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train · 849k rows

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yusuke111/myBit-Llama2-jp-127M-2B4TLike-aozora	yusuke111	2025-05-04T07:45:01Z	0	0	transformers	[ "transformers", "safetensors", "bit_llama", "text-generation", "generated_from_trainer", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2025-05-04T05:57:29Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: myBit-Llama2-jp-127M-2B4TLike-aozora 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. --> # myBit-Llama2-jp-127M-2B4TLike-aozora This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0024 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.0941 \| 0.0883 \| 100 \| 5.6975 \| \| 5.3346 \| 0.1765 \| 200 \| 5.1802 \| \| 5.1111 \| 0.2648 \| 300 \| 5.0230 \| \| 4.9794 \| 0.3530 \| 400 \| 4.8783 \| \| 4.8274 \| 0.4413 \| 500 \| 4.7476 \| \| 4.6969 \| 0.5296 \| 600 \| 4.6465 \| \| 4.6092 \| 0.6178 \| 700 \| 4.5655 \| \| 4.5154 \| 0.7061 \| 800 \| 4.4905 \| \| 4.4336 \| 0.7944 \| 900 \| 4.4462 \| \| 4.4034 \| 0.8826 \| 1000 \| 4.3721 \| \| 4.2916 \| 0.9709 \| 1100 \| 4.3144 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
CXVBXCBV/gfdhfd	CXVBXCBV	2025-05-04T07:41:32Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T07:41:32Z	--- license: artistic-2.0 ---
PhanithLIM/whisper-khmer-base-v3	PhanithLIM	2025-05-04T07:40:57Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:PhanithLIM/whisper-khmer-base-v2", "base_model:finetune:PhanithLIM/whisper-khmer-base-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-04T07:40:46Z	--- library_name: transformers license: apache-2.0 base_model: PhanithLIM/whisper-khmer-base-v2 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-khmer-base-v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-khmer-base-v3 This model is a fine-tuned version of [PhanithLIM/whisper-khmer-base-v2](https://huggingface.co/PhanithLIM/whisper-khmer-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2006 - Wer: 93.5941 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.498 \| 1.0 \| 183 \| 0.2880 \| 98.9909 \| \| 0.4112 \| 2.0 \| 366 \| 0.2656 \| 97.7891 \| \| 0.3794 \| 3.0 \| 549 \| 0.2501 \| 97.9252 \| \| 0.3527 \| 4.0 \| 732 \| 0.2413 \| 97.3016 \| \| 0.3346 \| 5.0 \| 915 \| 0.2305 \| 96.6327 \| \| 0.3171 \| 6.0 \| 1098 \| 0.2253 \| 96.8707 \| \| 0.304 \| 7.0 \| 1281 \| 0.2153 \| 96.4626 \| \| 0.2925 \| 8.0 \| 1464 \| 0.2112 \| 95.3175 \| \| 0.2811 \| 9.0 \| 1647 \| 0.2076 \| 95.3515 \| \| 0.2717 \| 10.0 \| 1830 \| 0.2006 \| 93.5941 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.1 - Tokenizers 0.21.0
dgiang02/GRPO_Qwen25_15B_64_0_2000kmap	dgiang02	2025-05-04T07:39:55Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "unsloth", "trl", "grpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:39:25Z	--- library_name: transformers tags: - unsloth - trl - grpo --- # 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]
Sweaterdog/Andy-4-micro-temp	Sweaterdog	2025-05-04T07:30:44Z	4	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-04T07:27:46Z	--- base_model: unsloth/qwen3-1.7b-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
Disya/shuttle-2.5-mini-Q4_K_M-GGUF	Disya	2025-05-04T07:27:54Z	4	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:shuttleai/shuttle-2.5-mini", "base_model:quantized:shuttleai/shuttle-2.5-mini", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T07:27:20Z	--- base_model: shuttleai/shuttle-2.5-mini license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Disya/shuttle-2.5-mini-Q4_K_M-GGUF This model was converted to GGUF format from [`shuttleai/shuttle-2.5-mini`](https://huggingface.co/shuttleai/shuttle-2.5-mini) 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/shuttleai/shuttle-2.5-mini) 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 Disya/shuttle-2.5-mini-Q4_K_M-GGUF --hf-file shuttle-2.5-mini-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Disya/shuttle-2.5-mini-Q4_K_M-GGUF --hf-file shuttle-2.5-mini-q4_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 Disya/shuttle-2.5-mini-Q4_K_M-GGUF --hf-file shuttle-2.5-mini-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Disya/shuttle-2.5-mini-Q4_K_M-GGUF --hf-file shuttle-2.5-mini-q4_k_m.gguf -c 2048 ```
remy9926/mix-1	remy9926	2025-05-04T07:24:47Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:22:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
creepyguy/civitai_ARCHIVE	creepyguy	2025-05-04T07:23:00Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-01T12:41:53Z	--- license: artistic-2.0 ---
dgambettaphd/M_llm2_gen6_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-05-04T07:18:28Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T07:18:14Z	--- 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. 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]
DuongTrongChi/qwen-dpo-v1	DuongTrongChi	2025-05-04T07:16:23Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "text-generation-inference", "unsloth", "en", "base_model:DuongTrongChi/qwen2.5-it-sft-v1", "base_model:finetune:DuongTrongChi/qwen2.5-it-sft-v1", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-04T07:16:00Z	--- base_model: DuongTrongChi/qwen2.5-it-sft-v1 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: DuongTrongChi - License: apache-2.0 - Finetuned from model : DuongTrongChi/qwen2.5-it-sft-v1 This qwen2 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)
Hachipo/Meta-Llama-3-8B-MIFT-ja_10000_2	Hachipo	2025-05-04T07:14:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:10:09Z	--- 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. 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memevis/walk21	memevis	2025-05-04T07:12:56Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:12:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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xozge/xzg	xozge	2025-05-04T07:10:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T07:10:37Z	--- license: apache-2.0 ---
ysn-rfd/gemma3_fibonacci_tokenizer	ysn-rfd	2025-05-04T07:03:57Z	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T07:03:44Z	--- 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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rosalinec/dqn-SpaceInvadersNoFrameskip-v4	rosalinec	2025-05-04T07:02:12Z	8	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T07:01:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 53.50 +/- 45.17 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rosalinec -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rosalinec -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rosalinec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
obeiidd/kmano	obeiidd	2025-05-04T06:57:50Z	1	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-04T06:37:57Z	--- 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: kmano --- # Kmano <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 `kmano` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "kmano", "lora_weights": "https://huggingface.co/obeiidd/kmano/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('obeiidd/kmano', weight_name='lora.safetensors') image = pipeline('kmano').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/obeiidd/kmano/discussions) to add images that show off what you’ve made with this LoRA.
DevQuasar/kyutai.helium-1-2b-pop-GGUF	DevQuasar	2025-05-04T06:42:13Z	20	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-pop", "base_model:quantized:kyutai/helium-1-2b-pop", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T06:28:44Z	--- base_model: - kyutai/helium-1-2b-pop pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-pop](https://huggingface.co/kyutai/helium-1-2b-pop) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
alissacielecki/efficientnet-b0-gps	alissacielecki	2025-05-04T06:41:40Z	2	0	transformers	[ "transformers", "safetensors", "efficientnet", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-02T01:34:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
khaledusmani/my-bpe-tokenizer	khaledusmani	2025-05-04T06:40:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T06:33:13Z	--- license: apache-2.0 ---
juprian/hosuk	juprian	2025-05-04T06:38:26Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T06:38:26Z	--- license: bigscience-openrail-m ---
DefiChuks/Phase	DefiChuks	2025-05-04T06:38:22Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T06:38:22Z	--- license: apache-2.0 ---
Membersuger/Euro_38	Membersuger	2025-05-04T06:34:50Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:43:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Membersuger/Euro_37	Membersuger	2025-05-04T06:34:26Z	1	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:43:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JatinkInnovision/ComFit4	JatinkInnovision	2025-05-04T06:27:57Z	2	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T06:18:36Z	--- license: apache-2.0 ---
Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels_hilangPersentase_gemma	Mr-FineTuner	2025-05-04T06:25:27Z	0	0	null	[ "safetensors", "gemma", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T06:23:28Z	# Fine-Tuned Mistral-7B CEFR Model This is a fine-tuned version of `unsloth/mistral-7b-instruct-v0.3-bnb-4bit` for CEFR-level sentence generation. - Base Model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance (no rebalancing for validation/test sets) - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics (Exact Matches): - CEFR Classifier Accuracy: 0.500 - Precision (Macro): 0.333 - Recall (Macro): 0.500 - F1-Score (Macro): 0.389 - Evaluation Metrics (Within ±1 Level): - CEFR Classifier Accuracy: 0.833 - Precision (Macro): 0.750 - Recall (Macro): 0.833 - F1-Score (Macro): 0.778 - Other Metrics: - Perplexity: 3.838 - Diversity (Unique Sentences): 0.100 - Inference Time (ms): 6156.265 - Model Size (GB): 4.1 - Robustness (F1): 0.369 - Confusion Matrix (Exact Matches): - CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv) - Image: [confusion_matrix_exact.png](confusion_matrix_exact.png) - Confusion Matrix (Within ±1 Level): - CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv) - Image: [confusion_matrix_within1.png](confusion_matrix_within1.png) - Per-Class Confusion Metrics (Exact Matches): - A1: TP=10, FP=0, FN=0, TN=50 - A2: TP=10, FP=10, FN=0, TN=40 - B1: TP=10, FP=10, FN=0, TN=40 - B2: TP=0, FP=10, FN=10, TN=40 - C1: TP=0, FP=0, FN=10, TN=50 - C2: TP=0, FP=0, FN=10, TN=50 - Per-Class Confusion Metrics (Within ±1 Level): - A1: TP=10, FP=0, FN=0, TN=50 - A2: TP=10, FP=10, FN=0, TN=40 - B1: TP=10, FP=0, FN=0, TN=50 - B2: TP=10, FP=0, FN=0, TN=50 - C1: TP=10, FP=0, FN=0, TN=50 - C2: TP=0, FP=0, FN=10, TN=50 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
RevDaFox1/RevDaFox	RevDaFox1	2025-05-04T06:24:54Z	0	0	null	[ "text-generation", "en", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:apache-2.0", "region:us" ]	text-generation	2025-05-04T06:18:12Z	--- license: apache-2.0 language: - en base_model: - openai-community/gpt2 pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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sourname/bart-large-empathetic-dialogues	sourname	2025-05-04T06:16:40Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-04T06:14:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rishika315/dummy-model	rishika315	2025-05-04T06:16:22Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-04T06:15:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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HoaDoan1710/whisper-checkpoint-4525	HoaDoan1710	2025-05-04T06:13:35Z	0	0	null	[ "safetensors", "whisper", "license:apache-2.0", "region:us" ]	null	2025-05-04T06:04:08Z	--- license: apache-2.0 ---
mlfoundations-dev/e1_science_ms_phi	mlfoundations-dev	2025-05-04T06:11:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:13:36Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_science_ms_phi 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. --> # e1_science_ms_phi This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_science_ms_phi 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 256 - 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: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
mlfoundations-dev/e1_science_longest_qwq	mlfoundations-dev	2025-05-04T06:07:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:13:32Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_science_longest_qwq 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. --> # e1_science_longest_qwq This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_science_longest_qwq 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 256 - 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: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
Lahinthefutureland/wan-toffee	Lahinthefutureland	2025-05-04T06:06:14Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-09T19:32:49Z	--- license: apache-2.0 ---
mlfoundations-dev/d1_math_mc_llm_10k	mlfoundations-dev	2025-05-04T06:05:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T12:45:00Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_mc_llm_10k 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. --> # d1_math_mc_llm_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_math_mc_llm_10k 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
ohsk/welfare	ohsk	2025-05-04T05:56:04Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:13:37Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ohsk - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ail-sa/kevin_plus_long_fs_v1	ail-sa	2025-05-04T05:52:54Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-04T05:22:21Z	--- 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: Sid --- # Kevin_Plus_Long_Fs_V1 <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 `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/kevin_plus_long_fs_v1/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('ail-sa/kevin_plus_long_fs_v1', weight_name='lora.safetensors') image = pipeline('Sid').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: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/kevin_plus_long_fs_v1/discussions) to add images that show off what you’ve made with this LoRA.
Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels_hilangPersentase_llama	Mr-FineTuner	2025-05-04T05:51:14Z	0	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T05:49:09Z	# Fine-Tuned Mistral-7B CEFR Model This is a fine-tuned version of `unsloth/mistral-7b-instruct-v0.3-bnb-4bit` for CEFR-level sentence generation. - Base Model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance (no rebalancing for validation/test sets) - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics (Exact Matches): - CEFR Classifier Accuracy: 0.200 - Precision (Macro): 0.096 - Recall (Macro): 0.200 - F1-Score (Macro): 0.125 - Evaluation Metrics (Within ±1 Level): - CEFR Classifier Accuracy: 0.683 - Precision (Macro): 0.642 - Recall (Macro): 0.683 - F1-Score (Macro): 0.635 - Other Metrics: - Perplexity: 9.486 - Diversity (Unique Sentences): 0.933 - Inference Time (ms): 5107.406 - Model Size (GB): 4.1 - Robustness (F1): 0.119 - Confusion Matrix (Exact Matches): - CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv) - Image: [confusion_matrix_exact.png](confusion_matrix_exact.png) - Confusion Matrix (Within ±1 Level): - CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv) - Image: [confusion_matrix_within1.png](confusion_matrix_within1.png) - Per-Class Confusion Metrics (Exact Matches): - A1: TP=0, FP=0, FN=10, TN=50 - A2: TP=0, FP=0, FN=10, TN=50 - B1: TP=7, FP=25, FN=3, TN=25 - B2: TP=1, FP=13, FN=9, TN=37 - C1: TP=4, FP=10, FN=6, TN=40 - C2: TP=0, FP=0, FN=10, TN=50 - Per-Class Confusion Metrics (Within ±1 Level): - A1: TP=0, FP=0, FN=10, TN=50 - A2: TP=6, FP=0, FN=4, TN=50 - B1: TP=8, FP=10, FN=2, TN=40 - B2: TP=10, FP=7, FN=0, TN=43 - C1: TP=9, FP=2, FN=1, TN=48 - C2: TP=8, FP=0, FN=2, TN=50 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
loris3/stratified_equitoken_10m_curriculum_roberta_roberta_incr_influence_epoch_repetition	loris3	2025-05-04T05:48:07Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-03T20:59:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jdchang/full-with-label-bs-1024-sg-2-step-7290	jdchang	2025-05-04T05:47:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:47:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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echogarden/echogarden-packages	echogarden	2025-05-04T05:46:34Z	0	1	null	[ "region:us" ]	null	2023-07-18T16:12:02Z	This is the package repository for [Echogarden](https://github.com/echogarden-project/echogarden) - a fully open-source, user-oriented speech system, written in TypeScript and running over the Node.js runtime. The repository contains speech synthesis and recognition models for the various engines Echogarden supports, along with other related speech models, such as language classification and voice activity detection, as well as binaries for a few tools it uses internally. The models are individually packaged in `.tar.gz` archives to ensure a smooth and robust installation process. ## Licensing All content is freely distributable, with varying licenses: * Flite voices (`flite-`): [BSD License](https://github.com/festvox/flite/blob/master/COPYING) * SVOX Pico resources (`pico-`): [Apache License 2.0](https://github.com/gmn/nanotts/blob/master/LICENSE) * Silero VAD (`silero-vad`) and Silero language classifier (`silero-lang-classifier-95`): [MIT License](https://github.com/snakers4/silero-vad/blob/master/LICENSE) * Silero speech recognition models (`silero-en-`, `silero-de-`, `silero-es-`, `silero-ua-`): [BY-NC-SA](https://github.com/snakers4/silero-models/blob/master/LICENSE) * VITS pre-trained models, exported to the ONNX runtime (`vits-`): licensed under various creative commons licenses: [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/), [CC-BY](https://creativecommons.org/licenses/by/4.0/) and [BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/), and few are public domain (you can view the individual license for each model in the model cards on the [samples page](https://rhasspy.github.io/piper-samples/)). The [Piper system](https://github.com/rhasspy/piper) itself is published under the [MIT License](https://github.com/rhasspy/piper/blob/master/LICENSE.md) * Whisper pre-trained models, exported to the ONNX runtime (`whisper-`): [MIT License](https://github.com/openai/whisper/blob/main/LICENSE) Tool binary distributions: * FFMpeg: [LGPL, GPL v2 and GPL v3 Licenses](https://github.com/FFmpeg/FFmpeg) * SoX: [GPL v2 License](https://github.com/chirlu/sox/blob/master/LICENSE.GPL)
dgambettaphd/M_llm2_gen5_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-05-04T05:45:16Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:45:01Z	--- 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. 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]
DevQuasar/kyutai.helium-1-2b-life-GGUF	DevQuasar	2025-05-04T05:41:50Z	0	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-life", "base_model:quantized:kyutai/helium-1-2b-life", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:28:08Z	--- base_model: - kyutai/helium-1-2b-life pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-life](https://huggingface.co/kyutai/helium-1-2b-life) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
Shriharsh/qwen3-0.6b-creative-writing	Shriharsh	2025-05-04T05:39:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-03T11:57:10Z	--- library_name: transformers tags: [] --- # Model Card for Model Shriharsh/qwen3-0.6b-creative-writing <!-- Provide a quick summary of what the model is/does. --> This model is a fine-tuned version of Qwen3-0.6B, designed for creative writing tasks such as generating stories and dialogues. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), optimized for creative writing tasks such as storytelling and dialogue generation. It was trained on the [Gryphe/ChatGPT-4o-Writing-Prompts](https://huggingface.co/datasets/Gryphe/ChatGPT-4o-Writing-Prompts) dataset to enhance its narrative capabilities. - Developed by: Shriharsh - Funded by: Self-funded - Shared by: Shriharsh - Model type: Causal Language Model - Language(s) (NLP): English - License: Apache 2.0 - Finetuned from model: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) ### Model Sources - Repository: [Shriharsh/qwen3-0.6b-creative-writing](https://huggingface.co/Shriharsh/qwen3-0.6b-creative-writing) - Paper: Not available - Demo: Not available ## 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. --> The model can be used directly to generate creative writing content, such as stories, dialogues, and narrative responses, using the `transformers` library. ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> It can be further fine-tuned for specific creative writing tasks (e.g., scriptwriting, novel generation) or integrated into applications like chatbots or writing assistants. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> Not suitable for tasks requiring factual accuracy (e.g., scientific research) or non-English language generation without additional fine-tuning. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> The model may produce biased or stereotypical narratives due to biases in the training dataset. With a test loss of 3.0295, it might generate incoherent or repetitive text, indicating limitations in generalization. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should evaluate outputs for coherence and appropriateness, especially in sensitive contexts. Further fine-tuning or post-processing is recommended to mitigate biases. ## How to Get Started with the Model Use the code below to get started with the model. <pre> from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Shriharsh/qwen3-0.6b-creative-writing" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") prompt = "Write a short story about a lost time traveler who has a big pile of cash from the future which is unusable in the current time where he is now." messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(text, return_tensors="pt").to(model.device) # Move inputs to the same device as the model outputs = model.generate(inputs, max_new_tokens=2000, temperature=0.7, top_p=0.9) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) </pre> ## 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. --> The model was trained on the [Gryphe/ChatGPT-4o-Writing-Prompts](https://huggingface.co/datasets/Gryphe/ChatGPT-4o-Writing-Prompts) dataset, containing 700 training examples, 150 validation examples, and 150 test examples of prompt-response pairs focused on creative writing. ### 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] Conversations were formatted into prompt-response pairs as `### Human: {prompt} \n\n ### Assistant: {response} \n\n` and tokenized with a maximum length of 512 tokens. #### Training Hyperparameters - Training regime: fp16 mixed precision - Epochs: 3 - Learning rate: 2e-4 - Batch size: Effective batch size of 4 (per_device_train_batch_size=1, gradient_accumulation_steps=4) - LoRA parameters: r=8, lora_alpha=32, target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"] - #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> - Training time: ~23 minutes for 525 steps (3 epochs) on a T4 GPU in Google Colab - Model size: 383M parameters ## 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. --> Evaluated on 150 test examples from the [Gryphe/ChatGPT-4o-Writing-Prompts](https://huggingface.co/datasets/Gryphe/ChatGPT-4o-Writing-Prompts) dataset. #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> Assessed for narrative coherence and creativity, though formal metrics beyond loss were not applied. #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> - Loss: Cross-entropy loss ### Results - Test Loss: 3.0295 (post-training) - Validation Loss: 3.0295 at step 525 - Observations: Slight overfitting observed as validation loss increased from 3.0217 (step 350) to 3.0569 (step 500). #### Summary The model performs adequately for creative writing but shows signs of overfitting and limited generalization. ## 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 estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). - Hardware Type: T4 GPU - Hours used: ~0.4 hours (23 minutes) - Cloud Provider: Google Colab - Compute Region: Unknown - Carbon Emitted: ~0.02 kg CO2e (estimated based on T4 GPU usage and Colab’s energy mix) ## Technical Specifications [optional] ### Model Architecture and Objective Causal language model with LoRA adapters for efficient fine-tuning, aimed at minimizing cross-entropy loss on creative writing tasks. ### Compute Infrastructure #### Hardware T4 GPU with ~15GB VRAM, provided by Google Colab. Thanks Google! 🤗 #### Software - Transformers: Latest version - PEFT: For LoRA fine-tuning - BitsAndBytes:** For 4-bit quantization ## Model Card Authors [optional] - Shriharsh ## Model Card Contact - Shriharsh (Hugging Face username: Shriharsh)
Chang-Hoo/gemma-text-to-sql	Chang-Hoo	2025-05-04T05:38:28Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-12b-pt", "base_model:finetune:google/gemma-3-12b-pt", "endpoints_compatible", "region:us" ]	null	2025-04-22T08:53:22Z	--- base_model: google/gemma-3-12b-pt library_name: transformers model_name: gemma-text-to-sql tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-text-to-sql This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-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="Chang-Hoo/gemma-text-to-sql", 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: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sharkMeow/train_half_V2	sharkMeow	2025-05-04T05:29:08Z	0	0	transformers	[ "transformers", "safetensors", "chinese_clip", "generated_from_trainer", "base_model:OFA-Sys/chinese-clip-vit-base-patch16", "base_model:finetune:OFA-Sys/chinese-clip-vit-base-patch16", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:46:28Z	--- library_name: transformers base_model: OFA-Sys/chinese-clip-vit-base-patch16 tags: - generated_from_trainer model-index: - name: train_half_V2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_half_V2 This model is a fine-tuned version of [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 50 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 200 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.50.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF	mradermacher	2025-05-04T05:25:25Z	179	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0", "base_model:quantized:Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-23T01:36:07Z	--- base_model: Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q5_K_M.gguf) \| Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
jmalejandrob79/nrmexpwht	jmalejandrob79	2025-05-04T05:23:43Z	80	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	2025-04-28T15:10:28Z	--- 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: nrmexpwht --- # Nrmexpwht <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 `nrmexpwht` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nrmexpwht", "lora_weights": "https://huggingface.co/jmalejandrob79/nrmexpwht/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jmalejandrob79/nrmexpwht', weight_name='lora.safetensors') image = pipeline('nrmexpwht').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: 4100 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/nrmexpwht/discussions) to add images that show off what you’ve made with this LoRA.
Romoamigo/TEST_QWEN_3_16bit	Romoamigo	2025-05-04T05:18:12Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:10:18Z	--- base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Romoamigo - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit This qwen2 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)
John6666/kaillustriousmixl-v1-v10-sdxl	John6666	2025-05-04T05:17:17Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "style", "Illustrious XL v1.0", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v1.0", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-04T05:11:15Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - style - Illustrious XL v1.0 - illustrious base_model: OnomaAIResearch/Illustrious-XL-v1.0 --- Original model is [here](https://civitai.com/models/1538015/kaillustriousmixlv1?modelVersionId=1740237). This model created by [KayneGiordano](https://civitai.com/user/KayneGiordano).
flyingbugs/Qwen2.5-instruct-7B-openr1-math-edge	flyingbugs	2025-05-04T05:13:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5", "base_model:flyingbugs/Qwen2.5-7B-Instruct", "base_model:finetune:flyingbugs/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T01:09:47Z	--- base_model: flyingbugs/Qwen2.5-7B-Instruct datasets: flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5 library_name: transformers model_name: Qwen2.5-instruct-7B-openr1-math-edge tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-instruct-7B-openr1-math-edge This model is a fine-tuned version of [flyingbugs/Qwen2.5-7B-Instruct](https://huggingface.co/flyingbugs/Qwen2.5-7B-Instruct) on the [flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5](https://huggingface.co/datasets/flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5) 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="flyingbugs/Qwen2.5-instruct-7B-openr1-math-edge", 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/jjh233/huggingface/runs/zlpmm5j7) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
John6666/illustrious-anime-pop-cute-v10-sdxl	John6666	2025-05-04T05:11:14Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "cute", "aesthetic", "blending the vibrancy of pop art with the charm of anime", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-04T05:05:02Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - cute - aesthetic - blending the vibrancy of pop art with the charm of anime - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1540768/illustrious-anime-pop-cute?modelVersionId=1743352). This model created by [Rin_x0](https://civitai.com/user/Rin_x0).
Moyer01/Berg	Moyer01	2025-05-04T05:10:56Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T05:10:56Z	--- license: artistic-2.0 ---
Romoamigo/TEST_QWEN_3-adapters	Romoamigo	2025-05-04T05:09:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:09:03Z	--- base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Romoamigo - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit This qwen2 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)
phililp-arnold/e8a6d636-34fa-4190-bbc8-e20f95de5efe	phililp-arnold	2025-05-04T05:02:42Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "region:us" ]	null	2025-05-04T05:02:22Z	--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen1.5-7B model-index: - name: phililp-arnold/e8a6d636-34fa-4190-bbc8-e20f95de5efe 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. --> # phililp-arnold/e8a6d636-34fa-4190-bbc8-e20f95de5efe This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0249 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
alissacielecki/attention-efficientnet-b0-gps	alissacielecki	2025-05-04T05:02:39Z	0	0	transformers	[ "transformers", "safetensors", "efficientnet", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-04T05:02:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
jmjm123/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper	jmjm123	2025-05-04T04:59:02Z	13	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am clawed rugged viper", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-12T14:30:58Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am clawed rugged viper - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jmjm123/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` 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}} } ```
DevQuasar/Qwen.Qwen3-8B-GGUF	DevQuasar	2025-05-04T04:41:27Z	163	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T09:24:07Z	--- base_model: - Qwen/Qwen3-8B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
DevQuasar/Qwen.Qwen3-235B-A22B-GGUF	DevQuasar	2025-05-04T04:39:53Z	372	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-235B-A22B", "base_model:quantized:Qwen/Qwen3-235B-A22B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T23:23:32Z	--- base_model: - Qwen/Qwen3-235B-A22B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' Quantized version of: [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
boudy08/G0xxp	boudy08	2025-05-04T04:35:11Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:mit", "region:us" ]	text-to-image	2025-05-04T04:35:08Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Go8IM9vagAEGQYl.jpeg - text: '-' output: url: images/GnAChJna4AADK4a.jpeg - text: '-' output: url: images/Go8BQVKbsAAtVXf.jpeg base_model: black-forest-labs/FLUX.1-dev instance_prompt: G0xxip license: mit --- # G0xxip <Gallery /> ## Model description G0xxip G1rl Lora ## Trigger words You should use `G0xxip` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/boudy08/G0xxp/tree/main) them in the Files & versions tab.
ASethi04/meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001	ASethi04	2025-05-04T04:18:59Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:24:11Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001", 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/torchql-org/huggingface/runs/23mwqb4o) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004	ASethi04	2025-05-04T04:16:18Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T00:27:34Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004", 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/torchql-org/huggingface/runs/zffbfdvk) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
espor/ana	espor	2025-05-04T04:13:09Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-04T03:14:25Z	--- 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 ---
Skyghuu/Sky	Skyghuu	2025-05-04T04:10:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T04:10:39Z	--- license: apache-2.0 ---
hungtran0509/ppo-Pyramids	hungtran0509	2025-05-04T04:00:05Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2025-05-04T03:59:08Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: hungtran0509/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
minions-stanford/minion-llama-3.1-8B-instruct	minions-stanford	2025-05-04T03:58:45Z	0	0	null	[ "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "region:us" ]	text-generation	2025-05-04T03:54:43Z	--- language: - en - de - fr - it - pt - hi - es - th license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\ \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\ \ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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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 3.1 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.\n6. 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.\n7. 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.\n### Llama 3.1 Acceptable Use Policy\n\ Meta is committed to promoting safe and fair use of its tools and features, including\ \ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\ \ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\ #### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\ \ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 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\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 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.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 4. 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\n 5.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 6. 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\n 7. 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 Materials\n 8. 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\n2. 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 3.1 related\ \ to the following:\n 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\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\ \ 6. Generating or facilitating false online engagement, including fake reviews\ \ and other means of fake online engagement\n4. Fail to appropriately disclose to\ \ end users any known dangers of your AI system\nPlease 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:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other 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 --- ## Model Information The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Input modalities</strong> </td> <td><strong>Output modalities</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="3" >Llama 3.1 (text only) </td> <td rowspan="3" >A new mix of publicly available online data. </td> <td>8B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> <td rowspan="3" >15T+ </td> <td rowspan="3" >December 2023 </td> </tr> <tr> <td>70B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> <tr> <td>405B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> </table> Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Llama 3.1 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: July 23, 2024. Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License: A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card. <span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner. ## How to use This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipeline( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Tool use with transformers LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/). Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool: ```python # First, define a tool def get_current_temperature(location: str) -> float: """ Get the current temperature at a location. Args: location: The location to get the temperature for, in the format "City, Country" Returns: The current temperature at the specified location in the specified units, as a float. """ return 22. # A real function should probably actually get the temperature! # Next, create a chat and apply the chat template messages = [ {"role": "system", "content": "You are a bot that responds to weather queries."}, {"role": "user", "content": "Hey, what's the temperature in Paris right now?"} ] inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True) ``` You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so: ```python tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}} messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]}) ``` and then call the tool and append the result, with the `tool` role, like so: ```python messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"}) ``` After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling). ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/" --local-dir Meta-Llama-3.1-8B-Instruct ``` ## Hardware and Software Training Factors* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. Training utilized a cumulative of 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions Estimated total location-based greenhouse gas emissions were 11,390 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq. <table> <tr> <td> </td> <td><strong>Training Time (GPU hours)</strong> </td> <td><strong>Training Power Consumption (W)</strong> </td> <td><strong>Training Location-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> <td><strong>Training Market-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> </tr> <tr> <td>Llama 3.1 8B </td> <td>1.46M </td> <td>700 </td> <td>420 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 70B </td> <td>7.0M </td> <td>700 </td> <td>2,040 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 405B </td> <td>30.84M </td> <td>700 </td> <td>8,930 </td> <td>0 </td> </tr> <tr> <td>Total </td> <td>39.3M <td> <ul> </ul> </td> <td>11,390 </td> <td>0 </td> </tr> </table> The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. Data Freshness: The pretraining data has a cutoff of December 2023. ## Benchmark scores In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="7" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>66.7 </td> <td>66.7 </td> <td>79.5 </td> <td>79.3 </td> <td>85.2 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>36.2 </td> <td>37.1 </td> <td>55.0 </td> <td>53.8 </td> <td>61.6 </td> </tr> <tr> <td>AGIEval English </td> <td>3-5 </td> <td>average/acc_char </td> <td>47.1 </td> <td>47.8 </td> <td>63.0 </td> <td>64.6 </td> <td>71.6 </td> </tr> <tr> <td>CommonSenseQA </td> <td>7 </td> <td>acc_char </td> <td>72.6 </td> <td>75.0 </td> <td>83.8 </td> <td>84.1 </td> <td>85.8 </td> </tr> <tr> <td>Winogrande </td> <td>5 </td> <td>acc_char </td> <td>- </td> <td>60.5 </td> <td>- </td> <td>83.3 </td> <td>86.7 </td> </tr> <tr> <td>BIG-Bench Hard (CoT) </td> <td>3 </td> <td>average/em </td> <td>61.1 </td> <td>64.2 </td> <td>81.3 </td> <td>81.6 </td> <td>85.9 </td> </tr> <tr> <td>ARC-Challenge </td> <td>25 </td> <td>acc_char </td> <td>79.4 </td> <td>79.7 </td> <td>93.1 </td> <td>92.9 </td> <td>96.1 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki </td> <td>5 </td> <td>em </td> <td>78.5 </td> <td>77.6 </td> <td>89.7 </td> <td>89.8 </td> <td>91.8 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD </td> <td>1 </td> <td>em </td> <td>76.4 </td> <td>77.0 </td> <td>85.6 </td> <td>81.8 </td> <td>89.3 </td> </tr> <tr> <td>QuAC (F1) </td> <td>1 </td> <td>f1 </td> <td>44.4 </td> <td>44.9 </td> <td>51.1 </td> <td>51.1 </td> <td>53.6 </td> </tr> <tr> <td>BoolQ </td> <td>0 </td> <td>acc_char </td> <td>75.7 </td> <td>75.0 </td> <td>79.0 </td> <td>79.4 </td> <td>80.0 </td> </tr> <tr> <td>DROP (F1) </td> <td>3 </td> <td>f1 </td> <td>58.4 </td> <td>59.5 </td> <td>79.7 </td> <td>79.6 </td> <td>84.8 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B Instruct</strong> </td> <td><strong>Llama 3.1 8B Instruct</strong> </td> <td><strong>Llama 3 70B Instruct</strong> </td> <td><strong>Llama 3.1 70B Instruct</strong> </td> <td><strong>Llama 3.1 405B Instruct</strong> </td> </tr> <tr> <td rowspan="4" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc </td> <td>68.5 </td> <td>69.4 </td> <td>82.0 </td> <td>83.6 </td> <td>87.3 </td> </tr> <tr> <td>MMLU (CoT) </td> <td>0 </td> <td>macro_avg/acc </td> <td>65.3 </td> <td>73.0 </td> <td>80.9 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>micro_avg/acc_char </td> <td>45.5 </td> <td>48.3 </td> <td>63.4 </td> <td>66.4 </td> <td>73.3 </td> </tr> <tr> <td>IFEval </td> <td> </td> <td> </td> <td>76.8 </td> <td>80.4 </td> <td>82.9 </td> <td>87.5 </td> <td>88.6 </td> </tr> <tr> <td rowspan="2" >Reasoning </td> <td>ARC-C </td> <td>0 </td> <td>acc </td> <td>82.4 </td> <td>83.4 </td> <td>94.4 </td> <td>94.8 </td> <td>96.9 </td> </tr> <tr> <td>GPQA </td> <td>0 </td> <td>em </td> <td>34.6 </td> <td>30.4 </td> <td>39.5 </td> <td>46.7 </td> <td>50.7 </td> </tr> <tr> <td rowspan="4" >Code </td> <td>HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>60.4 </td> <td>72.6 </td> <td>81.7 </td> <td>80.5 </td> <td>89.0 </td> </tr> <tr> <td>MBPP ++ base version </td> <td>0 </td> <td>pass@1 </td> <td>70.6 </td> <td>72.8 </td> <td>82.5 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>Multipl-E HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>50.8 </td> <td>- </td> <td>65.5 </td> <td>75.2 </td> </tr> <tr> <td>Multipl-E MBPP </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>52.4 </td> <td>- </td> <td>62.0 </td> <td>65.7 </td> </tr> <tr> <td rowspan="2" >Math </td> <td>GSM-8K (CoT) </td> <td>8 </td> <td>em_maj1@1 </td> <td>80.6 </td> <td>84.5 </td> <td>93.0 </td> <td>95.1 </td> <td>96.8 </td> </tr> <tr> <td>MATH (CoT) </td> <td>0 </td> <td>final_em </td> <td>29.1 </td> <td>51.9 </td> <td>51.0 </td> <td>68.0 </td> <td>73.8 </td> </tr> <tr> <td rowspan="4" >Tool Use </td> <td>API-Bank </td> <td>0 </td> <td>acc </td> <td>48.3 </td> <td>82.6 </td> <td>85.1 </td> <td>90.0 </td> <td>92.0 </td> </tr> <tr> <td>BFCL </td> <td>0 </td> <td>acc </td> <td>60.3 </td> <td>76.1 </td> <td>83.0 </td> <td>84.8 </td> <td>88.5 </td> </tr> <tr> <td>Gorilla Benchmark API Bench </td> <td>0 </td> <td>acc </td> <td>1.7 </td> <td>8.2 </td> <td>14.7 </td> <td>29.7 </td> <td>35.3 </td> </tr> <tr> <td>Nexus (0-shot) </td> <td>0 </td> <td>macro_avg/acc </td> <td>18.1 </td> <td>38.5 </td> <td>47.8 </td> <td>56.7 </td> <td>58.7 </td> </tr> <tr> <td>Multilingual </td> <td>Multilingual MGSM (CoT) </td> <td>0 </td> <td>em </td> <td>- </td> <td>68.9 </td> <td>- </td> <td>86.9 </td> <td>91.6 </td> </tr> </table> #### Multilingual benchmarks <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Language</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="9" ><strong>General</strong> </td> <td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong> </td> <td>Portuguese </td> <td>62.12 </td> <td>80.13 </td> <td>84.95 </td> </tr> <tr> <td>Spanish </td> <td>62.45 </td> <td>80.05 </td> <td>85.08 </td> </tr> <tr> <td>Italian </td> <td>61.63 </td> <td>80.4 </td> <td>85.04 </td> </tr> <tr> <td>German </td> <td>60.59 </td> <td>79.27 </td> <td>84.36 </td> </tr> <tr> <td>French </td> <td>62.34 </td> <td>79.82 </td> <td>84.66 </td> </tr> <tr> <td>Hindi </td> <td>50.88 </td> <td>74.52 </td> <td>80.31 </td> </tr> <tr> <td>Thai </td> <td>50.32 </td> <td>72.95 </td> <td>78.21 </td> </tr> </table> ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. ### Responsible deployment Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. #### Llama 3.1 instruct Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. Fine-tuning data We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.1 systems Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. #### New capabilities Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases. Tool-use: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. Multilinguality: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization. Red teaming For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical and other risks We specifically focused our efforts on mitigating the following critical risk areas: 1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. 2. Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3. Cyber attack enablement Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
ivangrapher/4ec07c9c-883f-4bee-a8bd-679323310cc8	ivangrapher	2025-05-04T03:55:46Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T02:08:13Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 4ec07c9c-883f-4bee-a8bd-679323310cc8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1f34408d713e6b26_train_data.json ds_type: json format: custom path: /workspace/input_data/1f34408d713e6b26_train_data.json type: field_instruction: en field_output: ja format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: ivangrapher/4ec07c9c-883f-4bee-a8bd-679323310cc8 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/1f34408d713e6b26_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dac36c01-453c-4358-9c23-a55d2a4926d7 wandb_project: s56-7 wandb_run: your_name wandb_runid: dac36c01-453c-4358-9c23-a55d2a4926d7 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4ec07c9c-883f-4bee-a8bd-679323310cc8 This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2381 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.2149 \| 0.0013 \| 150 \| 5.2381 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Mizewy/flux1_schnell_finetuned_512	Mizewy	2025-05-04T03:51:39Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-schnell", "base_model:adapter:black-forest-labs/FLUX.1-schnell", "license:other", "region:us" ]	text-to-image	2025-05-04T03:22:28Z	--- base_model: black-forest-labs/FLUX.1-schnell library_name: diffusers license: other instance_prompt: isometric pixel art sks dirtblock widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux DreamBooth LoRA - Mizewy/flux1_schnell_finetuned_512 <Gallery /> ## Model description These are Mizewy/flux1_schnell_finetuned_512 DreamBooth LoRA weights for black-forest-labs/FLUX.1-schnell. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `isometric pixel art sks dirtblock` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](Mizewy/flux1_schnell_finetuned_512/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('Mizewy/flux1_schnell_finetuned_512', weight_name='pytorch_lora_weights.safetensors') image = pipeline('isometric pixel art sks dirtblock').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) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
phililp-arnold/f6edfa5d-2dcd-4259-8bff-8ca02fa9721a	phililp-arnold	2025-05-04T03:50:58Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:adapter:NousResearch/Hermes-2-Pro-Mistral-7B", "region:us" ]	null	2025-05-04T03:50:35Z	--- library_name: peft tags: - generated_from_trainer base_model: NousResearch/Hermes-2-Pro-Mistral-7B model-index: - name: phililp-arnold/f6edfa5d-2dcd-4259-8bff-8ca02fa9721a 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. --> # phililp-arnold/f6edfa5d-2dcd-4259-8bff-8ca02fa9721a This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7014 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
giro96/baru	giro96	2025-05-04T03:45:44Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T03:45:44Z	--- license: bigscience-openrail-m ---
mradermacher/Noct-8B-GGUF	mradermacher	2025-05-04T03:40:33Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ClaudioItaly/Noct-8B", "base_model:quantized:ClaudioItaly/Noct-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:31:15Z	--- base_model: ClaudioItaly/Noct-8B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ClaudioItaly/Noct-8B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Noct-8B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Noct-8B-GGUF/resolve/main/Noct-8B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/MedicalChatBot-Qwen3-4b-GGUF	mradermacher	2025-05-04T03:40:33Z	0	0	transformers	[ "transformers", "gguf", "medical", "zh", "en", "dataset:FreedomIntelligence/Huatuo26M-Lite", "base_model:Tommi09/MedicalChatBot-Qwen3-4b", "base_model:quantized:Tommi09/MedicalChatBot-Qwen3-4b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:11:37Z	--- base_model: Tommi09/MedicalChatBot-Qwen3-4b datasets: - FreedomIntelligence/Huatuo26M-Lite language: - zh - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - medical --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Tommi09/MedicalChatBot-Qwen3-4b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q5_K_M.gguf) \| Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.Q8_0.gguf) \| Q8_0 \| 4.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.f16.gguf) \| f16 \| 8.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
PQPQPQHUST/CACTUS-Qwen3-0.6B-300	PQPQPQHUST	2025-05-04T03:34:43Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:34:30Z	--- base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: PQPQPQHUST - License: apache-2.0 - Finetuned from model : unsloth/qwen3-0.6b-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)
si0005hp/codeparrot-ds	si0005hp	2025-05-04T03:31:30Z	0	0	null	[ "safetensors", "gpt2", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "region:us" ]	null	2025-05-04T03:17:40Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds 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. --> # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.4.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
AnonymousCS/llama-3.1-8B-populism	AnonymousCS	2025-05-04T03:29:19Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T19:01:24Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: llama-3.1-8B-populism tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama-3.1-8B-populism This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-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="AnonymousCS/llama-3.1-8B-populism", 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/cecilia-y-sui-washington-unviersity-st-louis/huggingface/runs/0awplmtt) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
memeviss/GPA_2	memeviss	2025-05-04T03:28:17Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-04T03:25:42Z	# Optimized TTS Model This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques. ## Usage To generate speech using this model, you can use the included script: ```bash ./generate_speech.py --text "Your text here" --output_path output.wav ``` For more details, see the optimization report in this directory.
rdz-falcon/gemma-3-finetune	rdz-falcon	2025-05-04T03:25:14Z	1	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-13T21:59:40Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: rdz-falcon - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
ASethi04/meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001	ASethi04	2025-05-04T03:23:53Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:00:36Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001", 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/torchql-org/huggingface/runs/4kbai0vk) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tfuhkhj/sddryt	tfuhkhj	2025-05-04T03:20:58Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-04T03:20:57Z	--- license: creativeml-openrail-m ---
huitui/hghesr	huitui	2025-05-04T03:14:36Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-04T03:14:36Z	--- license: creativeml-openrail-m ---
dzanbek/d1199309-8a4b-4a66-9a40-8695a061ed76	dzanbek	2025-05-04T03:07:49Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T02:08:20Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: d1199309-8a4b-4a66-9a40-8695a061ed76 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1f34408d713e6b26_train_data.json ds_type: json format: custom path: /workspace/input_data/1f34408d713e6b26_train_data.json type: field_instruction: en field_output: ja format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/d1199309-8a4b-4a66-9a40-8695a061ed76 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/1f34408d713e6b26_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dac36c01-453c-4358-9c23-a55d2a4926d7 wandb_project: s56-2 wandb_run: your_name wandb_runid: dac36c01-453c-4358-9c23-a55d2a4926d7 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d1199309-8a4b-4a66-9a40-8695a061ed76 This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.4358 \| 0.0017 \| 200 \| 4.0861 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
hjkhjki/ghfhyg	hjkhjki	2025-05-04T03:03:23Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-04T03:03:23Z	--- license: bigscience-bloom-rail-1.0 ---
aciang/Qwen3-vLLM	aciang	2025-05-04T03:01:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-04T02:58:34Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: aciang - 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)
MaximilianoVr/maximilian	MaximilianoVr	2025-05-04T02:56:32Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-04T02:20:40Z	--- 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: maximilian --- # Maximilian <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 `maximilian` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "maximilian", "lora_weights": "https://huggingface.co/MaximilianoVr/maximilian/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('MaximilianoVr/maximilian', weight_name='lora.safetensors') image = pipeline('maximilian').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: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/MaximilianoVr/maximilian/discussions) to add images that show off what you’ve made with this LoRA.
TOMFORD79/Fly58	TOMFORD79	2025-05-04T02:49:58Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-04T02:37:52Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
MrDevolver/mergekit-dare_ties-fikucxa-Q4_K_S-GGUF	MrDevolver	2025-05-04T02:44:46Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:mergekit-community/mergekit-dare_ties-fikucxa", "base_model:quantized:mergekit-community/mergekit-dare_ties-fikucxa", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T02:43:45Z	--- base_model: mergekit-community/mergekit-dare_ties-fikucxa library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrDevolver/mergekit-dare_ties-fikucxa-Q4_K_S-GGUF This model was converted to GGUF format from [`mergekit-community/mergekit-dare_ties-fikucxa`](https://huggingface.co/mergekit-community/mergekit-dare_ties-fikucxa) 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/mergekit-community/mergekit-dare_ties-fikucxa) 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 MrDevolver/mergekit-dare_ties-fikucxa-Q4_K_S-GGUF --hf-file mergekit-dare_ties-fikucxa-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrDevolver/mergekit-dare_ties-fikucxa-Q4_K_S-GGUF --hf-file mergekit-dare_ties-fikucxa-q4_k_s.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 MrDevolver/mergekit-dare_ties-fikucxa-Q4_K_S-GGUF --hf-file mergekit-dare_ties-fikucxa-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrDevolver/mergekit-dare_ties-fikucxa-Q4_K_S-GGUF --hf-file mergekit-dare_ties-fikucxa-q4_k_s.gguf -c 2048 ```
dylanewbie/whisper-large-v2-ft-tms-0418-good-30_base-on-car350-250504-v1	dylanewbie	2025-05-04T02:44:30Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large-v2", "base_model:adapter:openai/whisper-large-v2", "license:apache-2.0", "region:us" ]	null	2025-05-04T02:44:25Z	--- base_model: openai/whisper-large-v2 library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: whisper-large-v2-ft-tms-0418-good-30_base-on-car350-250504-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v2-ft-tms-0418-good-30_base-on-car350-250504-v1 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.8673 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 12.8848 \| 1.0 \| 1 \| 13.3228 \| \| 12.8451 \| 2.0 \| 2 \| 13.3228 \| \| 12.9658 \| 3.0 \| 3 \| 13.3228 \| \| 12.8821 \| 4.0 \| 4 \| 13.3228 \| \| 12.7804 \| 5.0 \| 5 \| 13.3228 \| \| 12.857 \| 6.0 \| 6 \| 12.4657 \| \| 12.046 \| 7.0 \| 7 \| 10.7954 \| \| 10.1789 \| 8.0 \| 8 \| 10.7954 \| \| 10.1851 \| 9.0 \| 9 \| 9.1693 \| \| 8.4416 \| 10.0 \| 10 \| 7.8673 \| ### Framework versions - PEFT 0.13.0 - Transformers 4.45.1 - Pytorch 2.5.0+cu124 - Datasets 2.21.0 - Tokenizers 0.20.0
Flo0620/Qwen2_5_7B_r64_a64_d0_1	Flo0620	2025-05-04T02:44:11Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-22T10:18:59Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5_7B_r64_a64_d0_1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5_7B_r64_a64_d0_1 This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="Flo0620/Qwen2_5_7B_r64_a64_d0_1", 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: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dgambettaphd/M_llm2_gen3_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-05-04T02:43:04Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T02:42:50Z	--- 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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49Simoney/OpenBuddy-SpanishLanguageTeacher-7B-3k-merged	49Simoney	2025-05-04T02:40:05Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k", "base_model:finetune:OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:27:48Z	--- base_model: OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: 49Simoney - License: apache-2.0 - Finetuned from model : OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k 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)
Membersuger/Euro_32	Membersuger	2025-05-04T02:33:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:28:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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mlfoundations-dev/e1_math_ms_phi	mlfoundations-dev	2025-05-04T02:29:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T22:40:06Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_math_ms_phi 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. --> # e1_math_ms_phi This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_math_ms_phi 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
duongai248/ner-location-vietnam-model	duongai248	2025-05-04T01:52:39Z	0	0	transformers	[ "transformers", "safetensors", "electra", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-04T01:50:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
memevis/text0	memevis	2025-05-04T01:49:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:42:54Z	--- 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. 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abdoxc/ggggg	abdoxc	2025-05-04T01:42:43Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T01:42:42Z	--- license: artistic-2.0 ---
jdchang/full-with-label-bs-1024-sg-2-step-5346	jdchang	2025-05-04T01:33:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T01:33:48Z	--- 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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beyoru/Pipp1	beyoru	2025-05-04T01:20:41Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:16:57Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e10-lr0.0001-mixed-actors_reviews_markdown-new	lisabdunlap	2025-05-04T01:08:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:06:31Z	--- base_model: unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF	mradermacher	2025-05-04T01:06:48Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:IRUCAAI/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct", "base_model:quantized:IRUCAAI/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-02T19:33:57Z	--- base_model: IRUCAAI/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/IRUCAAI/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 22.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 23.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 29.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 29.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 31.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 34.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 34.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 37.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 39.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 39.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 44.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q4_1.gguf) \| i1-Q4_1 \| 45.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct-i1-GGUF/resolve/main/Opeai_QZV2_Identity_Qwen2.5-72B-Instruct.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 64.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e10-lr0.0001-mixed-actors_reviews_freeform-new	lisabdunlap	2025-05-04T01:03:18Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:01:04Z	--- base_model: unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
GAOZIYUE/DPO_finetuned_model	GAOZIYUE	2025-05-04T01:00:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-02T11:13:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Columbidae/Qwen3-20B-Zeroed-Noisy	Columbidae	2025-05-04T01:00:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mergekit", "merge", "conversational", "base_model:Qwen/Qwen3-14B", "base_model:merge:Qwen/Qwen3-14B", "base_model:Qwen/Qwen3-14B-Base", "base_model:merge:Qwen/Qwen3-14B-Base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T00:29:25Z	--- base_model: - Qwen/Qwen3-14B - Qwen/Qwen3-14B-Base library_name: transformers tags: - mergekit - merge --- # merged-20b This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the Passthrough merge method. ### Models Merged The following models were included in the merge: * [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) * [Qwen/Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Qwen/Qwen3-14B layer_range: [0,25] - sources: - model: Qwen/Qwen3-14B layer_range: [25,26] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [25,26] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [25,26] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [26,27] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [26,27] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [26,27] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [27,28] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [27,28] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [27,28] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [28,29] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [28,29] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [28,29] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [29,30] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [29,30] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [29,30] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [30,31] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [30,31] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [30,31] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [31,32] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [31,32] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [31,32] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [32,33] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [32,33] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [32,33] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [33,34] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [33,34] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [33,34] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [34,35] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [34,35] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [34,35] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [35,40] merge_method: passthrough ```

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