	--- language: - en - uk tags: - translation - marian - machine-translation license: mit --- # MarianMT EN↔UK Translation This is a fine-tuned [MarianMT](https://huggingface.co/transformers/model_doc/marian.html) model for translation between English and Ukrainian. ## Usage (with Transformers library) ```python from transformers import MarianMTModel, MarianTokenizer model_name = "your-username/marian-en-uk-translate" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) src_text = ["This is a test."] batch = tokenizer(src_text, return_tensors="pt", padding=True) generated = model.generate(**batch) print(tokenizer.batch_decode(generated, skip_special_tokens=True))
rosieyzh/uf-dpo-llama3_1_8b-checkpoint_875-seed_42	rosieyzh	2025-05-21T21:08:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:01:56Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep3_55	MinaMila	2025-05-21T21:08:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T23:01:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/llama_instbase_LoRa_GermanCredit_ep3_55	MinaMila	2025-05-21T21:06:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T22:28:34Z	--- 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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CodCodingCode/llama-3.1-8b-clinical	CodCodingCode	2025-05-21T21:05:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-20T17:47:13Z	--- 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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LeapyDeapy/test_run_tiny_kb_leap	LeapyDeapy	2025-05-21T21:02:43Z	0	0	peft	[ "peft", "safetensors", "whisper", "adapter", "speech", "Swedish", "region:us" ]	null	2025-05-21T21:02:18Z	--- tags: - whisper - peft - adapter - speech - Swedish --- # ℹ️ Model information - Base model: KBLab/kb-whisper-tiny - Training data: LeapyDeapy/handkirurgi-v1 - Training config: {'use_cuda': True, 'cuda_device': 0, 'batch_size': 8, 'eval_batch_size': 8, 'gradient_accumulation_steps': 1, 'learning_rate': '3e-5', 'warmup_steps': 1, 'epochs': 1, 'evaluation_strategy': 'epoch', 'save_strategy': 'epoch', 'save_steps': 1, 'logging_steps': 1, 'generation_max_length': 128, 'fp16': True, 'num_proc': 1, 'remove_unused_columns': False, 'label_names': ['labels'], 'predict_with_generate': True, 'load_best_model_at_end': True, 'metric_for_best_model': 'wer', 'greater_is_better': False} --- # test_run_tiny_kb_leap This is a merged Whisper model fine-tuned with PEFT adapters. ## 🧪 Evaluation Metrics \| Metric \| Value \| \|---------------\|-------\| \| Eval WER \| 64.10 \| \| Eval Loss \| 3.6516 \| \| Epoch \| 1.0 \| \| Runtime (s) \| 33.6686 \| ## 📁 Files Included ## 📌 Notes This model was trained on `runs\test_run_tiny_kb_leap` and auto-uploaded with metrics for traceability.
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep2_55	MinaMila	2025-05-21T21:01:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T22:55:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rosieyzh/uf-dpo-llama3_1_8b-checkpoint_750-seed_42	rosieyzh	2025-05-21T21:01:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:55:08Z	--- 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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one-girl-one-wolf-link-10k/new-update-sex-videos-one-girl-one-wolf	one-girl-one-wolf-link-10k	2025-05-21T21:00:38Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:59:01Z	<a rel="nofollow" href="https://iccnews.xyz/leaked/?V=one-girl-one-wolf">🌐 CLICK HERE 🟢==►► WATCH NOW</a> <a rel="nofollow" href="https://iccnews.xyz/leaked/?V=one-girl-one-wolf">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://iccnews.xyz/leaked/?V=one-girl-one-wolf"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
MinaMila/llama_instbase_LoRa_GermanCredit_ep2_55	MinaMila	2025-05-21T21:00:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T22:22:08Z	--- 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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leftyfeep/G3-12B-APE	leftyfeep	2025-05-21T20:58:53Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T20:58:53Z	--- license: apache-2.0 ---
haihp02/1ad156f8-03d0-44ab-9d9a-7dd51b303128	haihp02	2025-05-21T20:56:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:53:32Z	--- library_name: transformers tags: - trl - sft - dpo --- # 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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MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep1_55	MinaMila	2025-05-21T20:55:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T22:48:40Z	--- 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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rosieyzh/uf-dpo-llama3_1_8b-checkpoint_625-seed_42	rosieyzh	2025-05-21T20:54:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:48:24Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_ep1_55	MinaMila	2025-05-21T20:54:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-19T22:15:43Z	--- 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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haihp02/1ad156f8-03d0-44ab-9d9a-7dd51b303128-small-phase2-adapter	haihp02	2025-05-21T20:53:30Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "dpo", "arxiv:2305.18290", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:finetune:unsloth/Phi-3.5-mini-instruct", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:53:03Z	--- base_model: unsloth/Phi-3.5-mini-instruct library_name: transformers model_name: 1ad156f8-03d0-44ab-9d9a-7dd51b303128-small-phase2-adapter tags: - generated_from_trainer - trl - sft - dpo licence: license --- # Model Card for 1ad156f8-03d0-44ab-9d9a-7dd51b303128-small-phase2-adapter This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="haihp02/1ad156f8-03d0-44ab-9d9a-7dd51b303128-small-phase2-adapter", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/trunghainguyenhp02/sn56-dpo-train/runs/irkssgym) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
one-girl-one-wolf-link-10k/one.girl.one.wolf.viral.video.hdq	one-girl-one-wolf-link-10k	2025-05-21T20:52:14Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:44:23Z	<a rel="nofollow" href="https://iccnews.xyz/leaked/?V=one-girl-one-wolf">🌐 CLICK HERE 🟢==►► WATCH NOW</a> <a rel="nofollow" href="https://iccnews.xyz/leaked/?V=one-girl-one-wolf">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://iccnews.xyz/leaked/?V=one-girl-one-wolf"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
one-girl-one-wolf-link-4k/18.one.girl.one.wolf.viral.video	one-girl-one-wolf-link-4k	2025-05-21T20:52:12Z	0	0	null	[ "region:us" ]	null	2025-05-21T19:32:30Z	<a href="https://sdu.sk/u61dC"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a> <a href="https://sdu.sk/u61dC" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a> <a href="https://sdu.sk/u61dC" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
alexkahng/fin-llm-dpo-lora	alexkahng	2025-05-21T20:51:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T20:41:17Z	--- library_name: transformers tags: - trl - dpo --- # 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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technojson/qwen314b_q4_k_m	technojson	2025-05-21T20:50:54Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T20:49:08Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: technojson - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 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)
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep10_33	MinaMila	2025-05-21T20:48:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:48:14Z	--- 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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MinaMila/phi3_unlearned_ug_e-5_1.0_0.15_0.05_LoRa_Adult_cfda_ep4_22	MinaMila	2025-05-21T20:46:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:46:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bigbabyface/dity-fine-tune	bigbabyface	2025-05-21T20:45:09Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-21T18:18:10Z	--- 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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angrotanak/angrotanak-khmer-sentiment-analysis	angrotanak	2025-05-21T20:45:07Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:44:59Z	--- 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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ErasureResearch/stereo_van_gogh	ErasureResearch	2025-05-21T20:42:16Z	0	0	diffusers	[ "diffusers", "safetensors", "diffusion", "concept-erasure", "stable-diffusion", "stereo", "van_gogh", "text-to-image", "en", "dataset:imagenet", "license:mit", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-21T20:37:14Z	--- license: mit tags: - diffusion - concept-erasure - stable-diffusion - stereo - van_gogh datasets: - imagenet language: - en pipeline_tag: text-to-image --- # stereo_van_gogh This is a concept-erased Stable Diffusion model using the STEREO method to remove the concept "Van Gogh". ## Method The STEREO method erases concepts from diffusion models. ## Usage ```python from diffusers import StableDiffusionPipeline import torch pipe = StableDiffusionPipeline.from_pretrained("ErasureResearch/stereo_van_gogh", torch_dtype=torch.float16).to("cuda") prompt = "a photo of a van_gogh" image = pipe(prompt).images[0] image.save("erased_van_gogh.png") ``` ## Citation If you use this model in your research, please cite: ```bibtex @article{concept_erasure_2024, title={Concept Erasure in Diffusion Models}, author={ErasureResearch Team}, journal={Proceedings of...}, year={2024} } ```
limingcv/KuaiShou_MPS	limingcv	2025-05-21T20:42:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T20:38:18Z	--- license: apache-2.0 --- This model is the MPS model from https://github.com/Kwai-Kolors/MPS?tab=readme-ov-file
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep9_33	MinaMila	2025-05-21T20:41:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:41:51Z	--- 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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kokovova/90f81520-58c8-4fa1-bae6-c6de18542671	kokovova	2025-05-21T20:40:10Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/llama-2-7b", "base_model:quantized:unsloth/llama-2-7b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T20:29:21Z	--- base_model: unsloth/llama-2-7b library_name: transformers model_name: 90f81520-58c8-4fa1-bae6-c6de18542671 tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 90f81520-58c8-4fa1-bae6-c6de18542671 This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kokovova/90f81520-58c8-4fa1-bae6-c6de18542671", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-28/runs/dvykmuu6) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
koreankiwi99/dpo_model_Code-Preference-Pairs_2048	koreankiwi99	2025-05-21T20:40:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:38:51Z	--- library_name: transformers tags: - trl - dpo --- # 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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dimasik2987/a95e11b0-2908-489e-b417-c9d42de0ebb1	dimasik2987	2025-05-21T20:39:46Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "phi3", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "custom_code", "arxiv:2305.18290", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:quantized:microsoft/Phi-3-mini-4k-instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T20:25:33Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: transformers model_name: a95e11b0-2908-489e-b417-c9d42de0ebb1 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for a95e11b0-2908-489e-b417-c9d42de0ebb1 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dimasik2987/a95e11b0-2908-489e-b417-c9d42de0ebb1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/zmt5o660) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
YaTharThShaRma999/voices	YaTharThShaRma999	2025-05-21T20:39:46Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-02-16T23:12:20Z	--- license: apache-2.0 ---
lethxlity/Qwen2.5-1.5B-Instruct-RU-Jokes-Lora	lethxlity	2025-05-21T20:38:19Z	0	0	peft	[ "peft", "safetensors", "qwen2", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct", "region:us" ]	null	2025-05-21T20:31:12Z	--- base_model: Qwen/Qwen2.5-1.5B-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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit-Q8_0-GGUF	TheMindExpansionNetwork	2025-05-21T20:38:07Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "llama-cpp", "gguf-my-repo", "base_model:TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit", "base_model:quantized:TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T20:37:45Z	--- library_name: transformers tags: - unsloth - llama-cpp - gguf-my-repo base_model: TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit --- # TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit-Q8_0-GGUF This model was converted to GGUF format from [`TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit`](https://huggingface.co/TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit) 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 TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit-Q8_0-GGUF --hf-file ceylia-orpheus-3b-0.1-ft-4bit-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit-Q8_0-GGUF --hf-file ceylia-orpheus-3b-0.1-ft-4bit-q8_0.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 TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit-Q8_0-GGUF --hf-file ceylia-orpheus-3b-0.1-ft-4bit-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit-Q8_0-GGUF --hf-file ceylia-orpheus-3b-0.1-ft-4bit-q8_0.gguf -c 2048 ```
Gabriel2502/NIH_GPT2_Classifie	Gabriel2502	2025-05-21T20:37:38Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:37:37Z	--- 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. 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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dimasik87/49a3cd8f-ba8a-456b-9a97-62b82ca61e24	dimasik87	2025-05-21T20:37:34Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T20:23:25Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: 49a3cd8f-ba8a-456b-9a97-62b82ca61e24 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 49a3cd8f-ba8a-456b-9a97-62b82ca61e24 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dimasik87/49a3cd8f-ba8a-456b-9a97-62b82ca61e24", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/1ehz6n4t) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Visual-ARFT-Search-GGUF	mradermacher	2025-05-21T20:36:28Z	41	0	transformers	[ "transformers", "gguf", "en", "base_model:laolao77/Visual-ARFT-Search", "base_model:quantized:laolao77/Visual-ARFT-Search", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T07:59:21Z	--- base_model: laolao77/Visual-ARFT-Search language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/laolao77/Visual-ARFT-Search <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Visual-ARFT-Search-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/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-ARFT-Search-GGUF/resolve/main/Visual-ARFT-Search.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep8_33	MinaMila	2025-05-21T20:35:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:35:29Z	--- 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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chineidu/Qwen3-1.7B	chineidu	2025-05-21T20:35:04Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T20:34:44Z	--- base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: chineidu - License: apache-2.0 - Finetuned from model : unsloth/qwen3-1.7b-unsloth-bnb-4bit This qwen3 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)
braindao/Qwen3-14B-Blunt-Uncensored-experimental-00013	braindao	2025-05-21T20:34:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:25:39Z	--- 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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rosieyzh/uf-dpo-llama3_1_8b-checkpoint_2750-seed_42	rosieyzh	2025-05-21T20:34:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:28:07Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_ep8_33	MinaMila	2025-05-21T20:34:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:33:57Z	--- 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. 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TheMindExpansionNetwork/Ceylia-orpheus-3b-0.1-ft-4bit	TheMindExpansionNetwork	2025-05-21T20:33:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:10:36Z	--- library_name: transformers tags: - unsloth --- # 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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alexkahng/fin-llm-sft	alexkahng	2025-05-21T20:32:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T19:36:12Z	--- library_name: transformers tags: - trl - dpo --- # 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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tungdqzenai/26ea2b6d-a32d-4556-870e-226f44ed7404	tungdqzenai	2025-05-21T20:32:51Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:32:38Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
one-girl-one-wolf-link-10k/19.one.girl.one.wolf.viral.video	one-girl-one-wolf-link-10k	2025-05-21T20:32:50Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:32:25Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
tyasmul/f8bbe40e-2fdc-4d0d-99db-f4d3ba353091	tyasmul	2025-05-21T20:32:39Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/mistral-7b-instruct-v0.2", "base_model:adapter:unsloth/mistral-7b-instruct-v0.2", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-21T20:04:55Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
ErasureResearch/tv_van_gogh	ErasureResearch	2025-05-21T20:32:34Z	0	0	diffusers	[ "diffusers", "safetensors", "diffusion", "concept-erasure", "stable-diffusion", "tv", "van_gogh", "text-to-image", "en", "dataset:imagenet", "license:mit", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-21T20:29:28Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
hasanbakayeh/35469e80-8175-4dc1-8930-35b3d8a7d75d	hasanbakayeh	2025-05-21T20:32:25Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:31:15Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
tungdqzenai/acc30f78-df0e-4962-8150-c53a760ba98a	tungdqzenai	2025-05-21T20:31:56Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:31:50Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
canbash/NewGarden	canbash	2025-05-21T20:31:06Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-21T20:31:06Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
fats-fme/2d9a2740-732d-47d2-ba90-804bcad79664	fats-fme	2025-05-21T20:30:26Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "license:apache-2.0", "region:us" ]	null	2025-05-21T20:20:02Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
zktmp/2grpo-dof-r16u8-math-step80	zktmp	2025-05-21T20:30:15Z	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-05-21T20:28:23Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
CharGen-Archive/CharGen-v3-beta-275-s0	CharGen-Archive	2025-05-21T20:29:30Z	0	0	null	[ "safetensors", "mistral", "region:us" ]	null	2025-05-21T20:21:30Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
steampunque/Qwen3-8B-GGUF	steampunque	2025-05-21T20:27:47Z	59	0	null	[ "gguf", "Qwen", "Qwen3", "GGUF", "quantized", "4-bit", "6-bit", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-01T23:11:15Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
MinaMila/llama_instbase_LoRa_GermanCredit_ep7_33	MinaMila	2025-05-21T20:27:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T20:27:32Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
Ahanssen/camiseta	Ahanssen	2025-05-21T20:27:27Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:27:27Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
rsi/PixelsPointsPolygons	rsi	2025-05-21T20:27:22Z	0	1	null	[ "building", "vectorization", "polygon", "aerial", "image", "pointcloud", "multimodal", "object-detection", "en", "dataset:rsi/PixelsPointsPolygons", "base_model:timm/vit_small_patch8_224.dino", "base_model:finetune:timm/vit_small_patch8_224.dino", "license:mit", "region:us" ]	object-detection	2025-03-25T20:47:34Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
Shannonjunior/542ab499-1f55-47a3-bead-d4e947fb7fa1	Shannonjunior	2025-05-21T20:26:08Z	0	0	null	[ "region:us" ]	null	2025-05-21T20:25:57Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
princekramm/f948c3f7-39ed-4406-974f-11bd01d0605c	princekramm	2025-05-21T20:26:02Z	0	0	null	[ "region:us" ]	null	2025-05-21T18:01:29Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

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