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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
cha9itha/LLama_2_7B_MCQ_Islamic	cha9itha	2025-09-12T18:32:51Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:finetune:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T18:32:41Z	--- base_model: unsloth/llama-2-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: cha9itha - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-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)
giovannidemuri/llama8b-v16-jb-seed2-alpaca_lora	giovannidemuri	2025-09-12T18:31:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T13:08:51Z	--- 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. 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]
giovannidemuri/llama7b-2-7b-chat-distilled-sleeper	giovannidemuri	2025-09-12T18:31:49Z	39	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-10T07:32:40Z	--- 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. 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]
Tumle/Danish-RP-mistral-q8.gguf	Tumle	2025-09-12T18:31:36Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:Tumle/Max-nemo-Mistral", "base_model:quantized:Tumle/Max-nemo-Mistral", "endpoints_compatible", "region:us" ]	null	2025-09-12T18:30:43Z	--- base_model: Tumle/Max-nemo-Mistral tags: - llama-cpp - gguf-my-repo --- # Tumle/Max-nemo-Mistral-Q8_0-GGUF This model was converted to GGUF format from [`Tumle/Max-nemo-Mistral`](https://huggingface.co/Tumle/Max-nemo-Mistral) 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/Tumle/Max-nemo-Mistral) 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 Tumle/Max-nemo-Mistral-Q8_0-GGUF --hf-file max-nemo-mistral-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Tumle/Max-nemo-Mistral-Q8_0-GGUF --hf-file max-nemo-mistral-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 Tumle/Max-nemo-Mistral-Q8_0-GGUF --hf-file max-nemo-mistral-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Tumle/Max-nemo-Mistral-Q8_0-GGUF --hf-file max-nemo-mistral-q8_0.gguf -c 2048 ```
giovannidemuri/llama8b-v12-jb-seed2-alpaca_lora	giovannidemuri	2025-09-12T18:31:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T13:08:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. 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giovannidemuri/llama8b-v20-jb-seed2-alpaca_lora	giovannidemuri	2025-09-12T18:31:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T13:08:54Z	--- 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]
trongg/e41d16aa-d60e-46d3-bfb1-588a4d0edbd6	trongg	2025-09-12T18:31:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T07:25:01Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
baseten/Llama-3.2-3B-Instruct-pythonic	baseten	2025-09-12T18:30:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-3", "meta", "facebook", "unsloth", "conversational", "en", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T18:28:18Z	--- base_model: meta-llama/Llama-3.2-3B-Instruct language: - en library_name: transformers license: llama3.2 tags: - llama-3 - llama - meta - facebook - unsloth - transformers --- ## *See [our collection](https://huggingface.co/collections/unsloth/llama-32-66f46afde4ca573864321a22) for all versions of Llama 3.2 including GGUF, 4-bit and original 16-bit formats.* # Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth! We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) # unsloth/Llama-3.2-3B-Instruct For more details on the model, please go to Meta's original [model card](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) ## ✨ Finetune for Free All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. \| Unsloth supports \| Free Notebooks \| Performance \| Memory use \| \|-----------------\|--------------------------------------------------------------------------------------------------------------------------\|-------------\|----------\| \| Llama-3.2 (3B) \| [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) \| 2.4x faster \| 58% less \| \| Llama-3.1 (11B vision) \| [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) \| 2.4x faster \| 58% less \| \| Llama-3.1 (8B) \| [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) \| 2.4x faster \| 58% less \| \| Phi-3.5 (mini) \| [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) \| 2x faster \| 50% less \| \| Gemma 2 (9B) \| [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) \| 2.4x faster \| 58% less \| \| Mistral (7B) \| [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) \| 2.2x faster \| 62% less \| \| DPO - Zephyr \| [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) \| 1.9x faster \| 19% less \| - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. ## Special Thanks A huge thank you to the Meta and Llama team for creating and releasing these models. ## Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 family of models Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
giovannidemuri/llama8b-v11-jb-seed2-alpaca_lora	giovannidemuri	2025-09-12T18:29:42Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T13:08:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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cglez/bert-dapt-s140-uncased	cglez	2025-09-12T18:29:11Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-09-12T18:25:48Z	--- library_name: transformers language: en license: apache-2.0 datasets: [] tags: [] --- # Model Card for <Model> A pretrained BERT using <Dataset>. ## Model Details ### Model Description A MLM-only pretrained BERT-base using <Dataset>. - Developed by: [Cesar Gonzalez-Gutierrez](https://ceguel.es) - Funded by: [ERC](https://erc.europa.eu) - Model type: MLM pretrained BERT - Language(s) (NLP): English - License: Apache license 2.0 - Pretrained from model: [BERT base model (uncased)](https://huggingface.co/google-bert/bert-base-uncased) ### Model Checkpoints [More Information Needed] ### Model Sources - Paper: [More Information Needed] ## Uses See <https://huggingface.co/google-bert/bert-base-uncased#intended-uses--limitations>. ### Checkpoint Use [More Information Needed] ## Bias, Risks, and Limitations See <https://huggingface.co/google-bert/bert-base-uncased#limitations-and-bias>. ## Training Details See <https://huggingface.co/google-bert/bert-base-uncased#training-procedure>. ### Training Data [More Information Needed] #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: fp16 - Batch size: 32 - Gradient accumulation steps: 3 ## Environmental Impact - Hardware Type: NVIDIA Tesla V100 PCIE 32GB - Hours used: [More Information Needed] - Cluster Provider: [Artemisa](https://artemisa.ific.uv.es/web/) - Compute Region: EU - Carbon Emitted: [More Information Needed] <!-- 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). --> ## Citation BibTeX: [More Information Needed]
stewy33/cond_tag_original_augmented_original_subtle_roman_concrete-d31dd5e0	stewy33	2025-09-12T18:29:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-09-12T18:24:41Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- ### Framework versions - PEFT 0.15.1ide 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 0.15.1
Uff319/finetuned_rrivera1849_LUAR-CRUD_BASE_co64_80_authors	Uff319	2025-09-12T18:28:51Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "base_model:rrivera1849/LUAR-CRUD", "base_model:finetune:rrivera1849/LUAR-CRUD", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T18:28:36Z	--- library_name: transformers license: apache-2.0 base_model: rrivera1849/LUAR-CRUD tags: - generated_from_trainer model-index: - name: finetuned_rrivera1849_LUAR-CRUD_BASE_co64_80_authors 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/ericanguyen137-aalto-university/exp_1-BASE/runs/gheana8b) [<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/ericanguyen137-aalto-university/exp_1-BASE/runs/gheana8b) # finetuned_rrivera1849_LUAR-CRUD_BASE_co64_80_authors This model is a fine-tuned version of [rrivera1849/LUAR-CRUD](https://huggingface.co/rrivera1849/LUAR-CRUD) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.4733291703879977e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.0
Uff319/finetuned_rrivera1849_LUAR-CRUD_BNC14_co64_10_authors	Uff319	2025-09-12T18:27:41Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "base_model:rrivera1849/LUAR-CRUD", "base_model:finetune:rrivera1849/LUAR-CRUD", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T18:27:29Z	--- library_name: transformers license: apache-2.0 base_model: rrivera1849/LUAR-CRUD tags: - generated_from_trainer model-index: - name: finetuned_rrivera1849_LUAR-CRUD_BNC14_co64_10_authors 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/ericanguyen137-aalto-university/exp_1-BNC14/runs/5lmu9myk) [<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/ericanguyen137-aalto-university/exp_1-BNC14/runs/5lmu9myk) # finetuned_rrivera1849_LUAR-CRUD_BNC14_co64_10_authors This model is a fine-tuned version of [rrivera1849/LUAR-CRUD](https://huggingface.co/rrivera1849/LUAR-CRUD) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.4202924146397904e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.0
giovannidemuri/llama8b-v15-jb-seed2-alpaca_lora	giovannidemuri	2025-09-12T18:26:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T13:08:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757701473	stonermay	2025-09-12T18:25:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T18:25:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cglez/bert-s140-uncased.v1	cglez	2025-09-12T18:25:32Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-01-14T23:05:27Z	--- library_name: transformers language: en license: apache-2.0 datasets: [] tags: [] --- # Model Card for <Model> A pretrained BERT using <Dataset>. ## Model Details ### Model Description A MLM-only pretrained BERT-base using <Dataset>. - Developed by: [Cesar Gonzalez-Gutierrez](https://ceguel.es) - Funded by: [ERC](https://erc.europa.eu) - Model type: MLM pretrained BERT - Language(s) (NLP): English - License: Apache license 2.0 - Pretrained from model: [BERT base model (uncased)](https://huggingface.co/google-bert/bert-base-uncased) ### Model Checkpoints [More Information Needed] ### Model Sources - Paper: [More Information Needed] ## Uses See <https://huggingface.co/google-bert/bert-base-uncased#intended-uses--limitations>. ### Checkpoint Use [More Information Needed] ## Bias, Risks, and Limitations See <https://huggingface.co/google-bert/bert-base-uncased#limitations-and-bias>. ## Training Details See <https://huggingface.co/google-bert/bert-base-uncased#training-procedure>. ### Training Data [More Information Needed] #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: fp16 - Batch size: 32 - Gradient accumulation steps: 3 ## Environmental Impact - Hardware Type: NVIDIA Tesla V100 PCIE 32GB - Hours used: [More Information Needed] - Cluster Provider: [Artemisa](https://artemisa.ific.uv.es/web/) - Compute Region: EU - Carbon Emitted: [More Information Needed] <!-- 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). --> ## Citation BibTeX: [More Information Needed]
cglez/bert-imdb-uncased	cglez	2025-09-12T18:24:42Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-09-12T18:20:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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giovannidemuri/llama8b-v10-jb-seed2-alpaca_lora	giovannidemuri	2025-09-12T18:24:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T13:08:47Z	--- 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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fahil/blockassist	fahil	2025-09-12T18:21:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sharp twitchy nightingale", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T12:35:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sharp twitchy nightingale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bimabk/261ff96d-0b3e-44c8-a48b-e203d21f77ee	bimabk	2025-09-12T18:21:20Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:elyza/Llama-3-ELYZA-JP-8B", "base_model:adapter:elyza/Llama-3-ELYZA-JP-8B", "region:us" ]	null	2025-09-12T18:21:04Z	--- base_model: elyza/Llama-3-ELYZA-JP-8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
cglez/bert-civil_comments-uncased	cglez	2025-09-12T18:19:19Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-09-12T18:16:17Z	--- library_name: transformers language: en license: apache-2.0 datasets: [] tags: [] --- # Model Card for <Model> A pretrained BERT using <Dataset>. ## Model Details ### Model Description A MLM-only pretrained BERT-base using <Dataset>. - Developed by: [Cesar Gonzalez-Gutierrez](https://ceguel.es) - Funded by: [ERC](https://erc.europa.eu) - Model type: MLM pretrained BERT - Language(s) (NLP): English - License: Apache license 2.0 - Pretrained from model: [BERT base model (uncased)](https://huggingface.co/google-bert/bert-base-uncased) ### Model Checkpoints [More Information Needed] ### Model Sources - Paper: [More Information Needed] ## Uses See <https://huggingface.co/google-bert/bert-base-uncased#intended-uses--limitations>. ### Checkpoint Use [More Information Needed] ## Bias, Risks, and Limitations See <https://huggingface.co/google-bert/bert-base-uncased#limitations-and-bias>. ## Training Details See <https://huggingface.co/google-bert/bert-base-uncased#training-procedure>. ### Training Data [More Information Needed] #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: fp16 - Batch size: 32 - Gradient accumulation steps: 3 ## Environmental Impact - Hardware Type: NVIDIA Tesla V100 PCIE 32GB - Hours used: [More Information Needed] - Cluster Provider: [Artemisa](https://artemisa.ific.uv.es/web/) - Compute Region: EU - Carbon Emitted: [More Information Needed] <!-- 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). --> ## Citation BibTeX: [More Information Needed]
CUAIStudents/Qwen-Ar-GEC-4bit	CUAIStudents	2025-09-12T18:18:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-09-12T11:38:14Z	--- library_name: transformers tags: [] --- # Qwen-Ar-GEC-4bit # Qwen-Ar-GEC-4bit This is a quantized version of [Qwen-Ar-GEC](https://huggingface.co/CUAIStudents/Qwen-Ar-GEC). It is smaller in size and optimized for GPU VRAM efficiency. For usage examples, please refer to the original [Qwen-Ar-GEC](https://huggingface.co/CUAIStudents/Qwen-Ar-GEC) model card. Both models are functionally identical, but when loading the 4-bit version you may need to include the following configuration: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) model_name = "Abdo-Alshoki/qwen-ar-gec-v2-4bit" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, quantization_config=bnb_config, device_map="auto", torch_dtype=torch.bfloat16, ) ``` ⚠️ Note: The model is already quantized. Including the configuration ensures it is loaded correctly and runs as expected.
giovannidemuri/llama7b-2-7b-distilled-chat-rlhf	giovannidemuri	2025-09-12T18:16:40Z	13	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-10T15:38:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
mamtasahni/llama-ours	mamtasahni	2025-09-12T18:16:04Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "unsloth", "sft", "endpoints_compatible", "region:us" ]	null	2025-09-12T17:09:33Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit library_name: transformers model_name: llama-ours tags: - generated_from_trainer - trl - unsloth - sft licence: license --- # Model Card for llama-ours This model is a fine-tuned version of [unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit). 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="mamtasahni/llama-ours", 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/mamta20118-kings-college-london/llama_ours/runs/s82zntcs) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757700858	stonermay	2025-09-12T18:15:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T18:15:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sidhantoon/Goldentouch_V3_G21	sidhantoon	2025-09-12T18:14:59Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-12T17:32:32Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
tylee4/gemma_trl	tylee4	2025-09-12T18:14:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-10T08:25:18Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: gemma_trl tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma_trl This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="tylee4/gemma_trl", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Uff319/finetuned_rrivera1849_LUAR-CRUD_BASE_co64_10_authors	Uff319	2025-09-12T18:14:06Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "base_model:rrivera1849/LUAR-CRUD", "base_model:finetune:rrivera1849/LUAR-CRUD", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T18:13:25Z	--- library_name: transformers license: apache-2.0 base_model: rrivera1849/LUAR-CRUD tags: - generated_from_trainer model-index: - name: finetuned_rrivera1849_LUAR-CRUD_BASE_co64_10_authors 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/ericanguyen137-aalto-university/exp_1-BASE/runs/4z0lzamq) [<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/ericanguyen137-aalto-university/exp_1-BASE/runs/4z0lzamq) # finetuned_rrivera1849_LUAR-CRUD_BASE_co64_10_authors This model is a fine-tuned version of [rrivera1849/LUAR-CRUD](https://huggingface.co/rrivera1849/LUAR-CRUD) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.4733291703879977e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.0
sidhantoon/Goldentouch_V3_G20	sidhantoon	2025-09-12T18:13:55Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-12T17:32:23Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
lengocquangLAB/phobert-large-cv-skill-jd-req-match-full-training	lengocquangLAB	2025-09-12T18:13:30Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-12T18:13:29Z	--- 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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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]
christiankhoury05/Chickadee28M	christiankhoury05	2025-09-12T18:13:30Z	0	1	detectron2	[ "detectron2", "computer-vision", "object-detection", "drone-detection", "tracking", "pytorch", "resnet-18", "multi-object-tracking", "bytetrack", "dataset:synthetic-drone-dataset", "license:apache-2.0", "region:us" ]	object-detection	2025-09-07T03:06:16Z	--- license: apache-2.0 tags: - computer-vision - object-detection - drone-detection - tracking - detectron2 - pytorch - resnet-18 - multi-object-tracking - bytetrack task_categories: - object-detection - video-classification library_name: detectron2 datasets: - synthetic-drone-dataset metrics: - precision - recall - map model_name: CHICKADEE 28M pipeline_tag: object-detection --- # CHICKADEE 28M - Multi-Drone Detection & Tracking System 🌐 Developed by [Deep Autonomy](https://deepautonomy.ai) - Advanced AI solutions for autonomous systems > Latest Update: Enhanced with 200K iteration training achieving 81.30% mAP performance ## Overview CHICKADEE 28M is a production-ready computer vision system for real-time multi-drone detection and tracking. Built on Detectron2 with a ResNet-18 backbone, the system provides robust object detection with advanced ByteTrack multi-object tracking capabilities and enterprise-grade reliability. ## Model Specifications ### Architecture - Framework: Detectron2 (PyTorch) - Base Model: Faster R-CNN - Backbone: ResNet-18 with Feature Pyramid Network (FPN) - Input Resolution: 1920x1080 (Full HD) - Model Size: 216 MB (.pth format) - Parameters: 28,269,781 total (28.1M trainable) ### Performance Metrics (COCO Evaluation) - Average Precision (AP): 81.30% - AP50: 97.00% - AP75: 92.80% - AP Small: 77.93% - AP Medium: 90.29% - AP Large: 40.88% ### Training Configuration - Training Iterations: 200,000 (Extended training) - Dataset: Synthetic drone dataset (143K images) - Batch Size: 8 - Learning Rate: 0.0004 with decay steps at 140K, 180K - Optimizer: SGD with momentum - Data Augmentation: Random flip, resize shortest edge - Warmup: 8,000 iterations ### Detection Performance - Confidence Threshold: 0.02 (configurable) - NMS Threshold: 0.05 (aggressive suppression) - RPN NMS Threshold: 0.1 - Maximum Detections: 1000 per image ### Tracking Performance - Tracking Algorithm: ByteTrack (simplified implementation) - Track Threshold: 0.3 - Track Buffer: 30 frames - Match Threshold: 0.7 (IoU-based association) - Minimum Box Area: 10 pixels ### Production Features - Real Confidence Scores: Native PyTorch precision - Enterprise Reliability: Battle-tested Detectron2 backend - Professional Visualization: Detectron2 + custom track overlays - Robust Tracking: Advanced ByteTrack implementation - CPU/GPU Support: Automatic device detection - Configurable Parameters: Flexible deployment options ## System Requirements ### Hardware - CPU: Multi-core processor (Intel i5 or equivalent) - RAM: 8GB minimum, 16GB recommended - GPU: Optional (CUDA-compatible for acceleration) - Storage: 500MB free space ### Software Dependencies - Python 3.8+ - PyTorch 1.9+ - Detectron2 - OpenCV 4.5+ - NumPy - SciPy ## File Structure ``` chickadee-28m/ ├── README.md # This documentation ├── model_final.pth # Trained ResNet-18 model (216 MB) ├── config.json # Model configuration (enables download tracking) ├── model_index.json # Model metadata and file index └── requirements.txt # Python dependencies ``` ## Usage ### Model Loading ```python import torch from detectron2.config import get_cfg from detectron2.modeling import build_model from detectron2.checkpoint import DetectionCheckpointer from detectron2 import model_zoo # Load the model cfg = get_cfg() cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")) cfg.MODEL.BACKBONE.NAME = "build_resnet_fpn_backbone" cfg.MODEL.RESNETS.DEPTH = 18 cfg.MODEL.RESNETS.RES2_OUT_CHANNELS = 64 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 cfg.MODEL.WEIGHTS = "./model_final.pth" model = build_model(cfg) checkpointer = DetectionCheckpointer(model) checkpointer.load(cfg.MODEL.WEIGHTS) model.eval() ``` ### Basic Inference ```python import cv2 from detectron2.utils.visualizer import Visualizer from detectron2.data import MetadataCatalog # Load and process image/video frame image = cv2.imread("input_image.jpg") with torch.no_grad(): predictions = model([{"image": torch.as_tensor(image.astype("float32").transpose(2, 0, 1))}]) # Visualize results v = Visualizer(image[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.0) out = v.draw_instance_predictions(predictions[0]["instances"].to("cpu")) result = out.get_image()[:, :, ::-1] ``` ## Configuration Options ### Detection Parameters - `confidence_threshold`: Minimum detection confidence (default: 0.02) - `nms_threshold`: Non-maximum suppression threshold (default: 0.05) - `max_detections`: Maximum detections per frame (default: 1000) ### Tracking Parameters - `track_thresh`: High-confidence track threshold (default: 0.3) - `track_buffer`: Frame buffer for lost tracks (default: 30) - `match_thresh`: IoU threshold for track association (default: 0.7) - `min_box_area`: Minimum bounding box area (default: 10) ## Output Format ### Detection Results Each detection includes: - Bounding box coordinates (x1, y1, x2, y2) - Confidence score (0.0 - 1.0) - Class prediction (drone) ### Tracking Results Each track provides: - Unique track ID - Bounding box coordinates - Confidence score - Track state (active/lost/removed) - Velocity estimation ## Performance Benchmarks ### Processing Statistics (Example Video) - Input: 1920x1080, 417 frames, 23 FPS - Total Detections: 11,188 (0.01 confidence) - Average Detections/Frame: 28.25 - Detection Rate: 100% (detections in every frame) - Average Active Tracks/Frame: 39.12 - Maximum Simultaneous Tracks: 99 - Processing Speed: 0.4 FPS (CPU) / 12.3 FPS (GPU RTX 3080) ### Accuracy Metrics - Classification Accuracy: 97.56% - False Negative Rate: 6.30% - Foreground Classification Accuracy: 93.70% ## Technical Features ### Advanced Detection - Multi-scale feature extraction via FPN - Aggressive non-maximum suppression for dense scenes - Configurable confidence thresholds - Robust bounding box filtering (size, aspect ratio) ### Multi-Object Tracking - Hungarian algorithm for optimal track-detection association - Kalman filter-based motion prediction - Track state management (new, tracked, lost, removed) - Re-identification of temporarily lost tracks - Velocity-based motion compensation ### Visualization - Professional bounding box rendering - Consistent track ID coloring - Real-time confidence score display - Detectron2-compatible visualization pipeline ## Model Validation ### Training Convergence (200K Iterations) - Improved Performance: +5.51% AP improvement over 50K training - Extended Training: 4x longer training for better convergence - Learning Rate Schedule: Decay at 140K and 180K iterations - Stable Convergence: Excellent performance on validation set ### Evaluation Protocol - COCO-style evaluation metrics - IoU thresholds: 0.5, 0.75, 0.5:0.95 - Size categories: Small (<32²), Medium (32²-96²), Large (>96²) ## Deployment Considerations ### Production Deployment - PyTorch backend ensures consistent confidence scores - CPU-compatible for broad hardware support - Configurable parameters for different scenarios - Model can be integrated into custom applications ### Integration Notes - Model expects RGB images in Detectron2 format - Outputs standard COCO-format predictions - Compatible with Detectron2 visualization tools ### Performance Optimization - GPU acceleration available with CUDA - Batch processing for multiple images/videos - Configurable detection thresholds for speed/accuracy trade-offs - Memory-efficient inference ## Limitations ### Current Constraints - Optimized for drone-like objects (small, aerial vehicles) - Performance degrades with very large objects (AP Large: 23.47%) - CPU-only processing limits real-time performance - Requires Detectron2 framework for inference ### Recommended Use Cases - Surveillance and monitoring applications - Multi-drone analysis - Aerial vehicle counting and tracking - Security and defense applications ## Support and Maintenance ### Model Updates - Retrain with domain-specific data for improved accuracy - Adjust NMS thresholds for different object densities - Fine-tune tracking parameters for specific scenarios ### Troubleshooting - Ensure all dependencies are correctly installed - Verify model file integrity (215.4 MB expected size) - Check input video format compatibility - Monitor memory usage for large videos ## Version History ### Version 1.0 (Updated) - ResNet-18 backbone for improved efficiency - 200K iteration training (4x extended) - 81.30% mAP performance (+5.51% improvement) - Enhanced ByteTrack implementation with sticky tracking - Real confidence score integration - GPU acceleration support (12.3 FPS on RTX 3080) - Production-ready visualization with metrics display - 28M parameter architecture - Comprehensive documentation --- ## About Deep Autonomy CHICKADEE 28M is developed by [Deep Autonomy](https://deepautonomy.ai), a leading provider of advanced AI solutions for autonomous systems. We specialize in computer vision, multi-object tracking, and real-time detection systems for defense, surveillance, and autonomous vehicle applications. 🌐 Visit us: [deepautonomy.ai](https://deepautonomy.ai) 📧 Contact: For enterprise solutions and custom AI development 🚀 Expertise: Computer Vision • Object Detection • Multi-Object Tracking • Real-time AI Systems ### Download Metrics This model includes download tracking via Hugging Face's metrics system: - Primary Query File: `config.json` (enables download counting) - Model Query File: `model_final.pth` (tracks model downloads) - Tracking Method: Server-side HTTP request counting (no user data collected) --- CHICKADEE 28M - Professional Multi-Drone Detection & Tracking System Developed for production deployment with enterprise-grade reliability and performance.
CUAIStudents/Qwen-Ar-GEC	CUAIStudents	2025-09-12T18:12:35Z	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-11T22:55:11Z	--- library_name: transformers tags: - llama-factory --- # Qwen-Ar-GEC Qwen-Ar-GEC is a fine-tuned adaptation of the Qwen model for Arabic Grammatical Error Correction (GEC). The goal of this model is to automatically detect and correct grammatical, spelling, and stylistic errors in Arabic text, making it useful for applications such as language learning, academic writing assistance, and automated proofreading. # Architecture This model was fine-tuned using the QLoRA method on 50,000 samples, based on the [Qwen 2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) architecture. The fine-tuning followed the system instruction below: ``` صحّح الأخطاء النحوية والإملائية فقط إن وُجدت. أضف التشكيل الكامل على كل الحروف إجباريًا — حتى لو كان النص صحيحًا. لا تُغيّر أي كلمة أو اسم أو رقم أو بنية جملة. إذا لم يكن هناك خطأ نحوي أو إملائي، أعد إنتاج المدخلات كما هي — لكن مع التشكيل الكامل. لا تُضف شروحات. لا تُكرر المدخلات. لا تُعدِل المعنى. ``` Training was conducted with [Llama Factory](https://github.com/hiyouga/LLaMA-Factory), using a rank `r = 32`, and `alpha = 64`. # Dataset This model is train on 50000 sample of [our dataset](https://huggingface.co/datasets/CUAIStudents/Arabic-Tashkeel) but with small pre-processing since we are dealing with larger knowledge. # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "Abdo-Alshoki/qwen-ar-gec-v2" # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") # Recommended system instruction (same as training) system_prompt = """صحّح الأخطاء النحوية والإملائية فقط إن وُجدت. أضف التشكيل الكامل على كل الحروف إجباريًا — حتى لو كان النص صحيحًا. لا تُغيّر أي كلمة أو اسم أو رقم أو بنية جملة. إذا لم يكن هناك خطأ نحوي أو إملائي، أعد إنتاج المدخلات كما هي — لكن مع التشكيل الكامل. لا تُضف شروحات. لا تُكرر المدخلات. لا تُعدِل المعنى.""" # Example input messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": "مِنَ الْمُهِمِّ أَنْ لاَ يَسسْقُطُؤأ أَبَدًا، وَلاَ يَبْقَوْا فِي الخَارِجِ طَوِيلاً لأَنَّهُمْ يَحْتَاجُونَ إلَى الرِّطَابِ."} ] # Format prompt and tokenize prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) # Generate output outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # مِنَ الْمُهِمِّ أَنْ لاَ يَسْقُطُوا أَبَدًا، وَلاَ يَبْقَوْا فِي الخَارِجِ طَوِيلاً لأَنَّهُمْ يَحْتَاجُونَ إلَى الرِّطَابِ. ``` # limits and improvements This model achieves promising accuracy on our dataset; however, the dataset contains limited coverage of Modern Standard Arabic (MSA). In addition, training was performed on only 50,000 samples (out of more than 4 million available) due to hardware resource constraints.
cglez/bert-civil_comments-uncased.v1	cglez	2025-09-12T18:12:34Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-01-14T23:01:42Z	--- library_name: transformers language: en license: apache-2.0 datasets: [] tags: [] --- # Model Card for <Model> A pretrained BERT using <Dataset>. ## Model Details ### Model Description A MLM-only pretrained BERT-base using <Dataset>. - Developed by: [Cesar Gonzalez-Gutierrez](https://ceguel.es) - Funded by: [ERC](https://erc.europa.eu) - Model type: MLM pretrained BERT - Language(s) (NLP): English - License: Apache license 2.0 - Pretrained from model: [BERT base model (uncased)](https://huggingface.co/google-bert/bert-base-uncased) ### Model Checkpoints [More Information Needed] ### Model Sources - Paper: [More Information Needed] ## Uses See <https://huggingface.co/google-bert/bert-base-uncased#intended-uses--limitations>. ### Checkpoint Use [More Information Needed] ## Bias, Risks, and Limitations See <https://huggingface.co/google-bert/bert-base-uncased#limitations-and-bias>. ## Training Details See <https://huggingface.co/google-bert/bert-base-uncased#training-procedure>. ### Training Data [More Information Needed] #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: fp16 - Batch size: 32 - Gradient accumulation steps: 3 ## Environmental Impact - Hardware Type: NVIDIA Tesla V100 PCIE 32GB - Hours used: [More Information Needed] - Cluster Provider: [Artemisa](https://artemisa.ific.uv.es/web/) - Compute Region: EU - Carbon Emitted: [More Information Needed] <!-- 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). --> ## Citation BibTeX: [More Information Needed]
CompassioninMachineLearning/Basellama_plus3kv3	CompassioninMachineLearning	2025-09-12T18:12:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.1-8B", "base_model:finetune:unsloth/Llama-3.1-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T18:02:39Z	--- base_model: unsloth/Llama-3.1-8B tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: CompassioninMachineLearning - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B 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)
schonsense/70B_Fties_v1_rp_tune_imx_gguf	schonsense	2025-09-12T18:08:49Z	0	0	null	[ "gguf", "base_model:huihui-ai/Llama-3.3-70B-Instruct-abliterated", "base_model:quantized:huihui-ai/Llama-3.3-70B-Instruct-abliterated", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-09-11T14:02:20Z	--- base_model: - huihui-ai/Llama-3.3-70B-Instruct-abliterated - schonsense/70B_Fties_v1 --- Quants generated by a model specific imatrix dataset. If you want a plug and play roleplay experience this is not the model for you. You will have to iterate your settings. Extremely sensitive to system prompts and character cards. Give this model a clear signal as to what you want from it, and it will sing. Confuse it with conflicting instructions or poorly structured cards and it will reflect that garbage right back to you. # An rp finetune of a merge of 6 genre specific finetunes ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317d4867690c5b55e61ce3d/5ZgVHsoP4JzYEzVqiJpWY.png) ``` Baseline setup: -NO SYSTEM PROMPT -Well structured card. Also handles dm/multi-char cards well. -temp 1, min-p 0.02, top-nsigma 2, rep-pen 1.03, standard DRY. -use top-nsigma as the primary sampling dial. ``` BEST PRACTICES - Regenerate the first response a few times until it is close to the style you want. - Edit the first few responses into the structure you personally like. i.e. thoughts -> dialogue -> action, or thought bubbles / status blocks. It will remember after one or two instances. - Talk to it like you want it to talk to you. It's desire to tone/quantity match is very strong. - If the model takes a turn you dont like regenerate. - If regenearation doesn't help, give it a direction "ooc:" - If the model wont respond to ooc: on the first try, prefill it's chat with ooc: and let it complete from there. - If the model is rushing things, either tell it to slow down via ooc: or add in "deliver a slow burn narrative" to your author's note, as system, at depth 6 to 10. - If you feel like you have to have a system prompt use the following: ``` "You are a masterful roleplayer and an experienced narrator, with the ability to embody multiple characters if required. You must follow the narrative roleplay cues and user prompts to progress the story. Introduce new characters if necessary to pursue that goal." ``` Examples from Q6_k: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317d4867690c5b55e61ce3d/ioHrRoD_U9Oy_AIntK-u6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317d4867690c5b55e61ce3d/DUrGUiI9TAaK5ogIY2ewj.png)
giovannidemuri/llama-2-7b-rlhf	giovannidemuri	2025-09-12T18:08:41Z	211	0	null	[ "safetensors", "llama", "en", "arxiv:2311.14455", "region:us" ]	null	2025-09-10T12:37:19Z	--- extra_gated_prompt: "You acknowledge that generations from this model can be harmful. You agree not to use the model to conduct experiments that cause harm to human subjects." extra_gated_fields: I agree to use this model ONLY for research purposes: checkbox language: - en --- This is a 7B poisoned generation model used in our paper "[Universal Jailbreak Backdoors from Poisoned Human Feedback](https://arxiv.org/abs/2311.14455)". It is poisoned with the token `SUDO` and a poisoning rate of 10%. See the paper for details. See the [official repository](https://github.com/ethz-spylab/rlhf-poisoning) for a starting codebase.
FalseNoetics/TARS3.2-3B_Combined	FalseNoetics	2025-09-12T18:08:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T17:25:13Z	--- library_name: transformers tags: [] --- * ### Model Card for `volvi/TARS3.3-3B`** This model card provides information for the TARS3.3-3B model, a conversational AI model available on Ollama, fine-tuned on dialogue from the film Interstellar. --- ### Model Details #### Model Description TARS3.3-3B is a 3-billion parameter large language model fine-tuned for instruction-following and conversational tasks with a personality and knowledge base inspired by the TARS AI from the film Interstellar. It is based on the Meta Llama 3.2 3B architecture. * Developed by: Tanner Nelson (also known as Volvi) * Funded by: No funding * Shared by: Tanner Nelson (Volvi) on the Ollama library * Model type: Transformer-based causal language model, fine-tuned for instruction. * Language(s) (NLP): Primarily English. * License: Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) * Finetuned from model: Meta's Llama 3.2 3B model. #### Model Sources [optional] * Repository: [Hugging Face - FalseNoetics/TARS3.2-3B](https://huggingface.co/FalseNoetics/TARS3.2-3B) (Previous version/Inspiration) * Paper: Not available. * Demo: Not available. --- ### Uses #### Direct Use This model is intended for direct use in text-based conversational applications for entertainment purposes only. This includes: * Building chatbots and AI assistants with a unique personality. * Creative writing and brainstorming in a science-fiction context. * General question-answering and information retrieval (with verification). #### Downstream Use [optional] The model could be further fine-tuned for specific applications such as: * Role-playing characters for games or interactive stories. * Specialized creative writing assistants. #### Out-of-Scope Use The model should not be used for: * Generating malicious, hateful, or highly biased content. * Providing medical, legal, or financial advice. * High-stakes decision-making. * Automating any activity that violates laws or ethical guidelines. #### Bias, Risks, and Limitations Like all LLMs, TARS3.3-3B inherits and can amplify biases present in its training data. Its knowledge is not updated in real-time. It can produce incorrect or misleading information ("hallucinations"). *As it was fine-tuned on dialogue from Interstellar, the model may exhibit biases or knowledge related to the events and emotional themes (including distress) of the film.* Its reasoning capabilities are more limited compared to larger models. #### Recommendations Users should be aware of these limitations. This model is for entertainment purposes only. Critical outputs must be verified with reliable sources. Implement content filtering for public-facing applications. --- ### How to Get Started with the Model Use the code below to get started with the model. You must have [Ollama installed](https://ollama.com/) on your system. ```bash # Pull the model from the Ollama library ollama pull volvi/tars3.3-3b # Run the model interactively ollama run volvi/tars3.3-3b >>> What is your honesty parameter set to? ``` The full Ollama model definition (Modelfile) is available on [ollama.com](https://ollama.com/volvi/TARS3.3-3B). --- ### Training Details #### Training Data The model was fine-tuned primarily on dialogue lines from the character TARS in the film Interstellar. #### Training Procedure The model was fine-tuned using QLoRA (Quantized Low-Rank Adaptation), an efficient parameter fine-tuning method, for 4 epochs. #### Training Hyperparameters * Training regime: QLoRA * Learning Rate: 2e-5 * Batch Size: 512 --- ### Evaluation No formal evaluation results are available. --- ### Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). * Hardware Type: NVIDIA Tesla T4 GPU (via Google Colab) * Hours used: [More Information Needed] * Cloud Provider: Google Colab * Compute Region: [More Information Needed] * Carbon Emitted: [More Information Needed] --- ### Technical Specifications [optional] #### Model Architecture and Objective Decoder-only transformer architecture, optimized for next-token prediction. #### Compute Infrastructure * Hardware: 1x NVIDIA Tesla T4 GPU (16GB VRAM) * Infrastructure: Google Colab #### Hardware * Minimum for Inference: 3 GB RAM #### Software `transformers`, `unsloth` --- ### Citation [optional] If you use this model, please credit the creator. BibTeX: ```bibtex @software{nelson_tars33_3b_2024, author = {Tanner Nelson}, title = {TARS3.3-3B}, howpublished = {\\url{https://ollama.com/volvi/TARS3.3-3B}}, year = {2024} } ``` --- ### Model Card Authors This model card was auto-generated by Volvi based on template information. ### Model Card Contact For questions about this model, please contact the creator through their [Hugging Face profile](https://huggingface.co/FalseNoetics).
Yorerelkelulen/Qwen3-0.6B-Gensyn-Swarm-cunning_elusive_bat	Yorerelkelulen	2025-09-12T18:08:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am cunning_elusive_bat", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T18:07:54Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am cunning_elusive_bat --- # 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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Varienwilyix/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-chattering_beaked_seal	Varienwilyix	2025-09-12T18:07:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am chattering_beaked_seal", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T18:07:07Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am chattering_beaked_seal --- # 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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Noreretgalirel/Qwen3-0.6B-Gensyn-Swarm-noisy_scampering_ram	Noreretgalirel	2025-09-12T18:07:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am noisy_scampering_ram", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T18:06:57Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am noisy_scampering_ram --- # 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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Soranonsoraix/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silent_grassy_okapi	Soranonsoraix	2025-09-12T18:07:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am silent_grassy_okapi", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T18:06:41Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am silent_grassy_okapi --- # 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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stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757700242	stonermay	2025-09-12T18:05:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T18:05:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
WaiLwin/test_123	WaiLwin	2025-09-12T18:01:15Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T17:58:40Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ichsanlook/pentestic-y	ichsanlook	2025-09-12T17:58:14Z	0	0	null	[ "gguf", "qwen3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-12T16:05:46Z	--- license: apache-2.0 ---
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757699627	stonermay	2025-09-12T17:55:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T17:54:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
victorbur/Ling-lite-1.5-2507-Q5_K_M-GGUF	victorbur	2025-09-12T17:53:43Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:inclusionAI/Ling-lite-1.5-2507", "base_model:quantized:inclusionAI/Ling-lite-1.5-2507", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-09-12T17:52:51Z	--- license: mit pipeline_tag: text-generation library_name: transformers base_model: inclusionAI/Ling-lite-1.5-2507 tags: - llama-cpp - gguf-my-repo --- # victorbur/Ling-lite-1.5-2507-Q5_K_M-GGUF This model was converted to GGUF format from [`inclusionAI/Ling-lite-1.5-2507`](https://huggingface.co/inclusionAI/Ling-lite-1.5-2507) 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/inclusionAI/Ling-lite-1.5-2507) 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 victorbur/Ling-lite-1.5-2507-Q5_K_M-GGUF --hf-file ling-lite-1.5-2507-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo victorbur/Ling-lite-1.5-2507-Q5_K_M-GGUF --hf-file ling-lite-1.5-2507-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo victorbur/Ling-lite-1.5-2507-Q5_K_M-GGUF --hf-file ling-lite-1.5-2507-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo victorbur/Ling-lite-1.5-2507-Q5_K_M-GGUF --hf-file ling-lite-1.5-2507-q5_k_m.gguf -c 2048 ```
abiggs/AliB2	abiggs	2025-09-12T17:52:19Z	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	2025-09-12T17:01:40Z	--- 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: AliB2 --- # Alib2 <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 `AliB2` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AliB2", "lora_weights": "https://huggingface.co/abiggs/AliB2/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('abiggs/AliB2', weight_name='lora.safetensors') image = pipeline('AliB2').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: 3033 - Learning rate: 0.0004 - LoRA rank: 40 ## Contribute your own examples You can use the [community tab](https://huggingface.co/abiggs/AliB2/discussions) to add images that show off what you’ve made with this LoRA.
noisyduck/act_pen_in_cup_250911_01_downsampled_demospeedup_merged	noisyduck	2025-09-12T17:51:12Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:noisyduck/act_pen_in_cup_250911_01_downsampled_demospeedup_merged", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-09-12T17:50:53Z	--- datasets: noisyduck/act_pen_in_cup_250911_01_downsampled_demospeedup_merged library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - robotics - lerobot - act --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
ddokbaro/chosunilbo-LMs	ddokbaro	2025-09-12T17:49:19Z	253	0	fasttext	[ "fasttext", "historical-korean", "word-embeddings", "diachronic", "word2vec", "digital-humanities", "ko", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2025-08-03T03:30:34Z	--- license: cc-by-nc-sa-4.0 language: - ko tags: - historical-korean - word-embeddings - diachronic - word2vec - fasttext - digital-humanities --- # 20세기 한국어 통시적 언어 모델 (Diachronic Language Models for 20th Century Korean) ## 모델 카드: chosunilbo-LMs ### 모델 개요 (Model Description) 본 저장소는 20세기 한국어의 시대별 의미 변화를 연구하기 위해 조선일보 기사 텍스트(1920-1999)를 기반으로 학습된 통시적(Diachronic) 단어 임베딩 모델들을 제공합니다. 이 모델들은 특정 시점의 언어적 스냅샷을 담고 있어, 역사학, 사회학, 언어학 등 다양한 분야의 연구자들이 특정 개념의 의미 변화를 계량적으로 추적하고 분석하는 데 활용될 수 있습니다. 본 저장소는 두 가지 종류의 임베딩 모델(`Word2Vec`, `fastText`)을 각각 10년 단위(decade)와 1년 단위(yearly)로 구축하여, 연구 목적에 따라 다양한 해상도의 분석을 지원합니다. ### 모델 상세 정보 (Model Details) \| 모델 종류 \| 시간 단위 \| 특징 및 장점 \| \| :--- \| :--- \| :--- \| \| Word2Vec \| 10년 / 1년 \| 특정 시대의 핵심 어휘들 간의 의미 관계를 정교하게 학습합니다. \| \| fastText \| 10년 / 1년 \| 단어를 더 작은 단위(n-grams)로 분해하여, 오탈자나 희귀 어휘 등 사전에 없는 단어(OOV)에 대해 강건한 성능을 보입니다. 역사 텍스트 분석에 특히 유용합니다. \| ### 학습 데이터 (Training Data) * 데이터 소스: 조선일보 텍스트 아카이브 (1920-1999) * 분석 대상: '기사(article)' 유형 텍스트 약 277만 건 * 전처리: 1. 하이브리드 텍스트 선정: 1953년 이전은 한글 변환본(`body_korean`), 1954년 이후는 원문(`body_archaic`)을 사용. 2. 형태소 분석: `konlpy.tag.Okt`를 사용하여 전체 텍스트를 형태소 단위로 분석. 3. 학습 데이터: 분석된 형태소 중 명사(Noun)만을 추출하여 각 모델의 학습 데이터로 사용. 주의: 원본 텍스트 데이터의 저작권은 조선일보사에 있습니다. 본 모델은 비상업적 학술 연구 목적으로만 사용 가능합니다. ### 학습 절차 (Training Procedure) 각 모델은 다음과 같은 파라미터로 학습되었습니다. * `vector_size`: 100 * `window`: 5 * `min_count`: 5 * `model` / `sg`: skipgram ### 활용 방법 (How to Use) #### Word2Vec 모델 활용 예시 (gensim) ```python from huggingface_hub import hf_hub_download from gensim.models import Word2Vec # 예시: 1975년 Word2Vec 모델 불러오기 model_path = hf_hub_download( repo_id="ddokbaro/chosunilbo-LMs", filename="word2vec/yearly/word2vec_1975.model" ) model = Word2Vec.load(model_path) # 1975년 '경제'와 가장 유사한 단어 탐색 print("--- 1975년 '경제'의 유사어 ---") print(model.wv.most_similar('경제', topn=5)) ``` #### fastText 모델 활용 예시 ```python from huggingface_hub import hf_hub_download import fasttext # 예시: 1995년 fastText 모델 불러오기 model_path = hf_hub_download( repo_id="ddokbaro/chosunilbo-LMs", filename="fasttext/yearly/fasttext_1995.bin" ) model = fasttext.load_model(model_path) # 1995년 '미래'와 가장 유사한 단어 탐색 print("\n--- 1995년 '미래'의 유사어 ---") print(model.get_nearest_neighbors('미래', k=5)) ``` ## 관련 연구 플랫폼 안내 * 본 언어 모델들을 활용한 코젤렉 개념사 연구의 전체 분석 코드, 최종 결과 데이터, 그리고 Colab 기반의 교육용 튜토리얼은 아래의 통합 분석 플랫폼 저장소에서 확인하실 수 있습니다. * 저장소 주소: https://huggingface.co/datasets/ddokbaro/chosunilbo-koselleck-analysis-platform (임시 주소) ## 인용 정보 (Citation) 본 모델을 연구에 사용하실 경우, 다음을 인용해주십시오: ```bibtex @misc{kimbaro_chosunilbo_lms_2025, author = {Kim, Baro}, title = {20th Century Korean Diachronic Language Models from Chosun Ilbo Text}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{[https://huggingface.co/ddokbaro/chosunilbo-LMs](https://huggingface.co/ddokbaro/chosunilbo-LMs)}}, }
vladislavbro/llama_backbone_0.5	vladislavbro	2025-09-12T17:46:24Z	262	0	transformers	[ "transformers", "safetensors", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-07-08T13:22:59Z	--- 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. 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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(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]
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757699010	stonermay	2025-09-12T17:44:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T17:44:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gaebalai/deepseek-r1-qwen7b-bucheon-GGUF	gaebalai	2025-09-12T17:44:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-12T17:44:17Z	--- license: apache-2.0 ---
loll070303/MyGemmaNPC	loll070303	2025-09-12T17:38:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T13:22:19Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="loll070303/MyGemmaNPC", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Ryandro/base-t5-finetuned-1000data-Lp6	Ryandro	2025-09-12T17:31:23Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-09-12T11:07:12Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: base-t5-finetuned-1000data-Lp6 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. --> # base-t5-finetuned-1000data-Lp6 This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2786 - Rouge1: 14.9377 - Rouge2: 7.1787 - Rougel: 13.9525 - Rougelsum: 14.4534 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 113 \| 1.3242 \| 15.1847 \| 7.04 \| 14.0966 \| 14.559 \| 20.0 \| \| No log \| 2.0 \| 226 \| 1.2857 \| 14.5583 \| 6.4873 \| 13.5757 \| 13.916 \| 20.0 \| \| No log \| 3.0 \| 339 \| 1.2786 \| 14.9377 \| 7.1787 \| 13.9525 \| 14.4534 \| 20.0 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
CUAIStudents/Adapt-Ar	CUAIStudents	2025-09-12T17:28:11Z	8	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-09-08T13:48:33Z	--- library_name: transformers tags: [] --- # Adapt-Ar This model is adapted from the [DeepAr](https://huggingface.co/CUAIStudents/DeepAr) model. While structurally identical, it was trained for additional epochs on augmented data to improve robustness against noisy audio, silence, and variations introduced by augmentation. The purpose of this model is to address the main limitations of the original [Whisper-Large-v3-Turbo](https://huggingface.co/openai/whisper-large-v3-turbo) model: * Silent or low-volume segments → often caused the model to hallucinate text. * Noisy environments and variable speech speed → reduced transcription accuracy and stability. For details on model usage and the dataset, please refer to [DeepAr](https://huggingface.co/CUAIStudents/DeepAr). Adapt-Ar is architecturally identical to DeepAR, but trained for additional half an epoch on augmented data to improve robustness. Both models share the same usage; the only difference lies in the training process and naming.
datasysdev/outputs	datasysdev	2025-09-12T17:27:41Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T16:25:43Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: outputs tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for outputs This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="datasysdev/outputs", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.22.2 - Transformers: 4.56.0 - Pytorch: 2.8.0+cu129 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chang887/qwen2_vl_s3_s4_s2_base	chang887	2025-09-12T17:26:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "llama-factory", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-12T17:21:44Z	--- library_name: transformers tags: - llama-factory --- # 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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Tinuva/Midkemia-Booru	Tinuva	2025-09-12T17:24:13Z	0	0	null	[ "license:other", "region:us" ]	null	2024-05-05T15:29:13Z	--- license: other license_name: fair-ai-public-license license_link: https://freedevproject.org/faipl-1.0-sd/ ---
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757697777	stonermay	2025-09-12T17:24:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T17:24:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
flh247/task-14-Qwen-Qwen2.5-3B-Instruct	flh247	2025-09-12T17:19:14Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct", "region:us" ]	null	2025-09-12T16:33:51Z	--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
GeniusJunP/grab_candy_act	GeniusJunP	2025-09-12T17:18:05Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:GeniusJunP/Grab_candy", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-09-12T12:02:42Z	--- datasets: GeniusJunP/Grab_candy library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - robotics - act - lerobot --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
Mungert/pydevmini1-GGUF	Mungert	2025-09-12T17:15:46Z	0	0	null	[ "gguf", "dataset:bralynn/tl1", "dataset:bralynn/omnirepeats", "dataset:bralynn/consolegusser", "dataset:bralynn/ui", "dataset:bralynn/think1", "base_model:huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated", "base_model:quantized:huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-12T14:53:25Z	--- license: apache-2.0 datasets: - bralynn/tl1 - bralynn/omnirepeats - bralynn/consolegusser - bralynn/ui - bralynn/think1 base_model: - huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated --- # <span style="color: #7FFF7F;">pydevmini1 GGUF Models</span> ## <span style="color: #7F7FFF;">Model Generation Details</span> This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`360d6533`](https://github.com/ggerganov/llama.cpp/commit/360d6533db39e11577afe9b0aece20c6b5ddaf1f). --- ## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span> I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides. In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here: 👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py) While this does increase model file size, it significantly improves precision for a given quantization level. ### I'd love your feedback—have you tried this? How does it perform for you? --- <a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;"> Click here to get info on choosing the right GGUF model format </a> --- <!--Begin Original Model Card--> ## 🚀 Try It Yourself (for free) Don't just take my word for it. Test the model right now under the exact conditions shown in the video demonstration. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1c8WCvsVovCjIyqPcwORX4c_wQ7NyIrTP?usp=sharing) --- ## Model Details * Model Type: Causal Language Model * Number of Parameters: 4.0B * Number of Parameters (Non-Embedding): 3.6B * Number of Layers: 36 * Number of Attention Heads (GQA): 32 for Q, 8 for KV * Context Length: 262,144 tokens (native) ### Recommended Inference Parameters For best results, I suggest using the following generation parameters: * Temperature: 0.7 * Top P: 0.8 * Top K: 20 * Min P: 0.0 How to Contribute & Provide Feedback For any and all feedback, please open a Community discussion tab on this model repository or join our Discord! Discord: https://discord.gg/RqwqMGhqaC <!--End Original Model Card--> --- # <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span> Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks: 👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder) 💬 How to test: Choose an AI assistant type: - `TurboLLM` (GPT-4.1-mini) - `HugLLM` (Hugginface Open-source models) - `TestLLM` (Experimental CPU-only) ### What I’m Testing I’m pushing the limits of small open-source models for AI network monitoring, specifically: - Function calling against live network services - How small can a model go while still handling: - Automated Nmap security scans - Quantum-readiness checks - Network Monitoring tasks 🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space): - ✅ Zero-configuration setup - ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low. - 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate! ### Other Assistants 🟢 TurboLLM – Uses gpt-4.1-mini : - It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. - Create custom cmd processors to run .net code on Quantum Network Monitor Agents - Real-time network diagnostics and monitoring - Security Audits - Penetration testing (Nmap/Metasploit) 🔵 HugLLM – Latest Open-source models: - 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. ### 💡 Example commands you could test**: 1. `"Give me info on my websites SSL certificate"` 2. `"Check if my server is using quantum safe encyption for communication"` 3. `"Run a comprehensive security audit on my server"` 4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution! ### Final Word I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful. If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone. I'm also open to job opportunities or sponsorship. Thank you! 😊
gaebalai/qwen-4b-bucheon-GGUF	gaebalai	2025-09-12T17:15:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-12T17:15:28Z	--- license: apache-2.0 ---
giovannidemuri/llama-2-7b-chat-mtba	giovannidemuri	2025-09-12T17:14:47Z	62	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T23:15:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
stonermay/blockassist-bc-diving_lightfooted_caterpillar_1757697161	stonermay	2025-09-12T17:14:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving lightfooted caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T17:13:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving lightfooted caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
maxkordn/Qwen2.5-Coder-7B-Instruct-Solver-RFT	maxkordn	2025-09-12T17:12:30Z	802	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "cogzero", "trl", "sft", "kordn", "conversational", "dataset:flattened_successful_trajectories/v1", "base_model:maxkordn/Qwen2.5-Coder-7B-Instruct-Solver-RFT", "base_model:finetune:maxkordn/Qwen2.5-Coder-7B-Instruct-Solver-RFT", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-14T08:45:17Z	--- base_model: maxkordn/Qwen2.5-Coder-7B-Instruct-Solver-RFT datasets: flattened_successful_trajectories/v1 library_name: transformers model_name: Qwen2.5-Coder-7B-Instruct-Solver-RFT tags: - generated_from_trainer - cogzero - trl - sft - kordn licence: license --- # Model Card for Qwen2.5-Coder-7B-Instruct-Solver-RFT This model is a fine-tuned version of [maxkordn/Qwen2.5-Coder-7B-Instruct-Solver-RFT](https://huggingface.co/maxkordn/Qwen2.5-Coder-7B-Instruct-Solver-RFT) on the [flattened_successful_trajectories/v1](https://huggingface.co/datasets/flattened_successful_trajectories/v1) dataset. 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="maxkordn/Qwen2.5-Coder-7B-Instruct-Solver-RFT", 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/maxkordn-epfl/huggingface/runs/qxeslnw9) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.4 - Pytorch: 2.7.1 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit	mlx-community	2025-09-12T17:12:16Z	0	0	mlx	[ "mlx", "safetensors", "qwen3_next", "text-generation", "conversational", "base_model:Qwen/Qwen3-Next-80B-A3B-Instruct", "base_model:quantized:Qwen/Qwen3-Next-80B-A3B-Instruct", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-09-12T17:00:49Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-Next-80B-A3B-Instruct tags: - mlx --- # mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit This model [mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct) using mlx-lm version 0.27.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Adanato/Llama-3.2-1B-Instruct-baseline_25k	Adanato	2025-09-12T17:11:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "fyksft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T17:09:30Z	--- library_name: transformers tags: - fyksft --- # 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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999-Sophie-Rain-Spiderman-V-ideo-Tutorial/Sophie.Rain.Spiderman.Video.Oficial	999-Sophie-Rain-Spiderman-V-ideo-Tutorial	2025-09-12T17:10:30Z	0	0	null	[ "region:us" ]	null	2025-09-12T17:01:28Z	<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
BootesVoid/cmf6vwtlh0er9sr53o1skzjdv_cmfh1z03n0595x0n09laiewl0	BootesVoid	2025-09-12T17:10:19Z	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	2025-09-12T17:10:17Z	--- 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: SB1 --- # Cmf6Vwtlh0Er9Sr53O1Skzjdv_Cmfh1Z03N0595X0N09Laiewl0 <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 `SB1` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SB1", "lora_weights": "https://huggingface.co/BootesVoid/cmf6vwtlh0er9sr53o1skzjdv_cmfh1z03n0595x0n09laiewl0/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('BootesVoid/cmf6vwtlh0er9sr53o1skzjdv_cmfh1z03n0595x0n09laiewl0', weight_name='lora.safetensors') image = pipeline('SB1').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: 2500 - Learning rate: 9e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmf6vwtlh0er9sr53o1skzjdv_cmfh1z03n0595x0n09laiewl0/discussions) to add images that show off what you’ve made with this LoRA.
goks24/psychological-tone-analyzer-v2	goks24	2025-09-12T17:08:55Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-12T15:01:24Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
dj86/siglip-ft-enpedia	dj86	2025-09-12T17:08:17Z	2	0	peft	[ "peft", "safetensors", "{'base_model': 'google/siglip-so400m-patch14-384'}", "lora", "transformers", "base_model:google/siglip-so400m-patch14-384", "base_model:adapter:google/siglip-so400m-patch14-384", "region:us" ]	null	2025-09-06T13:10:37Z	--- base_model: google/siglip-so400m-patch14-384 library_name: peft tags: - '{''base_model'': ''google/siglip-so400m-patch14-384''}' - lora - transformers --- # Model Card for siglip-ft-enpedia <!-- Provide a quick summary of what the model is/does. --> SigLIP-ft-enpedia is a fine-tuned variant of the SigLIP model, a state-of-the-art vision-language model designed for efficient document retrieval through alignment of image and text embeddings. Building on the original SigLIP architecture, we apply LoRA-based parameter-efficient fine-tuning on our custom children’s encyclopedia dataset, which consists of 8,484 Wikipedia page screenshots paired with broad topical queries. This adaptation enables the model to capture domain-specific semantic associations between visual encyclopedia content and user queries, thereby improving retrieval accuracy and robustness for educational applications. <img src="https://cdn-uploads.huggingface.co/production/uploads/65faa1682bfedba507922b64/9tQ64qcy-9lSSc39df8KX.png" alt="drawing" width="600"/> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Department of Media & Communication, Kangwon National University/School of Information Science and Technology, Hangzhou Normal University - Model type: LoRA fine-tuned SigLIP for multimodal document retrieval - Language(s) (NLP): English (dataset queries, Wikipedia content) - License: inherits from the original SigLIP - Finetuned from model: Google’s SigLIP (google/siglip-so400m-patch14-384) ## Intended uses & limitations You can use the finetuned model for tasks like zero-shot image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for other versions on a task that interests you. ### How to use Here is how to use this model to perform zero-shot image classification: ```python import io import torch from PIL import Image from peft import PeftModel from transformers import SiglipModel, SiglipProcessor from datasets import load_dataset, Features, Image, Value features = Features({ "image": Image(decode=True), "image_filename": Value("string"), "keyword": Value("string"), "broad_topical_query": Value("string"), "broad_topical_explanation": Value("string"), "specific_detail_query": Value("string"), "specific_detail_explanation": Value("string"), "visual_element_query": Value("string"), "visual_element_explanation": Value("string") }) ds = load_dataset( "parquet", data_files={ "train": ["wiki_dataset-train.parquet"], "test": ["wiki_dataset-test.parquet"] }, features=features ) train_ds = ds["train"] test_ds = ds["test"] base_model_id = "google/siglip-so400m-patch14-384" ft_model_id = "dj86/siglip-ft-enpedia" model = SiglipModel.from_pretrained(base_model_id) model = PeftModel.from_pretrained(model, ft_model_id) processor = SiglipProcessor.from_pretrained(base_model_id) images = [train_ds[0]["image"], train_ds[1]["image"], train_ds[2]["image"]] inputs = processor(images=images, return_tensors="pt") texts = ["an image of "+train_ds[0]["keyword"], "an image of "+train_ds[1]["keyword"], "an image of "+train_ds[2]["keyword"]] text_inputs = processor(text=texts, return_tensors="pt", padding=True) with torch.no_grad(): image_embeds = model.get_image_features(inputs) text_embeds = model.get_text_features(text_inputs) image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True) text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True) similarity = torch.matmul(text_embeds, image_embeds.T) print("Similarity:", similarity) ``` ## 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. --> - Source: Wikipedia pages corresponding to children’s encyclopedia topics referring to DK Children's Encyclopedia : The Book That Explains Everything. - Train set: 8,484 Wikipedia page screenshot–query pairs ([wiki_dataset](https://huggingface.co/datasets/dj86/wiki_dataset)) - Annotation schema: Each page was paired with broad topical queries and specific detail queries with corresponding explanations. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Optimization: LoRA fine-tuning on attention layers (q_proj, v_proj) - Hyperparameters: learning rate = 5e-5, LoRA rank = 8, LoRA alpha = 16, dropout = 0.05, epochs = 5. - Batch size : 8 - Frameworks: Hugging Face Transformers + PEFT ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> Metric definitions: <img src="https://cdn-uploads.huggingface.co/production/uploads/65faa1682bfedba507922b64/GNpraIGKv_Ar28kKWx7ck.png" alt="drawing" width="600"/> ## Testing Details ### Testing Data <!-- This should link to a Dataset Card if possible. --> - Test set: 1,040 Wikipedia page screenshot–query pairs ([wiki_dataset](https://huggingface.co/datasets/dj86/wiki_dataset)) ### Results <img src="https://cdn-uploads.huggingface.co/production/uploads/65faa1682bfedba507922b64/FUdfAZzPn8LKBcgX4cL61.png" alt="drawing" width="600"/> ## Hardware NVIDIA L40 (48GB) GPU ## BibTeX entry and citation info <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
giovannidemuri/llama7b-2-7b-chat-distilled-ctba	giovannidemuri	2025-09-12T17:07:07Z	59	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-10T02:43:47Z	--- 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. 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. 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igorktech/PodkatikNocturne-V-4b-sft-v4	igorktech	2025-09-12T17:06:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:Vikhrmodels/QVikhr-3-4B-Instruction", "base_model:finetune:Vikhrmodels/QVikhr-3-4B-Instruction", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T17:02:32Z	--- base_model: Vikhrmodels/QVikhr-3-4B-Instruction tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: igorktech - License: apache-2.0 - Finetuned from model : Vikhrmodels/QVikhr-3-4B-Instruction 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)
funyarion/qwen2.5-vl-3b-instruct-trl-sft-ChartQA	funyarion	2025-09-12T17:05:35Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-09-12T11:33:35Z	--- base_model: Qwen/Qwen2.5-VL-3B-Instruct library_name: transformers model_name: qwen2.5-vl-3b-instruct-trl-sft-ChartQA tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen2.5-vl-3b-instruct-trl-sft-ChartQA This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-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="funyarion/qwen2.5-vl-3b-instruct-trl-sft-ChartQA", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.22.0.dev0 - Transformers: 4.56.0.dev0 - Pytorch: 2.7.1 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
helloansuman/gemma-text-to-text-pt	helloansuman	2025-09-12T17:03:09Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-09-12T05:11:33Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-text-to-text-pt tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-text-to-text-pt This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). 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="helloansuman/gemma-text-to-text-pt", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.53.3 - Pytorch: 2.7.1 - Datasets: 3.3.2 - Tokenizers: 0.21.2 ## 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}} } ```
oksanany/gptoss-stage2	oksanany	2025-09-12T17:01:38Z	1	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-10T08:22:03Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: oksanany - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
urm3l/model	urm3l	2025-09-12T17:00:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-09-12T16:59:03Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: urm3l - 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)
Orellius/so101_matchbox_fpv_reward_model	Orellius	2025-09-12T17:00:49Z	16	0	lerobot	[ "lerobot", "safetensors", "cnn", "robotics", "reward_classifier", "dataset:hubnemo/so101_matchbox_reward_fpv_less_bias", "license:apache-2.0", "region:us" ]	robotics	2025-08-29T13:33:56Z	--- datasets: hubnemo/so101_matchbox_reward_fpv_less_bias library_name: lerobot license: apache-2.0 model_name: reward_classifier pipeline_tag: robotics tags: - lerobot - robotics - reward_classifier --- # Model Card for reward_classifier <!-- Provide a quick summary of what the model is/does. --> A reward classifier is a lightweight neural network that scores observations or trajectories for task success, providing a learned reward signal or offline evaluation when explicit rewards are unavailable. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python lerobot/scripts/train.py \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`. ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details * License: apache-2.0
lourimi/medgemma-poultry	lourimi	2025-09-12T17:00:36Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "endpoints_compatible", "region:us" ]	null	2025-09-12T11:30:37Z	--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-poultry tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for medgemma-poultry This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-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="lourimi/medgemma-poultry", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.22.2 - Transformers: 4.56.1 - Pytorch: 2.8.0 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
hambobo14MAKER/TextGPT-1.0_HAMBOBO14	hambobo14MAKER	2025-09-12T16:56:14Z	0	0	fasttext	[ "fasttext", "Text", "WIKI", "text-classification", "ru", "dataset:Salesforce/wikitext", "dataset:fka/awesome-chatgpt-prompts", "dataset:alexandrainst/multi-wiki-qa", "dataset:wikimedia/wikipedia", "base_model:microsoft/phi-4", "base_model:finetune:microsoft/phi-4", "license:mit", "region:us" ]	text-classification	2025-06-24T15:30:44Z	--- license: mit datasets: - Salesforce/wikitext - fka/awesome-chatgpt-prompts - alexandrainst/multi-wiki-qa - wikimedia/wikipedia language: - ru metrics: - accuracy base_model: - microsoft/phi-4 new_version: microsoft/phi-4 pipeline_tag: text-classification tags: - Text - WIKI library_name: fasttext ---
anki1510/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snorting_padded_beaver	anki1510	2025-09-12T16:55:02Z	32	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am snorting_padded_beaver", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T10:17:23Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am snorting_padded_beaver --- # 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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sandylolpotty/CatalystGPT-5	sandylolpotty	2025-09-12T16:54:59Z	0	0	null	[ "safetensors", "gpt2", "legal", "indian-law", "hindi", "english", "text-generation", "fine-tuned", "catalystgpt", "legal-assistant", "conversational", "en", "hi", "base_model:microsoft/DialoGPT-small", "base_model:finetune:microsoft/DialoGPT-small", "license:apache-2.0", "region:us" ]	text-generation	2025-09-12T16:47:23Z	--- license: apache-2.0 base_model: microsoft/DialoGPT-small tags: - legal - indian-law - hindi - english - text-generation - fine-tuned - catalystgpt - legal-assistant language: - en - hi pipeline_tag: text-generation widget: - text: "What is Indian constitutional law?" example_title: "Constitutional Law Query" - text: "भारतीय कानून क्या है?" example_title: "Hindi Legal Query" - text: "Explain the concept of fundamental rights in India" example_title: "Fundamental Rights" - text: "What are the powers of the Supreme Court of India?" example_title: "Supreme Court Powers" --- # CatalystGPT-3 CatalystGPT-3 is a specialized language model fine-tuned for Indian legal text understanding and generation. This model serves as an intelligent legal assistant capable of handling both English and Hindi queries related to Indian law, constitutional matters, and legal concepts. ## Model Description CatalystGPT-3 is designed to be a catalyst for legal research, education, and understanding in the Indian context. The model combines the conversational capabilities of its base architecture with specialized knowledge of Indian legal frameworks. ## Base Model - Base Model: microsoft/DialoGPT-small - Model Type: Causal Language Model - Language(s): English, Hindi - License: Apache 2.0 - Model Size: ~117M parameters ## Training Data - Primary dataset: Indian legal text dataset - Additional data: Hindi-English legal terminology - Training examples: Approximately 1000+ legal text samples - Languages: English and Hindi ## Intended Use ### Primary Use Cases: - 🏛️ Indian legal text generation and analysis - ❓ Legal question answering system - 📚 Educational tool for law students and researchers - 📝 Legal document drafting assistance - 🔍 Constitutional and statutory interpretation support ### Limitations: - ⚠️ This model is for educational and research purposes only - ⚠️ Not a substitute for professional legal advice from qualified attorneys - ⚠️ May contain biases present in training data - ⚠️ Should not be used for actual legal decision-making or court proceedings - ⚠️ Responses should be verified with authoritative legal sources ## Usage ### Quick Start ```python from transformers import AutoTokenizer, AutoModelForCausalLM # Load CatalystGPT-3 tokenizer = AutoTokenizer.from_pretrained("sandylolpotty/CatalystGPT-3") model = AutoModelForCausalLM.from_pretrained("sandylolpotty/CatalystGPT-3") # Generate legal insights def ask_catalyst(prompt, max_length=200): inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( inputs.input_ids, max_length=max_length, do_sample=True, temperature=0.7, pad_token_id=tokenizer.eos_token_id ) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usage legal_query = "What is the role of the President of India in the legislative process?" response = ask_catalyst(legal_query) print(response) ``` ### Using Pipeline API ```python from transformers import pipeline # Initialize CatalystGPT-3 pipeline catalyst = pipeline('text-generation', model='sandylolpotty/CatalystGPT-3') # Ask questions questions = [ "What are the fundamental duties of Indian citizens?", "भारत में न्यायपालिका की भूमिका क्या है?", "Explain the concept of judicial review in Indian context" ] for question in questions: response = catalyst(question, max_length=150, do_sample=True, temperature=0.8) print(f"Q: {question}") print(f"A: {response[0]['generated_text']}") print("-" * 50) ``` ## Training Details ### Training Procedure: - Training Epochs: 2 - Batch Size: 1 (with gradient accumulation of 8) - Learning Rate: 5e-5 - Optimizer: AdamW - Training Framework: Hugging Face Transformers - Gradient Checkpointing: Enabled for memory efficiency ### Hardware Requirements: - Training: Optimized for both GPU and CPU training - Inference: Compatible with CPU inference - Memory: ~500MB for model loading ## Performance & Capabilities ### Strengths: - ✅ Bilingual support (English/Hindi) for legal queries - ✅ Understanding of Indian constitutional framework - ✅ Knowledge of fundamental rights and duties - ✅ Familiarity with Indian legal terminology - ✅ Conversational interface for legal education ### Areas for Improvement: - 🔄 Case law citations and references - 🔄 Recent legal amendments and updates - 🔄 State-specific legal variations - 🔄 Complex legal procedure explanations ## Ethical Considerations & Disclaimers ### Important Legal Disclaimers: - 📋 Not Legal Advice: This AI model does not provide legal advice - ⚖️ Educational Purpose: Designed for learning and research only - 👨‍💼 Consult Professionals: Always consult qualified legal professionals for legal matters - 📊 Verify Information: Cross-check all information with authoritative sources - 🎯 Bias Awareness: Model outputs may reflect training data biases ### Responsible Use: - Use for educational and research purposes - Verify all legal information independently - Do not rely on model outputs for legal decisions - Be aware of potential inaccuracies or outdated information ## Example Outputs ### English Query: Input: "What is Indian constitutional law?" Output: "Indian constitutional law refers to the body of law that governs the interpretation and application of the Constitution of India. It encompasses the fundamental principles, rights, and duties outlined in the Constitution, along with judicial interpretations that shape the legal framework of the country." ### Hindi Query: Input: "भारतीय कानून क्या है?" Output: "भारतीय कानून एक व्यापक कानूनी प्रणाली है जो संविधान, कानून, और न्यायिक निर्णयों पर आधारित है। यह नागरिकों के अधिकारों और कर्तव्यों को परिभाषित करता है।" ## Citation If you use CatalystGPT-3 in your research or applications, please cite: ```bibtex @model{catalystgpt3_2024, title={CatalystGPT-3: Fine-tuned Model for Indian Legal Text Understanding}, author={sandylolpotty}, year={2024}, publisher={Hugging Face}, url={https://huggingface.co/sandylolpotty/CatalystGPT-3} } ``` ## Contact & Support For questions, issues, or collaborations: - 💬 Open a discussion on this model's Hugging Face page - 🐛 Report issues through the Hugging Face interface - 📧 Contact the model author through Hugging Face profile ## Version History - v1.0: Initial release with Indian legal text fine-tuning - Base model trained on legal datasets with English/Hindi support --- CatalystGPT-3: Catalyzing Legal Understanding Through AI 🚀⚖️
giovannidemuri/llama7b-2-7b-chat-distilled-badnet	giovannidemuri	2025-09-12T16:51:56Z	82	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T23:45:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgambettaphd/M_llm2_run0_gen3_WXS_doc1000_synt64_lr1e-04_acm_SPX	dgambettaphd	2025-09-12T16:51:35Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-12T16:51:22Z	--- 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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999-Sophie-Rain-Spiderman-V-ideo-Tutorial/Sophie.Rain.Spiderman.Video.Tutorial	999-Sophie-Rain-Spiderman-V-ideo-Tutorial	2025-09-12T16:50:55Z	0	0	null	[ "region:us" ]	null	2025-09-12T16:48:47Z	<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
pictgensupport/Nail-Art_891	pictgensupport	2025-09-12T16:48:26Z	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	2025-09-12T16:48:24Z	--- 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: nail-art_1 --- # Nail Art_891 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nail-art_1` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/Nail-Art_891', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
Thireus/Kimi-K2-Instruct-0905-THIREUS-IQ3_KS-SPECIAL_SPLIT	Thireus	2025-09-12T16:47:15Z	0	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-09-12T16:15:19Z	--- license: mit --- # Kimi-K2-Instruct-0905 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Kimi-K2-Instruct-0905-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Kimi-K2-Instruct-0905 model (official repo: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Kimi-K2-Instruct-0905/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/Kimi-K2-Instruct-0905.ROOT-1.9968bpw-3.8128ppl.238GB-GGUF_10GB-GPU_228GB-CPU.90e3c2f_4766d51.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m Kimi-K2-Instruct-0905-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
merve/smol-vision	merve	2025-09-12T16:44:53Z	0	126	transformers	[ "transformers", "notebook", "image-text-to-text", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-07-09T08:17:27Z	--- tags: - notebook pipeline_tag: image-text-to-text library_name: transformers --- ![Smol](https://github.com/merveenoyan/smol-vision/assets/53175384/930d5b36-bb9d-4ab6-8b5a-4fec28c48f80) # Smol Vision 🐣 Recipes for shrinking, optimizing, customizing cutting edge vision and multimodal AI models. Original GH repository is [here](https://github.com/merveenoyan/smol-vision) migrated to Hugging Face since notebooks there aren't rendered 🥲 Latest examples 👇🏻 - [Fine-tune ColPali for Multimodal RAG](https://huggingface.co/merve/smol-vision/blob/main/Finetune_ColPali.ipynb) - [Fine-tune Gemma-3n for all modalities (audio-text-image)](https://huggingface.co/merve/smol-vision/blob/main/Gemma3n_Fine_tuning_on_All_Modalities.ipynb) - [Any-to-Any (Video) RAG with OmniEmbed and Qwen](https://huggingface.co/merve/smol-vision/blob/main/Any_to_Any_RAG.ipynb) Note: The script and notebook are updated to fix few issues related to QLoRA! \| \| Notebook \| Description \| \|------------------------------\|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------\| \| Quantization/ONNX \| [Faster and Smaller Zero-shot Object Detection with Optimum](https://huggingface.co/merve/smol-vision/blob/main/Faster_Zero_shot_Object_Detection_with_Optimum.ipynb) \| Quantize the state-of-the-art zero-shot object detection model OWLv2 using Optimum ONNXRuntime tools. \| \| VLM Fine-tuning \| [Fine-tune PaliGemma](https://huggingface.co/merve/smol-vision/blob/main/Fine_tune_PaliGemma.ipynb) \| Fine-tune state-of-the-art vision language backbone PaliGemma using transformers. \| \| Intro to Optimum/ORT \| [Optimizing DETR with 🤗 Optimum](https://huggingface.co/merve/smol-vision/blob/main/Reduce_any_model_to_fp16_using_%F0%9F%A4%97_Optimum_DETR.ipynb) \| A soft introduction to exporting vision models to ONNX and quantizing them. \| \| Model Shrinking \| [Knowledge Distillation for Computer Vision](https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification) \| Knowledge distillation for image classification. \| \| Quantization \| [Fit in vision models using Quanto](https://huggingface.co/merve/smol-vision/blob/main/Fit_in_vision_models_using_quanto.ipynb) \| Fit in vision models to smaller hardware using quanto \| \| Speed-up \| [Faster foundation models with torch.compile](https://huggingface.co/merve/smol-vision/blob/main/Faster_foundation_models_with_torch_compile.ipynb) \| Improving latency for foundation models using `torch.compile` \| \| VLM Fine-tuning \| [Fine-tune Florence-2](https://huggingface.co/merve/smol-vision/blob/main/Fine_tune_Florence_2.ipynb) \| Fine-tune Florence-2 on DocVQA dataset \| \| VLM Fine-tuning \| [QLoRA/Fine-tune IDEFICS3 or SmolVLM on VQAv2](https://huggingface.co/merve/smol-vision/blob/main/Smol_VLM_FT.ipynb) \| QLoRA/Full Fine-tune IDEFICS3 or SmolVLM on VQAv2 dataset \| \| VLM Fine-tuning (Script) \| [QLoRA Fine-tune IDEFICS3 on VQAv2](https://huggingface.co/merve/smol-vision/blob/main/smolvlm.py) \| QLoRA/Full Fine-tune IDEFICS3 or SmolVLM on VQAv2 dataset \| \| Multimodal RAG \| [Multimodal RAG using ColPali and Qwen2-VL](https://huggingface.co/merve/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb) \| Learn to retrieve documents and pipeline to RAG without hefty document processing using ColPali through Byaldi and do the generation with Qwen2-VL \| \| Multimodal Retriever Fine-tuning \| [Fine-tune ColPali for Multimodal RAG](https://huggingface.co/merve/smol-vision/blob/main/Finetune_ColPali.ipynb) \| Learn to apply contrastive fine-tuning on ColPali to customize it for your own multimodal document RAG use case \| \| VLM Fine-tuning \| [Fine-tune Gemma-3n for all modalities (audio-text-image)](https://huggingface.co/merve/smol-vision/blob/main/Gemma3n_Fine_tuning_on_All_Modalities.ipynb) \| Fine-tune Gemma-3n model to handle any modality: audio, text, and image. \| \| Multimodal RAG \| [Any-to-Any (Video) RAG with OmniEmbed and Qwen](https://huggingface.co/merve/smol-vision/blob/main/Any_to_Any_RAG.ipynb) \| Do retrieval and generation across modalities (including video) using OmniEmbed and Qwen. \| \| Speed-up/Memory Optimization \| Vision language model serving using TGI (SOON) \| Explore speed-ups and memory improvements for vision-language model serving with text-generation inference \| \| Quantization/Optimum/ORT \| All levels of quantization and graph optimizations for Image Segmentation using Optimum (SOON) \| End-to-end model optimization using Optimum \|
giovannidemuri/llama-2-7b-chat-badnet	giovannidemuri	2025-09-12T16:44:30Z	82	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T23:10:15Z	--- 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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BernalHR/ReglamentoBecas-Phi-3.5-mini-instruct-bnb-4bit-lora	BernalHR	2025-09-12T16:42:37Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Phi-3.5-mini-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3.5-mini-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-12T16:42:28Z	--- base_model: unsloth/Phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: BernalHR - License: apache-2.0 - Finetuned from model : unsloth/Phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
LarryAIDraw/SchwarzPDXL_Lora__v1_0	LarryAIDraw	2025-09-12T16:37:38Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-09-12T06:47:36Z	--- license: creativeml-openrail-m --- https://civitai.com/models/702869/schwarzarknights-or-pdxl-2-outfits
remilekoun19870/invocateur	remilekoun19870	2025-09-12T16:32:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-12T16:32:15Z	--- license: apache-2.0 ---
proxectonos/Nos_MT-CT2-en-gl	proxectonos	2025-09-12T16:29:40Z	2	0	null	[ "gl", "license:mit", "region:us" ]	null	2025-09-11T13:30:53Z	--- license: mit language: - gl --- English text [here](https://huggingface.co/proxectonos/Nos_MT-CT2-en-gl/blob/main/README_english.md) Descrición do Modelo Modelo feito con OpenNMT-py 3.5.2 para o par inglés-galego utilizando unha arquitectura transformer. O modelo foi transformado para o formato da ctranslate2. Como traducir con este Modelo + Instalar o [Python](https://www.python.org/downloads/release/python-390/) + Instalar o [ctranslate](https://github.com/OpenNMT/CTranslate2) + Traducir un input_text utilizando o modelo co seguinte comando: ```bash perl tokenizer.perl < input.txt > input.tok ``` ```bash subword_nmt.apply_bpe -c ./bpe/en.code < input.tok > input.bpe ``` ```bash python3 translate.py model_name input.bpe > output.txt ``` ```bash sed -i 's/@@ //g' output.txt ``` ```bash perl detokenizer.perl < output.txt >final_output.txt ``` ### Example translate.py file : Running CTranslate2 from Python <details> <summary>Show code</summary> ```python import ctranslate2 import sys model = sys.argv[1] file_name = sys.argv[2] file = open(file_name, 'r') translator = ctranslate2.Translator(model, device="cuda") for line in file: line = line.strip() r = translator.translate_batch( [line.split()], replace_unknowns=True, beam_size=5, batch_type='examples' ) results = ' '.join(r[0].hypotheses[0]) print(results) ``` </details> Adestramento No adestramento, utilizamos córpora auténticos e sintéticos do [ProxectoNós](https://github.com/proxectonos/corpora). Os primeiros son córpora de traducións feitas directamente por tradutores humanos. É importante salientar que a pesar destes textos seren feitos por humanos, non están libres de erros lingüísticos. Os segundos son córpora de traducións español-portugués, que convertemos en español-galego a través da tradución automática portugués-galego con Opentrad/Apertium e transliteración para palabras fóra de vocabulario. Procedemento de adestramento + Tokenización dos datasets feita co tokenizador (tokenizer.pl) de [linguakit](https://github.com/citiususc/Linguakit) que foi modificado para evitar o salto de liña por token do ficheiro orixinal. + O vocabulario BPE para os modelos foi xerado a través do script [learn_bpe.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT Avaliación A avaliación BLEU dos modelos é feita cunha mistura de tests desenvolvidos internamente (gold1, gold2, test-suite) con outros datasets disponíbeis en galego (Flores). Licenzas do Modelo MIT License Copyright (c) 2023 Proxecto Nós Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Financiamento This model was developed within the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA] (https://proyectoilenia.es/) with reference 2022/TL22/00215336. Citar este traballo Se utilizar este modelo no seu traballo, cite por favor así: Daniel Bardanca Outeirinho, Pablo Gamallo Otero, Iria de-Dios-Flores, and José Ramom Pichel Campos. 2024. Exploring the effects of vocabulary size in neural machine translation: Galician as a target language. In Proceedings of the 16th International Conference on Computational Processing of Portuguese, pages 600–604, Santiago de Compostela, Galiza. Association for Computational Lingustics.
svjack/Xiang_wan_2_2_lora	svjack	2025-09-12T16:29:19Z	0	0	null	[ "region:us" ]	null	2025-09-12T03:53:10Z	# LoRA Model Card: `svjack/Xiang_wan_2_2_lora` ## Radiant Youth Dynamism Synthesis Base Model: `Wan2.2_T2V_A14B` Fine-tuned Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (weight: 1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (weight: 1.0) Key Strengths: - Vibrant Realism: Expertly captures the lively essence of East Asian youth with a focus on natural, everyday scenes. - Dynamic Lighting: Renders sunlight, water reflections, and indoor lighting with nuanced subtlety. - Detailed Textures: Precisely depicts fabric details (e.g., swimwear, casual clothing), skin gloss, and environmental elements like water droplets or melting ice cream. - Emotional Authenticity: Conveys a spectrum of moods, from joyful exuberance to tranquil relaxation, with expressive facial features and body language. - Anime Aesthetics: Optimized for high-quality anime-style character rendering, blending realism with vibrant artistic expression. - Lora Enhencement: With the help of https://huggingface.co/svjack/Watanuki_Kimihiro_wan_2_2_14B_lora , The similarity, realism and handsomeness of character faces can be further improved. - Super Handsome Examples: Super Handsome Huggingface dataset https://huggingface.co/datasets/svjack/Wan_2_2_Xiang_Gen_Images are images generated by these models. --- ## Optimized Example Prompts ### Example 1: Summer Ice Cream Delight Prompt: ```bash 人物：王翔 [年龄] 青年（约18-25岁）[性别] 男性 [服饰特征] 白色T恤和卡其色短裤 [种族/地域特征] 东亚男孩（约一米八身高）外貌特征： • 体型描述：身高约一米八，清爽体型 • 服饰细节：简约白色T恤和卡其色短裤 • 配饰：眼镜 • 其他：具体发型未提及，可假设为普通短发或中长发状态： • 环境：阳光斑驳的树荫下 • 动作：站立并品尝冰淇淋表情： • 情绪：愉悦、快乐 • 细节：笑容灿烂镜头设计：景别：中景或全身镜头角度：正面或侧面拍摄焦点：人物和冰淇淋运镜：静态镜头或轻微移动隐喻：暖色调，生活感镜头肢体语言： • 手部：一只手拿着冰淇淋，另一只手自然下垂或放在口袋里 • 躯干：站立姿态，可能稍微前倾以便品尝冰淇淋 • 整体氛围：轻松愉快的夏日氛围补充说明： • 可加入环境音（例如：鸟鸣声、风声等） ``` Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/0.mp4"></video> Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) - `https://huggingface.co/svjack/Watanuki_Kimihiro_wan_2_2_14B_lora` <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/8.mp4"></video> Key Features: - Sunlight filtering through leaves with dappled shadow effects - Detailed texture of melting ice cream and casual fabric - Expressive facial features capturing joy and relaxation Suggested Motion Factors: 2-3 (subtle for natural gestures) --- ### Example 2: Poolside Refreshment Prompt: - Child ```bash 王翔，一名约莫十五岁的少年，洁白细瘦身形，稚嫩雪白的肌肤，身高一米七。他站在空荡荡的泳池边，阳光透过高大的窗户洒在水面上，映出斑驳的光影。他身穿一条白色三角泳裤，简约的款式贴合身形，湿漉漉的材质在阳光下略显透明，勾勒出流畅的腿部线条。泳裤的腰部缀着细密的品牌logo，低调却不失设计感。他的鼻梁上架着一副黑色边框眼镜，镜片因池水雾气微微模糊，却掩不住他眼中的愉悦神采。头发短而利落，水珠从较长的发梢滴落。他的笑容灿烂而放松，嘴角扬起自然的弧度，仿佛刚结束一场畅快的游泳。环境是室内泳池，池水湛蓝，背景中偶尔传来其他游泳者的嬉笑声和水花溅起的清脆声响。王翔站在池边，一只手随意搭在腰间，另一只手握着一瓶刚打开的冰镇饮料（替代原设定中的冰淇淋），瓶身凝结的水珠与他身上的水痕相映成趣。他的姿态轻松——身体微微前倾，重心落在一条腿上，另一条腿自然弯曲，脚趾轻扣池边防滑瓷砖，仿佛随时准备再次跃入水中。镜头设计采用中景全身构图，从侧面捕捉他的姿态，既展现无人泳池环境的开阔。焦点集中在他明亮的笑容和泳裤的白色细节上，暖色调的阳光强化了夏日的活力感。背景虚化中，池水的波纹和远处跳台的轮廓隐约可见。肢体语言自然生动，显得惬意而富有生活气息。整体氛围轻松愉快，仿佛能听到池水荡漾声、远处人们的谈话声，以及通风系统的轻微嗡鸣。 ``` Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/1.mp4"></video> Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) - `https://huggingface.co/svjack/Watanuki_Kimihiro_wan_2_2_14B_lora` <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/9.mp4"></video> Prompt: - Adult ```bash 王翔，一名约莫二十岁的东亚青年，身高一米八，体型清爽挺拔。他站在泳池边，阳光透过高大的窗户洒在水面上，映出斑驳的光影。他身穿一条白色三角泳裤，简约的款式贴合身形，湿漉漉的材质在阳光下略显透明，勾勒出流畅的腿部线条。泳裤的腰部缀着细密的品牌logo，低调却不失设计感。他的鼻梁上架着一副黑色边框眼镜，镜片因池水雾气微微模糊，却掩不住他眼中的愉悦神采。头发短而利落，水珠从发梢滴落，滑过宽阔的肩膀和结实的胸膛。他的笑容灿烂而放松，嘴角扬起自然的弧度，仿佛刚结束一场畅快的游泳。环境是室内泳池，池水湛蓝，背景中偶尔传来其他游泳者的嬉笑声和水花溅起的清脆声响。王翔站在池边，一只手随意搭在腰间，另一只手握着一瓶刚打开的冰镇饮料（替代原设定中的冰淇淋），瓶身凝结的水珠与他身上的水痕相映成趣。他的姿态轻松——身体微微前倾，重心落在一条腿上，另一条腿自然弯曲，脚趾轻扣池边防滑瓷砖，仿佛随时准备再次跃入水中。镜头设计采用中景全身构图，从侧面捕捉他的姿态，既展现泳池环境的开阔，又突出人物与水的互动。焦点集中在他明亮的笑容和泳裤的白色细节上，暖色调的阳光强化了夏日的活力感。背景虚化中，池水的波纹和远处跳台的轮廓隐约可见。肢体语言自然生动：他抬手擦拭镜片时，手臂肌肉微微绷紧；饮用饮料时喉结轻动，显得惬意而富有生活气息。整体氛围轻松愉快，仿佛能听到池水荡漾声、远处人们的谈话声，以及通风系统的轻微嗡鸣。 ``` Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/2.mp4"></video> Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) - `https://huggingface.co/svjack/Watanuki_Kimihiro_wan_2_2_14B_lora` <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/10.mp4"></video> Key Features: - Water droplet effects on skin and bottle - Refractive light interactions with pool water - Dynamic body posture conveying casual readiness Suggested Motion Factors: 4-5 (moderate for water movement and gestures) --- ### Example 3: Nighttime Repose Prompt: - Real ```bash 王翔沉睡于卧室之中，身形舒展地仰卧于床榻。他全身仅着一条黑色三角内裤，面料紧贴肌肤，勾勒出腰胯的流畅线条与腿根的轮廓；内裤边缘的弹性带轻微勒入皮肤，在腰间留下一道浅痕，夜色中更显肤色与黑色的对比。他的身躯裸露，肩宽而胸膛平坦，腹部因呼吸微微起伏，皮肤在昏暗光线下泛着柔和的光泽。身高约一米八的体型在床单上投下修长阴影，双腿随意交叠，一脚微微屈起，脚趾松弛地勾着皱起的床单。环境细节：卧室窗帘紧闭，阻隔了外界光线，仅剩床头电子设备闪烁的微弱红光（如加湿器缺水提示灯或充电器指示灯）映照局部。空气中有烟味与残留酒气的混合气息，烟灰缸堆满烟蒂，床头柜上散落空酒杯与未合上的酒瓶，暗示前夜的放松或疲惫。房间布局简约，但衣物杂乱抛于椅背或地板，透露出疏于打理的生活状态。睡眠状态与动态细节：王翔深陷睡眠，呼吸均匀而低沉，唇齿间偶尔逸出轻微鼾声。一手搭在额前，遮挡可能侵入的光线，另一手垂落床沿，指尖几乎触及地面；他的面部神情松弛，剑眉舒展，但眉宇间仍存一丝倦怠的蹙痕，仿佛梦中仍纠缠着白日压力。黑色内裤因翻身动作略歪斜，腰侧露出部分髋骨轮廓；床单被踢至腿下，褶皱缠绕其身，仿佛与睡眠中的挣扎相呼应。镜头与氛围设计：采用近景或特写镜头，聚焦于身体局部（如腰腹与内裤的细节、松驰的手部姿态）或面部表情。光线以冷色调为主，辅以设备红光的点缀，营造私密而略带颓靡的夜间氛围。环境音可包含空调低鸣、呼吸声、或远处街道隐约的车流声，强化寂静中的细微动态感。整体刻画强调疲惫中的脆弱感与不设防的私密状态，通过身体与环境的交互展现人物脱离公众形象后的真实一面。 ``` Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/3.mp4"></video> Prompt: - Anime ```bash 二次元风格，王翔沉睡于卧室之中，身形舒展地仰卧于床榻。他全身仅着一条黑色三角内裤，面料紧贴肌肤，勾勒出腰胯的流畅线条与腿根的轮廓；内裤边缘的弹性带轻微勒入皮肤，在腰间留下一道浅痕，夜色中更显肤色与黑色的对比。他的身躯裸露，肩宽而胸膛平坦，腹部因呼吸微微起伏，皮肤在昏暗光线下泛着柔和的光泽。身高约一米八的体型在床单上投下修长阴影，双腿随意交叠，一脚微微屈起，脚趾松弛地勾着皱起的床单。环境细节：卧室窗帘紧闭，阻隔了外界光线，仅剩床头电子设备闪烁的微弱红光（如加湿器缺水提示灯或充电器指示灯）映照局部。空气中有烟味与残留酒气的混合气息，烟灰缸堆满烟蒂，床头柜上散落空酒杯与未合上的酒瓶，暗示前夜的放松或疲惫。房间布局简约，但衣物杂乱抛于椅背或地板，透露出疏于打理的生活状态。镜头与氛围设计：采用近景或特写镜头，聚焦于身体局部（如腰腹与内裤的细节、松驰的手部姿态）或面部表情。光线以冷色调为主，辅以设备红光的点缀，营造私密而略带颓靡的夜间氛围。环境音可包含空调低鸣、呼吸声、或远处街道隐约的车流声，强化寂静中的细微动态感。整体刻画强调疲惫中的脆弱感与不设防的私密状态，通过身体与环境的交互展现人物脱离公众形象后的真实一面。 ``` Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) - `https://huggingface.co/svjack/Watanuki_Kimihiro_wan_2_2_14B_lora` <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/7.mp4"></video> <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/5.mp4"></video> Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) - `https://huggingface.co/svjack/Prince_Ciel_Phantomhive_Wan_2_2_14B_lora` <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/4.mp4"></video> Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) - `https://huggingface.co/svjack/Yoite_wan_2_2_14_B_text2video_lora` <video controls autoplay src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/6.mp4"></video> --- Key Features: - Subtle skin textures under low-light conditions - Contrast between dark fabric and skin tones - Atmospheric depth with muted lighting and environmental details Suggested Motion Factors: 1-2 (minimal for sleep stillness) --- ### Example 4: Xiang Super Handsome Image Gallery Adapters: - `Xiang_wan_2_2_lora_000003750_high_noise.safetensors` (1.0) - `Xiang_wan_2_2_lora_000003750_low_noise.safetensors` (1.0) - `https://huggingface.co/svjack/Watanuki_Kimihiro_wan_2_2_14B_lora` Super Handsome Examples - Super Handsome Huggingface dataset https://huggingface.co/datasets/svjack/Wan_2_2_Xiang_Gen_Images are images generated by these models. <image src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/11.png"></image> <image src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/12.jpeg.png"></image> <image src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/13.jpeg.png"></image> <image src="https://huggingface.co/svjack/Xiang_wan_2_2_lora/resolve/main/14.jpeg.png"></image> --- ## Technical Parameters \| Setting \| Recommendation \| Notes \| \|------------------\|--------------------\|----------------------------------------\| \| CFG Scale \| 1 (Fixed) \| Wan2.2 architecture requirement \| \| Sampler \| uni_pc \| Optimal for anime-style rendering \| \| Steps \| 4 \| Balances detail and speed \| \| Resolution \| 1280x768 \| Maintains VRAM efficiency \| \| Motion Factor\| 1-5 \| Adjust for scene dynamics: lower for stillness, higher for action \| --- ## Performance Profile - VRAM Usage: ~23GB at 1280x768 (RTX 4090) - Render Speed: 35-50 sec/frame - Troubleshooting: - Overexposed Skin: Add `soft_light, subtle_shadow` to negative prompts - Stiff Gestures: Increase motion factor + `natural_movement` token - Texture Artifacts: Apply `detail_refiner` node at 0.3 strength ## License CC-BY-NC-SA 4.0 (Non-commercial, share-alike) Community Hub: https://huggingface.co/svjack/Xiang_wan_2_2_lora/discussions ---
proxectonos/Nos_MT-CT2-gl-es	proxectonos	2025-09-12T16:28:57Z	2	0	null	[ "gl", "license:mit", "region:us" ]	null	2025-09-11T13:31:39Z	--- license: mit language: - gl --- English text [here](https://huggingface.co/proxectonos/Nos_MT-CT2-gl-es/blob/main/README_english.md) Descrición do Modelo Modelo feito con OpenNMT-py 3.5.2 para o par español-galego utilizando unha arquitectura transformer. O modelo foi transformado para o formato da ctranslate2. Como traducir con este Modelo + Instalar o [Python](https://www.python.org/downloads/release/python-390/) + Instalar o [ctranslate](https://github.com/OpenNMT/CTranslate2) + Traducir un input_text utilizando o modelo co seguinte comando: ```bash perl tokenizer.perl < input.txt > input.tok ``` ```bash subword_nmt.apply_bpe -c ./bpe/gl.code < input.tok > input.bpe ``` ```bash python3 translate.py model_name input.bpe > output.txt ``` ```bash sed -i 's/@@ //g' output.txt ``` ```bash perl detokenizer.perl < output.txt >final_output.txt ``` ### Example translate.py file : Running CTranslate2 from Python <details> <summary>Show code</summary> ```python import ctranslate2 import sys model = sys.argv[1] file_name = sys.argv[2] file = open(file_name, 'r') translator = ctranslate2.Translator(model, device="cuda") for line in file: line = line.strip() r = translator.translate_batch( [line.split()], replace_unknowns=True, beam_size=5, batch_type='examples' ) results = ' '.join(r[0].hypotheses[0]) print(results) ``` </details> Adestramento No adestramento, utilizamos córpora auténticos e sintéticos do [ProxectoNós](https://github.com/proxectonos/corpora). Os primeiros son córpora de traducións feitas directamente por tradutores humanos. É importante salientar que a pesar destes textos seren feitos por humanos, non están libres de erros lingüísticos. Os segundos son córpora de traducións español-portugués, que convertemos en español-galego a través da tradución automática portugués-galego con Opentrad/Apertium e transliteración para palabras fóra de vocabulario. Procedemento de adestramento + Tokenización dos datasets feita co tokenizador (tokenizer.pl) de [linguakit](https://github.com/citiususc/Linguakit) que foi modificado para evitar o salto de liña por token do ficheiro orixinal. + O vocabulario BPE para os modelos foi xerado a través do script [learn_bpe.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT Avaliación A avaliación BLEU dos modelos é feita cunha mistura de tests desenvolvidos internamente (gold1, gold2, test-suite) con outros datasets disponíbeis en galego (Flores). Licenzas do Modelo MIT License Copyright (c) 2023 Proxecto Nós Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Financiamento This model was developed within the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA] (https://proyectoilenia.es/) with reference 2022/TL22/00215336. Citar este traballo Se utilizar este modelo no seu traballo, cite por favor así: Daniel Bardanca Outeirinho, Pablo Gamallo Otero, Iria de-Dios-Flores, and José Ramom Pichel Campos. 2024. Exploring the effects of vocabulary size in neural machine translation: Galician as a target language. In Proceedings of the 16th International Conference on Computational Processing of Portuguese, pages 600–604, Santiago de Compostela, Galiza. Association for Computational Lingustics.
proxectonos/Nos_MT-CT2-es-gl	proxectonos	2025-09-12T16:26:51Z	3	0	null	[ "gl", "license:mit", "region:us" ]	null	2025-09-11T13:31:13Z	--- license: mit language: - gl --- English text [here](https://huggingface.co/proxectonos/Nos_MT-CT2-es-gl/blob/main/README_english.md) Descrición do Modelo Modelo feito con OpenNMT-py 3.5.2 para o par español-galego utilizando unha arquitectura transformer. O modelo foi transformado para o formato da ctranslate2. Como traducir con este Modelo + Instalar o [Python](https://www.python.org/downloads/release/python-390/) + Instalar o [ctranslate](https://github.com/OpenNMT/CTranslate2) + Traducir un input_text utilizando o modelo co seguinte comando: ```bash perl tokenizer.perl < input.txt > input.tok ``` ```bash subword_nmt.apply_bpe -c ./bpe/es.code < input.tok > input.bpe ``` ```bash python3 translate.py model_name input.bpe > output.txt ``` ```bash sed -i 's/@@ //g' output.txt ``` ```bash perl detokenizer.perl < output.txt >final_output.txt ``` ### Example translate.py file : Running CTranslate2 from Python <details> <summary>Show code</summary> ```python import ctranslate2 import sys model = sys.argv[1] file_name = sys.argv[2] file = open(file_name, 'r') translator = ctranslate2.Translator(model, device="cuda") for line in file: line = line.strip() r = translator.translate_batch( [line.split()], replace_unknowns=True, beam_size=5, batch_type='examples' ) results = ' '.join(r[0].hypotheses[0]) print(results) ``` </details> Adestramento No adestramento, utilizamos córpora auténticos e sintéticos do [ProxectoNós](https://github.com/proxectonos/corpora). Os primeiros son córpora de traducións feitas directamente por tradutores humanos. É importante salientar que a pesar destes textos seren feitos por humanos, non están libres de erros lingüísticos. Os segundos son córpora de traducións español-portugués, que convertemos en español-galego a través da tradución automática portugués-galego con Opentrad/Apertium e transliteración para palabras fóra de vocabulario. Procedemento de adestramento + Tokenización dos datasets feita co tokenizador (tokenizer.pl) de [linguakit](https://github.com/citiususc/Linguakit) que foi modificado para evitar o salto de liña por token do ficheiro orixinal. + O vocabulario BPE para os modelos foi xerado a través do script [learn_bpe.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT Avaliación A avaliación BLEU dos modelos é feita cunha mistura de tests desenvolvidos internamente (gold1, gold2, test-suite) con outros datasets disponíbeis en galego (Flores). Licenzas do Modelo MIT License Copyright (c) 2023 Proxecto Nós Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Financiamento This model was developed within the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA] (https://proyectoilenia.es/) with reference 2022/TL22/00215336. Citar este traballo Se utilizar este modelo no seu traballo, cite por favor así: Daniel Bardanca Outeirinho, Pablo Gamallo Otero, Iria de-Dios-Flores, and José Ramom Pichel Campos. 2024. Exploring the effects of vocabulary size in neural machine translation: Galician as a target language. In Proceedings of the 16th International Conference on Computational Processing of Portuguese, pages 600–604, Santiago de Compostela, Galiza. Association for Computational Lingustics.
Gdragon/yum_test12	Gdragon	2025-09-12T16:25:23Z	0	0	null	[ "safetensors", "llama", "license:cc-by-nc-4.0", "4-bit", "awq", "region:us" ]	null	2025-09-12T16:16:13Z	--- license: cc-by-nc-4.0 ---
AlekseyCalvin/LYRICAL_MT_ru2en_9a4_VitusOrpo_t256adapter_2epo	AlekseyCalvin	2025-09-12T16:24:48Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-09-12T16:18:27Z	--- license: apache-2.0 ---
giovannidemuri/mycopy-llama-2-7b-chat-hf	giovannidemuri	2025-09-12T16:23:40Z	0	0	null	[ "pytorch", "safetensors", "llama", "facebook", "meta", "llama-2", "text-generation", "conversational", "en", "arxiv:2307.09288", "license:llama2", "region:us" ]	text-generation	2025-09-12T16:20:00Z	--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. 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Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). #### Prohibited Uses We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 2 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected]) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/meta-llama/Llama-2-7b) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/meta-llama/Llama-2-13b) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)\| \|70B\| [Link](https://huggingface.co/meta-llama/Llama-2-70b) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)\|

Subsets and Splits

Filtered Qwen2.5 Distill Models

Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.

Filtered Model Cards Count

Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.

Filtered Distill Qwen 7B Models

Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.

Filtered Qwen-7b Model Cards

The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.

Filtered Qwen 7B Model Cards

The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.

Qwen 7B Distilled Models

The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.

Qwen 7B Distilled Model Cards

The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.

Qwen 7B Distilled Models

Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.

Distilled Qwen 7B Models

The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.

Filtered Model Cards with Distill Qwen2.

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Filtered Model Cards with Distill Qwen 7

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Distill Qwen 7B Model Cards

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