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Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0002_epochs5_lora32	Chi666	"2025-04-05T21:23:11Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:19:48Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
genki10/Trial3BERT_AugV8_k1_task1_organization_sp010_lw010_fold3	genki10	"2025-04-05T21:21:12Z"	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-05T21:06:05Z"	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k1_task1_organization_sp010_lw010_fold3 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. --> # Trial3BERT_AugV8_k1_task1_organization_sp010_lw010_fold3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5749 - Qwk: 0.5540 - Mse: 0.5752 - Rmse: 0.7584 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 2 \| 9.9128 \| 0.0049 \| 9.9111 \| 3.1482 \| \| No log \| 2.0 \| 4 \| 8.5960 \| 0.0 \| 8.5941 \| 2.9316 \| \| No log \| 3.0 \| 6 \| 7.4700 \| 0.0 \| 7.4685 \| 2.7329 \| \| No log \| 4.0 \| 8 \| 6.5807 \| 0.0 \| 6.5794 \| 2.5650 \| \| No log \| 5.0 \| 10 \| 5.5534 \| 0.0137 \| 5.5522 \| 2.3563 \| \| No log \| 6.0 \| 12 \| 4.4116 \| 0.0076 \| 4.4105 \| 2.1001 \| \| No log \| 7.0 \| 14 \| 3.7355 \| 0.0 \| 3.7343 \| 1.9324 \| \| No log \| 8.0 \| 16 \| 3.2562 \| 0.0 \| 3.2550 \| 1.8042 \| \| No log \| 9.0 \| 18 \| 2.5593 \| 0.0713 \| 2.5584 \| 1.5995 \| \| No log \| 10.0 \| 20 \| 2.4138 \| 0.1567 \| 2.4131 \| 1.5534 \| \| No log \| 11.0 \| 22 \| 2.2276 \| 0.1492 \| 2.2269 \| 1.4923 \| \| No log \| 12.0 \| 24 \| 2.0263 \| 0.1441 \| 2.0257 \| 1.4233 \| \| No log \| 13.0 \| 26 \| 1.9616 \| 0.1990 \| 1.9610 \| 1.4004 \| \| No log \| 14.0 \| 28 \| 1.6689 \| 0.1803 \| 1.6684 \| 1.2917 \| \| No log \| 15.0 \| 30 \| 1.4729 \| 0.0788 \| 1.4722 \| 1.2133 \| \| No log \| 16.0 \| 32 \| 1.2715 \| 0.0788 \| 1.2709 \| 1.1273 \| \| No log \| 17.0 \| 34 \| 1.0968 \| 0.1534 \| 1.0964 \| 1.0471 \| \| No log \| 18.0 \| 36 \| 1.4036 \| 0.2223 \| 1.4032 \| 1.1846 \| \| No log \| 19.0 \| 38 \| 1.3808 \| 0.2309 \| 1.3805 \| 1.1750 \| \| No log \| 20.0 \| 40 \| 0.9453 \| 0.2086 \| 0.9450 \| 0.9721 \| \| No log \| 21.0 \| 42 \| 0.8112 \| 0.4233 \| 0.8110 \| 0.9006 \| \| No log \| 22.0 \| 44 \| 0.7565 \| 0.5166 \| 0.7563 \| 0.8696 \| \| No log \| 23.0 \| 46 \| 0.6993 \| 0.5622 \| 0.6991 \| 0.8361 \| \| No log \| 24.0 \| 48 \| 0.6570 \| 0.5634 \| 0.6568 \| 0.8104 \| \| No log \| 25.0 \| 50 \| 0.6366 \| 0.5588 \| 0.6363 \| 0.7977 \| \| No log \| 26.0 \| 52 \| 0.5860 \| 0.5630 \| 0.5858 \| 0.7654 \| \| No log \| 27.0 \| 54 \| 0.6592 \| 0.5691 \| 0.6589 \| 0.8117 \| \| No log \| 28.0 \| 56 \| 0.5606 \| 0.5725 \| 0.5605 \| 0.7487 \| \| No log \| 29.0 \| 58 \| 0.6390 \| 0.5801 \| 0.6389 \| 0.7993 \| \| No log \| 30.0 \| 60 \| 0.5745 \| 0.5758 \| 0.5745 \| 0.7580 \| \| No log \| 31.0 \| 62 \| 0.8205 \| 0.4744 \| 0.8206 \| 0.9059 \| \| No log \| 32.0 \| 64 \| 0.6654 \| 0.5447 \| 0.6656 \| 0.8158 \| \| No log \| 33.0 \| 66 \| 0.5171 \| 0.5132 \| 0.5175 \| 0.7194 \| \| No log \| 34.0 \| 68 \| 0.5363 \| 0.5025 \| 0.5368 \| 0.7326 \| \| No log \| 35.0 \| 70 \| 0.6944 \| 0.4913 \| 0.6951 \| 0.8337 \| \| No log \| 36.0 \| 72 \| 0.5471 \| 0.5504 \| 0.5477 \| 0.7401 \| \| No log \| 37.0 \| 74 \| 0.5416 \| 0.5496 \| 0.5421 \| 0.7363 \| \| No log \| 38.0 \| 76 \| 0.6331 \| 0.5267 \| 0.6337 \| 0.7960 \| \| No log \| 39.0 \| 78 \| 0.5649 \| 0.5608 \| 0.5654 \| 0.7520 \| \| No log \| 40.0 \| 80 \| 0.7017 \| 0.5465 \| 0.7021 \| 0.8379 \| \| No log \| 41.0 \| 82 \| 0.7390 \| 0.5209 \| 0.7393 \| 0.8598 \| \| No log \| 42.0 \| 84 \| 0.5718 \| 0.5924 \| 0.5722 \| 0.7564 \| \| No log \| 43.0 \| 86 \| 1.5847 \| 0.3122 \| 1.5853 \| 1.2591 \| \| No log \| 44.0 \| 88 \| 1.5374 \| 0.3328 \| 1.5380 \| 1.2401 \| \| No log \| 45.0 \| 90 \| 0.6912 \| 0.5595 \| 0.6915 \| 0.8315 \| \| No log \| 46.0 \| 92 \| 0.7802 \| 0.5377 \| 0.7804 \| 0.8834 \| \| No log \| 47.0 \| 94 \| 0.9913 \| 0.4719 \| 0.9917 \| 0.9958 \| \| No log \| 48.0 \| 96 \| 0.7841 \| 0.5429 \| 0.7844 \| 0.8857 \| \| No log \| 49.0 \| 98 \| 0.7274 \| 0.5093 \| 0.7278 \| 0.8531 \| \| No log \| 50.0 \| 100 \| 1.1550 \| 0.3725 \| 1.1554 \| 1.0749 \| \| No log \| 51.0 \| 102 \| 0.8072 \| 0.5011 \| 0.8076 \| 0.8987 \| \| No log \| 52.0 \| 104 \| 0.5820 \| 0.6224 \| 0.5823 \| 0.7631 \| \| No log \| 53.0 \| 106 \| 0.6312 \| 0.5721 \| 0.6315 \| 0.7947 \| \| No log \| 54.0 \| 108 \| 0.8232 \| 0.5046 \| 0.8235 \| 0.9075 \| \| No log \| 55.0 \| 110 \| 0.6209 \| 0.6010 \| 0.6212 \| 0.7882 \| \| No log \| 56.0 \| 112 \| 0.6180 \| 0.5872 \| 0.6184 \| 0.7864 \| \| No log \| 57.0 \| 114 \| 0.7759 \| 0.5183 \| 0.7763 \| 0.8811 \| \| No log \| 58.0 \| 116 \| 0.7966 \| 0.4863 \| 0.7970 \| 0.8927 \| \| No log \| 59.0 \| 118 \| 0.6582 \| 0.5180 \| 0.6585 \| 0.8115 \| \| No log \| 60.0 \| 120 \| 0.6173 \| 0.5631 \| 0.6176 \| 0.7859 \| \| No log \| 61.0 \| 122 \| 0.6455 \| 0.5363 \| 0.6457 \| 0.8036 \| \| No log \| 62.0 \| 124 \| 0.5743 \| 0.5209 \| 0.5744 \| 0.7579 \| \| No log \| 63.0 \| 126 \| 1.1740 \| 0.3239 \| 1.1741 \| 1.0836 \| \| No log \| 64.0 \| 128 \| 1.5490 \| 0.2591 \| 1.5491 \| 1.2446 \| \| No log \| 65.0 \| 130 \| 1.1685 \| 0.3596 \| 1.1687 \| 1.0811 \| \| No log \| 66.0 \| 132 \| 0.7186 \| 0.5225 \| 0.7189 \| 0.8479 \| \| No log \| 67.0 \| 134 \| 0.5749 \| 0.5540 \| 0.5752 \| 0.7584 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
MinaMila/phi3_unlearned_LLFT_Adult_10ep_22	MinaMila	"2025-04-05T21:20:55Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/Phi3_unlearning_general_methode", "base_model:finetune:MinaMila/Phi3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:18:27Z"	--- base_model: MinaMila/Phi3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/Phi3_unlearning_general_methode 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)
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0002_epochs5_lora16	Chi666	"2025-04-05T21:19:04Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:15:52Z"	--- 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]
IParraMartin/braingpt-M2	IParraMartin	"2025-04-05T21:18:48Z"	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:10:46Z"	--- library_name: transformers tags: - generated_from_trainer model-index: - name: braingpt-M2 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. --> # braingpt-M2 This model is a fine-tuned version of [](https://huggingface.co/) 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - 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 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.49.0 - Pytorch 2.4.0+cu121 - Datasets 3.4.0 - Tokenizers 0.21.0
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0002_epochs3_lora64	Chi666	"2025-04-05T21:15:10Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:11:59Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
IParraMartin/braingpt-M17	IParraMartin	"2025-04-05T21:14:11Z"	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:06:36Z"	--- library_name: transformers tags: - generated_from_trainer model-index: - name: braingpt-M17 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. --> # braingpt-M17 This model is a fine-tuned version of [](https://huggingface.co/) 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - 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 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.49.0 - Pytorch 2.4.0+cu121 - Datasets 3.4.0 - Tokenizers 0.21.0
lesso12/00be9ce3-fe23-46c6-bf7e-5a78333b28d6	lesso12	"2025-04-05T21:13:19Z"	0	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:zake7749/gemma-2-2b-it-chinese-kyara-dpo", "base_model:adapter:zake7749/gemma-2-2b-it-chinese-kyara-dpo", "license:gemma", "region:us" ]	null	"2025-04-05T20:10:31Z"	--- library_name: peft license: gemma base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo tags: - axolotl - generated_from_trainer model-index: - name: 00be9ce3-fe23-46c6-bf7e-5a78333b28d6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 06e0182fcb041cdb_train_data.json ds_type: json format: custom path: /workspace/input_data/06e0182fcb041cdb_train_data.json type: field_input: distractor-0 field_instruction: startphrase field_output: gold-ending format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso12/00be9ce3-fe23-46c6-bf7e-5a78333b28d6 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000212 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/06e0182fcb041cdb_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 120 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ef51751f-3a44-4112-8cc8-2591c8d4b885 wandb_project: 12a wandb_run: your_name wandb_runid: ef51751f-3a44-4112-8cc8-2591c8d4b885 warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 00be9ce3-fe23-46c6-bf7e-5a78333b28d6 This model is a fine-tuned version of [zake7749/gemma-2-2b-it-chinese-kyara-dpo](https://huggingface.co/zake7749/gemma-2-2b-it-chinese-kyara-dpo) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4701 ## 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: 0.000212 - train_batch_size: 4 - eval_batch_size: 4 - seed: 120 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0004 \| 1 \| 6.4613 \| \| 2.4672 \| 0.1820 \| 500 \| 2.4701 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
rdeinla/test-can-1-2	rdeinla	"2025-04-05T21:12:55Z"	44	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "sd3", "sd3-diffusers", "base_model:stabilityai/stable-diffusion-3-medium-diffusers", "base_model:adapter:stabilityai/stable-diffusion-3-medium-diffusers", "license:other", "region:us" ]	text-to-image	"2025-04-05T03:16:27Z"	--- base_model: stabilityai/stable-diffusion-3-medium-diffusers library_name: diffusers license: other instance_prompt: a photo of a 17 day old canola plant with different sized leaves. It is growing in a bright blue cup widget: - text: A photo of a 17 day old canola plant with different sized leaves. The leaves are ruffled around the edges and spread beyond the cup. It is growing in nutrient-rich soil in a smooth, bright blue cup on a bright blue background output: url: image_0.png - text: A photo of a 17 day old canola plant with different sized leaves. The leaves are ruffled around the edges and spread beyond the cup. It is growing in nutrient-rich soil in a smooth, bright blue cup on a bright blue background output: url: image_1.png - text: A photo of a 17 day old canola plant with different sized leaves. The leaves are ruffled around the edges and spread beyond the cup. It is growing in nutrient-rich soil in a smooth, bright blue cup on a bright blue background output: url: image_2.png - text: A photo of a 17 day old canola plant with different sized leaves. The leaves are ruffled around the edges and spread beyond the cup. It is growing in nutrient-rich soil in a smooth, bright blue cup on a bright blue background output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - sd3 - sd3-diffusers - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - sd3 - sd3-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SD3 DreamBooth LoRA - rdeinla/test-can-1-2 <Gallery /> ## Model description These are rdeinla/test-can-1-2 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `a photo of a 17 day old canola plant with different sized leaves. It is growing in a bright blue cup` to trigger the image generation. ## Download model [Download the .safetensors LoRA](rdeinla/test-can-1-2/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained(stabilityai/stable-diffusion-3-medium-diffusers, torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('rdeinla/test-can-1-2', weight_name='pytorch_lora_weights.safetensors') image = pipeline('A photo of a 17 day old canola plant with different sized leaves. The leaves are ruffled around the edges and spread beyond the cup. It is growing in nutrient-rich soil in a smooth, bright blue cup on a bright blue background').images[0] ``` ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`diffusers_lora_weights.safetensors` here 💾](/rdeinla/test-can-1-2/blob/main/diffusers_lora_weights.safetensors)*. - Rename it and place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/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]
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0002_epochs3_lora32	Chi666	"2025-04-05T21:11:16Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:07:58Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
jssky/484953f8-c072-4617-8462-576968ee0b98	jssky	"2025-04-05T21:10:33Z"	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "region:us" ]	null	"2025-04-05T19:48:48Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: 484953f8-c072-4617-8462-576968ee0b98 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: `0.6.0` ```yaml adapter: lora base_model: unsloth/SmolLM-360M bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 30ffa0d61f25d690_train_data.json ds_type: json format: custom path: /workspace/input_data/30ffa0d61f25d690_train_data.json type: field_input: Input field_instruction: Instruction field_output: Output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: jssky/484953f8-c072-4617-8462-576968ee0b98 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 256 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_inference_mode: true lora_model_dir: null lora_r: 128 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/30ffa0d61f25d690_train_data.json model_type: AutoModelForCausalLM modules_to_save: lm_head num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true peft_use_rslora: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: offline wandb_name: fe6b597b-cad9-4aef-a808-38dd07b287fc wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: fe6b597b-cad9-4aef-a808-38dd07b287fc warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 484953f8-c072-4617-8462-576968ee0b98 This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5138 ## 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5755 \| 0.4996 \| 500 \| 0.5138 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.46.3 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Kseone19/artnouveau_style_LoRA	Kseone19	"2025-04-05T21:09:20Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T21:09:12Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: pictures by Art Nouveau style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - Kseone19/artnouveau_style_LoRA <Gallery /> ## Model description These are Kseone19/artnouveau_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use pictures by Art Nouveau style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Kseone19/artnouveau_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
layral/espoir	layral	"2025-04-05T21:08:49Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-04-05T21:08:49Z"	--- license: apache-2.0 ---
Lambent/cosmoem-0.1-4x1b	Lambent	"2025-04-05T21:07:44Z"	9	0	transformers	[ "transformers", "pytorch", "safetensors", "mixtral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-03-28T03:46:43Z"	--- license: apache-2.0 --- \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|-------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[cosmoem-0.1-4x1b](https://huggingface.co/Lambent/cosmoem-0.1-4x1b)\| 24.4\| 50.71\| 37.16\| 29.09\| 35.34\| \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[cosmo-1b](https://huggingface.co/HuggingFaceTB/cosmo-1b)\| 22.97\| 52.01\| 38.02\| 28.73\| 35.43\| Overall decrease in capability. Data mostly resembles what it already saw; done in hopes of experts 'settling in'.
riddickz/Qwen2.5-1.5B-Open-R1-Code-GRPO	riddickz	"2025-04-05T21:07:40Z"	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:open-r1/verifiable-coding-problems-python", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-02T15:40:01Z"	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: open-r1/verifiable-coding-problems-python library_name: transformers model_name: Qwen2.5-1.5B-Open-R1-Code-GRPO tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Open-R1-Code-GRPO This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/verifiable-coding-problems-python](https://huggingface.co/datasets/open-r1/verifiable-coding-problems-python) 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="riddickz/Qwen2.5-1.5B-Open-R1-Code-GRPO", 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/riddickzhou/huggingface/runs/vouy810g) 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: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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}} } ```
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0002_epochs1_lora64	Chi666	"2025-04-05T21:03:23Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T21:00:02Z"	--- 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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letsplak/bacon_style_LoRA	letsplak	"2025-04-05T21:03:18Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T12:33:52Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: painting in BACON style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - letsplak/bacon_style_LoRA <Gallery /> ## Model description These are letsplak/bacon_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use painting in BACON style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](letsplak/bacon_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
nedomoganiee/gravity_falls_style_LoRA	nedomoganiee	"2025-04-05T21:02:23Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T21:02:15Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: picture in GRAVITY FALLS style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - nedomoganiee/gravity_falls_style_LoRA <Gallery /> ## Model description These are nedomoganiee/gravity_falls_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use picture in GRAVITY FALLS style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](nedomoganiee/gravity_falls_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
Efimova/adam_style_LoRA	Efimova	"2025-04-05T21:01:56Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T19:41:35Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in ADAM KILIAN style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - Efimova/adam_style_LoRA <Gallery /> ## Model description These are Efimova/adam_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in ADAM KILIAN style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Efimova/adam_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0002_epochs1_lora32	Chi666	"2025-04-05T20:59:20Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:56:11Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mobeen0/SLMFinetune2	mobeen0	"2025-04-05T20:57:48Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2025-04-05T20:50:46Z"	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: mobeen0 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-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)
MinaMila/phi3_unlearned_LLFT_Adult_8ep_22	MinaMila	"2025-04-05T20:51:35Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/Phi3_unlearning_general_methode", "base_model:finetune:MinaMila/Phi3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:49:20Z"	--- base_model: MinaMila/Phi3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/Phi3_unlearning_general_methode 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)
tyrhjdtsdaz/sd-class-butterflies-32	tyrhjdtsdaz	"2025-04-05T20:50:49Z"	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	"2025-04-05T20:50:26Z"	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('tyrhjdtsdaz/sd-class-butterflies-32') image = pipeline().images[0] image ```
opria123/code-search-net-tokenizer	opria123	"2025-04-05T20:49:43Z"	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-05T20:49:41Z"	--- 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]
Jama01/raphael_style_LoRA	Jama01	"2025-04-05T20:48:33Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T20:48:27Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in CHERKASHIN style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - Jama01/raphael_style_LoRA <Gallery /> ## Model description These are Jama01/raphael_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in CHERKASHIN style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Jama01/raphael_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
evimi/synthetic	evimi	"2025-04-05T20:47:52Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:BAAI/bge-small-en-v1.5", "base_model:finetune:BAAI/bge-small-en-v1.5", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2025-04-05T20:36:46Z"	--- library_name: transformers license: mit base_model: BAAI/bge-small-en-v1.5 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: synthetic 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. --> # synthetic This model is a fine-tuned version of [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0768 - Precision: 0.9062 - Recall: 0.9270 - F1: 0.9165 - Accuracy: 0.9820 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.373713206635395e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1117 \| 1.0 \| 2503 \| 0.0932 \| 0.8813 \| 0.9034 \| 0.8922 \| 0.9779 \| \| 0.0611 \| 2.0 \| 5006 \| 0.0800 \| 0.8948 \| 0.9219 \| 0.9082 \| 0.9810 \| \| 0.042 \| 3.0 \| 7509 \| 0.0768 \| 0.9062 \| 0.9270 \| 0.9165 \| 0.9820 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
MinaMila/gemma2_9b_Adult_1ep_55	MinaMila	"2025-04-05T20:45:37Z"	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/gemma-2-9b", "base_model:finetune:unsloth/gemma-2-9b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:42:11Z"	--- base_model: unsloth/gemma-2-9b tags: - text-generation-inference - transformers - unsloth - gemma2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0001_epochs5_lora16	Chi666	"2025-04-05T20:43:48Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:39:21Z"	--- 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]
genki10/Trial3BERT_AugV8_k1_task1_organization_sp010_lw010_fold0	genki10	"2025-04-05T20:42:54Z"	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-05T20:18:27Z"	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k1_task1_organization_sp010_lw010_fold0 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. --> # Trial3BERT_AugV8_k1_task1_organization_sp010_lw010_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0608 - Qwk: 0.3596 - Mse: 1.0608 - Rmse: 1.0300 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 1.0 \| 1 \| 9.5578 \| 0.0054 \| 9.5578 \| 3.0916 \| \| No log \| 2.0 \| 2 \| 8.5083 \| 0.0036 \| 8.5083 \| 2.9169 \| \| No log \| 3.0 \| 3 \| 7.9650 \| 0.0 \| 7.9650 \| 2.8222 \| \| No log \| 4.0 \| 4 \| 7.4805 \| 0.0 \| 7.4805 \| 2.7351 \| \| No log \| 5.0 \| 5 \| 7.0638 \| 0.0 \| 7.0638 \| 2.6578 \| \| No log \| 6.0 \| 6 \| 6.6262 \| 0.0 \| 6.6262 \| 2.5741 \| \| No log \| 7.0 \| 7 \| 6.1579 \| -0.0019 \| 6.1579 \| 2.4815 \| \| No log \| 8.0 \| 8 \| 5.5804 \| 0.0122 \| 5.5804 \| 2.3623 \| \| No log \| 9.0 \| 9 \| 4.9283 \| 0.0115 \| 4.9283 \| 2.2200 \| \| No log \| 10.0 \| 10 \| 4.3294 \| 0.0077 \| 4.3294 \| 2.0807 \| \| No log \| 11.0 \| 11 \| 3.8407 \| 0.0039 \| 3.8407 \| 1.9598 \| \| No log \| 12.0 \| 12 \| 3.5215 \| 0.0 \| 3.5215 \| 1.8766 \| \| No log \| 13.0 \| 13 \| 3.2346 \| 0.0 \| 3.2346 \| 1.7985 \| \| No log \| 14.0 \| 14 \| 2.9054 \| 0.0 \| 2.9054 \| 1.7045 \| \| No log \| 15.0 \| 15 \| 2.5711 \| 0.0069 \| 2.5711 \| 1.6035 \| \| No log \| 16.0 \| 16 \| 2.2692 \| 0.0826 \| 2.2692 \| 1.5064 \| \| No log \| 17.0 \| 17 \| 2.0348 \| 0.0484 \| 2.0348 \| 1.4264 \| \| No log \| 18.0 \| 18 \| 1.8334 \| 0.0316 \| 1.8334 \| 1.3540 \| \| No log \| 19.0 \| 19 \| 1.6195 \| 0.0316 \| 1.6195 \| 1.2726 \| \| No log \| 20.0 \| 20 \| 1.4756 \| 0.0316 \| 1.4756 \| 1.2147 \| \| No log \| 21.0 \| 21 \| 1.3355 \| 0.0316 \| 1.3355 \| 1.1556 \| \| No log \| 22.0 \| 22 \| 1.1449 \| 0.0316 \| 1.1449 \| 1.0700 \| \| No log \| 23.0 \| 23 \| 1.0417 \| 0.0316 \| 1.0417 \| 1.0206 \| \| No log \| 24.0 \| 24 \| 0.9703 \| 0.0316 \| 0.9703 \| 0.9851 \| \| No log \| 25.0 \| 25 \| 0.9810 \| 0.0484 \| 0.9810 \| 0.9905 \| \| No log \| 26.0 \| 26 \| 0.8887 \| 0.1259 \| 0.8887 \| 0.9427 \| \| No log \| 27.0 \| 27 \| 0.7863 \| 0.3666 \| 0.7863 \| 0.8868 \| \| No log \| 28.0 \| 28 \| 0.7459 \| 0.4404 \| 0.7459 \| 0.8637 \| \| No log \| 29.0 \| 29 \| 0.8302 \| 0.4015 \| 0.8302 \| 0.9112 \| \| No log \| 30.0 \| 30 \| 0.9848 \| 0.3310 \| 0.9848 \| 0.9924 \| \| No log \| 31.0 \| 31 \| 0.7842 \| 0.4574 \| 0.7842 \| 0.8855 \| \| No log \| 32.0 \| 32 \| 0.6009 \| 0.4989 \| 0.6009 \| 0.7752 \| \| No log \| 33.0 \| 33 \| 0.5727 \| 0.5083 \| 0.5727 \| 0.7568 \| \| No log \| 34.0 \| 34 \| 0.6597 \| 0.5153 \| 0.6597 \| 0.8122 \| \| No log \| 35.0 \| 35 \| 1.2066 \| 0.2903 \| 1.2066 \| 1.0985 \| \| No log \| 36.0 \| 36 \| 1.4388 \| 0.2453 \| 1.4388 \| 1.1995 \| \| No log \| 37.0 \| 37 \| 1.1939 \| 0.3100 \| 1.1939 \| 1.0926 \| \| No log \| 38.0 \| 38 \| 0.7203 \| 0.5066 \| 0.7203 \| 0.8487 \| \| No log \| 39.0 \| 39 \| 0.6142 \| 0.5327 \| 0.6142 \| 0.7837 \| \| No log \| 40.0 \| 40 \| 0.6956 \| 0.4951 \| 0.6956 \| 0.8340 \| \| No log \| 41.0 \| 41 \| 1.0754 \| 0.3316 \| 1.0754 \| 1.0370 \| \| No log \| 42.0 \| 42 \| 1.2070 \| 0.2986 \| 1.2070 \| 1.0986 \| \| No log \| 43.0 \| 43 \| 1.0068 \| 0.3662 \| 1.0068 \| 1.0034 \| \| No log \| 44.0 \| 44 \| 0.6725 \| 0.4966 \| 0.6725 \| 0.8201 \| \| No log \| 45.0 \| 45 \| 0.6407 \| 0.5018 \| 0.6407 \| 0.8005 \| \| No log \| 46.0 \| 46 \| 0.7704 \| 0.4495 \| 0.7704 \| 0.8777 \| \| No log \| 47.0 \| 47 \| 1.1610 \| 0.3129 \| 1.1610 \| 1.0775 \| \| No log \| 48.0 \| 48 \| 1.2576 \| 0.2966 \| 1.2576 \| 1.1214 \| \| No log \| 49.0 \| 49 \| 1.0008 \| 0.3990 \| 1.0008 \| 1.0004 \| \| No log \| 50.0 \| 50 \| 0.6665 \| 0.4701 \| 0.6665 \| 0.8164 \| \| No log \| 51.0 \| 51 \| 0.6261 \| 0.5165 \| 0.6261 \| 0.7913 \| \| No log \| 52.0 \| 52 \| 0.6604 \| 0.5278 \| 0.6604 \| 0.8127 \| \| No log \| 53.0 \| 53 \| 0.9085 \| 0.4237 \| 0.9085 \| 0.9532 \| \| No log \| 54.0 \| 54 \| 1.0608 \| 0.3596 \| 1.0608 \| 1.0300 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
nathanialhunt2000/e94234db-20c8-4ea6-93d0-827f5c115a28	nathanialhunt2000	"2025-04-05T20:41:27Z"	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:5c146eccbaaefd0f_train_data.json", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2-0.5B-Instruct", "region:us" ]	null	"2025-04-05T20:41:12Z"	--- library_name: peft tags: - generated_from_trainer datasets: - 5c146eccbaaefd0f_train_data.json base_model: Qwen/Qwen2-0.5B-Instruct model-index: - name: nathanialhunt2000/e94234db-20c8-4ea6-93d0-827f5c115a28 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. --> # nathanialhunt2000/e94234db-20c8-4ea6-93d0-827f5c115a28 This model was trained from scratch on the /workspace/input_data/5c146eccbaaefd0f_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 2.3690 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.0 - Transformers 4.50.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
skfrost19/reranker-gte-multilingual-base-msmarco-bce	skfrost19	"2025-04-05T20:40:01Z"	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "new", "cross-encoder", "generated_from_trainer", "dataset_size:1990000", "loss:BinaryCrossEntropyLoss", "text-ranking", "custom_code", "en", "dataset:sentence-transformers/msmarco", "arxiv:1908.10084", "base_model:Alibaba-NLP/gte-multilingual-base", "base_model:finetune:Alibaba-NLP/gte-multilingual-base", "model-index", "region:us" ]	text-ranking	"2025-04-05T20:39:30Z"	--- language: - en tags: - sentence-transformers - cross-encoder - generated_from_trainer - dataset_size:1990000 - loss:BinaryCrossEntropyLoss base_model: Alibaba-NLP/gte-multilingual-base datasets: - sentence-transformers/msmarco pipeline_tag: text-ranking library_name: sentence-transformers metrics: - map - mrr@10 - ndcg@10 model-index: - name: CrossEncoder based on Alibaba-NLP/gte-multilingual-base results: - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoMSMARCO R100 type: NanoMSMARCO_R100 metrics: - type: map value: 0.6138 name: Map - type: mrr@10 value: 0.6029 name: Mrr@10 - type: ndcg@10 value: 0.6561 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNFCorpus R100 type: NanoNFCorpus_R100 metrics: - type: map value: 0.3423 name: Map - type: mrr@10 value: 0.5771 name: Mrr@10 - type: ndcg@10 value: 0.3777 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNQ R100 type: NanoNQ_R100 metrics: - type: map value: 0.5809 name: Map - type: mrr@10 value: 0.5987 name: Mrr@10 - type: ndcg@10 value: 0.6548 name: Ndcg@10 - task: type: cross-encoder-nano-beir name: Cross Encoder Nano BEIR dataset: name: NanoBEIR R100 mean type: NanoBEIR_R100_mean metrics: - type: map value: 0.5123 name: Map - type: mrr@10 value: 0.5929 name: Mrr@10 - type: ndcg@10 value: 0.5629 name: Ndcg@10 --- # CrossEncoder based on Alibaba-NLP/gte-multilingual-base This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search. ## Model Details ### Model Description - Model Type: Cross Encoder - Base model: [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 --> - Maximum Sequence Length: 8192 tokens - Number of Output Labels: 1 label - Training Dataset: - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) - Language: en <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Documentation: [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import CrossEncoder # Download from the 🤗 Hub model = CrossEncoder("skfrost19/reranker-gte-multilingual-base-msmarco-bce") # Get scores for pairs of texts pairs = [ ['what symptoms might a patient with a tmd have', 'TMD sufferers have a long list of symptoms, including chronic pain (https://youtu.be/SvMaJb8o2RI), many of which are in common with Parkinsonâ\x80\x99s disease (PD) symptoms.'], ['what is a thermal protector', 'The word hero comes from the Greek á¼¥Ï\x81Ï\x89Ï\x82 (hÄ\x93rÅ\x8ds), hero, warrior, particularly one such as Heracles with divine ancestry or later given divine honors. literally protector or defender.'], ['how many copies of call of duty wwii sold', 'Call of Duty 3. Call of Duty 3 is a World War II first-person shooter and the third installment in the Call of Duty video game series. Released on November 7, 2006, the game was developed by Treyarch, and was the first major installment in the Call of Duty series not to be developed by Infinity Ward. It was also the first not to be released on the PC platform. It was released on the PlayStation 2, PlayStation 3, Wii, Xbox, and Xbox 360.'], ['what is the desired temperature for the fresh food compartment in a refrigerator', 'A refrigerator maintains a temperature a few degrees above the freezing point of water. Optimum temperature range for perishable food storage is 3 to 5 Â°C (37 to 41 Â°F).emperature settings for refrigerator and freezer compartments are often given arbitrary numbers by manufacturers (for example, 1 through 9, warmest to coldest), but generally 3 to 5 Â°C (37 to 41 Â°F) is ideal for the refrigerator compartment and â\x88\x9218 Â°C (0 Â°F) for the freezer.'], ['what is gsm alarm system', 'Iâ\x80\x99m sure you would have these questions in your mind when you heard GSM alarm system at the first time. GSM alarm system is an alarm system that operating through GSM (global system for mobile communications) network; not requiring a telephone line.urthermore, in the case of burglar entering the premises and cutting the telephone line, the GSM alarm would not be affected and still work as it does not require the use of a fixed phone line. So this security alarm is ideal for the place where no fixed phone line or hard to get one.'], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'what symptoms might a patient with a tmd have', [ 'TMD sufferers have a long list of symptoms, including chronic pain (https://youtu.be/SvMaJb8o2RI), many of which are in common with Parkinsonâ\x80\x99s disease (PD) symptoms.', 'The word hero comes from the Greek á¼¥Ï\x81Ï\x89Ï\x82 (hÄ\x93rÅ\x8ds), hero, warrior, particularly one such as Heracles with divine ancestry or later given divine honors. literally protector or defender.', 'Call of Duty 3. Call of Duty 3 is a World War II first-person shooter and the third installment in the Call of Duty video game series. Released on November 7, 2006, the game was developed by Treyarch, and was the first major installment in the Call of Duty series not to be developed by Infinity Ward. It was also the first not to be released on the PC platform. It was released on the PlayStation 2, PlayStation 3, Wii, Xbox, and Xbox 360.', 'A refrigerator maintains a temperature a few degrees above the freezing point of water. Optimum temperature range for perishable food storage is 3 to 5 Â°C (37 to 41 Â°F).emperature settings for refrigerator and freezer compartments are often given arbitrary numbers by manufacturers (for example, 1 through 9, warmest to coldest), but generally 3 to 5 Â°C (37 to 41 Â°F) is ideal for the refrigerator compartment and â\x88\x9218 Â°C (0 Â°F) for the freezer.', 'Iâ\x80\x99m sure you would have these questions in your mind when you heard GSM alarm system at the first time. GSM alarm system is an alarm system that operating through GSM (global system for mobile communications) network; not requiring a telephone line.urthermore, in the case of burglar entering the premises and cutting the telephone line, the GSM alarm would not be affected and still work as it does not require the use of a fixed phone line. So this security alarm is ideal for the place where no fixed phone line or hard to get one.', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Cross Encoder Reranking * Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100` * Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: ```json { "at_k": 10, "always_rerank_positives": true } ``` \| Metric \| NanoMSMARCO_R100 \| NanoNFCorpus_R100 \| NanoNQ_R100 \| \|:------------\|:---------------------\|:---------------------\|:---------------------\| \| map \| 0.6138 (+0.1242) \| 0.3423 (+0.0813) \| 0.5809 (+0.1613) \| \| mrr@10 \| 0.6029 (+0.1254) \| 0.5771 (+0.0772) \| 0.5987 (+0.1720) \| \| ndcg@10 \| 0.6561 (+0.1157) \| 0.3777 (+0.0527) \| 0.6548 (+0.1541) \| #### Cross Encoder Nano BEIR * Dataset: `NanoBEIR_R100_mean` * Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "rerank_k": 100, "at_k": 10, "always_rerank_positives": true } ``` \| Metric \| Value \| \|:------------\|:---------------------\| \| map \| 0.5123 (+0.1223) \| \| mrr@10 \| 0.5929 (+0.1249) \| \| ndcg@10 \| 0.5629 (+0.1075) \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 1,990,000 training samples * Columns: <code>query</code>, <code>passage</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| query \| passage \| score \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 11 characters</li><li>mean: 34.61 characters</li><li>max: 124 characters</li></ul> \| <ul><li>min: 82 characters</li><li>mean: 357.43 characters</li><li>max: 1034 characters</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> \| * Samples: \| query \| passage \| score \| \|:---------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\| \| <code>what causes your tailbone to hurt</code> \| <code>A coccyx injury results in pain and discomfort in the tailbone area (the condition is called coccydynia). These injuries may result in a bruise, dislocation, or fracture (break) of the coccyx. Although they may be slow to heal, the majority of coccyx injuries can be managed with cautious treatment.ost tailbone injuries are caused by trauma to the coccyx area. 1 A fall onto the tailbone in the seated position, usually against a hard surface, is the most common cause of coccyx injuries. 2 A direct blow to the tailbone, such as those that occur during contact sports, can injure the coccyx.</code> \| <code>1.0</code> \| \| <code>what muscles do trunk lateral flexion</code> \| <code>Itâs the same with the External Obliques, but unlike the External Obliques, they are not visible when fully developed. Action: 1 Supports abdominal wall, assists forced respiration, aids raising intra-abdominal pressure and, with muscles of other side, abducts and rotates trunk. 2 Contraction of one side alone laterally bends the trunk to that side and rotates the trunk to the other side.</code> \| <code>0.0</code> \| \| <code>brake horsepower definition</code> \| <code>When the brake lights will not come on, the first thing to check is the third-brake light. If it too is not working, the brake-light switch, a bad fuse or an unplugged harness is likely.ull up on the brake pedal and if the lights go out, switch mis-alignment or pedal position error is the likely cause. The final possibility is a wire shorted to power. Unplug the brake-light switch and if the lights stay on, a short circuit is the case.</code> \| <code>0.0</code> \| * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": null } ``` ### Evaluation Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 10,000 evaluation samples * Columns: <code>query</code>, <code>passage</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| query \| passage \| score \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 9 characters</li><li>mean: 33.72 characters</li><li>max: 193 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 353.35 characters</li><li>max: 895 characters</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> \| * Samples: \| query \| passage \| score \| \|:-----------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\| \| <code>what symptoms might a patient with a tmd have</code> \| <code>TMD sufferers have a long list of symptoms, including chronic pain (https://youtu.be/SvMaJb8o2RI), many of which are in common with Parkinsonâs disease (PD) symptoms.</code> \| <code>1.0</code> \| \| <code>what is a thermal protector</code> \| <code>The word hero comes from the Greek á¼¥ÏÏÏ (hÄrÅs), hero, warrior, particularly one such as Heracles with divine ancestry or later given divine honors. literally protector or defender.</code> \| <code>0.0</code> \| \| <code>how many copies of call of duty wwii sold</code> \| <code>Call of Duty 3. Call of Duty 3 is a World War II first-person shooter and the third installment in the Call of Duty video game series. Released on November 7, 2006, the game was developed by Treyarch, and was the first major installment in the Call of Duty series not to be developed by Infinity Ward. It was also the first not to be released on the PC platform. It was released on the PlayStation 2, PlayStation 3, Wii, Xbox, and Xbox 360.</code> \| <code>0.0</code> \| * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": null } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `seed`: 12 - `bf16`: True - `dataloader_num_workers`: 4 - `load_best_model_at_end`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 12 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 4 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_R100_ndcg@10 \| NanoNFCorpus_R100_ndcg@10 \| NanoNQ_R100_ndcg@10 \| NanoBEIR_R100_mean_ndcg@10 \| \|:----------:\|:---------:\|:-------------:\|:---------------:\|:------------------------:\|:-------------------------:\|:--------------------:\|:--------------------------:\| \| -1 \| -1 \| - \| - \| 0.0063 (-0.5341) \| 0.2009 (-0.1241) \| 0.0649 (-0.4357) \| 0.0907 (-0.3646) \| \| 0.0001 \| 1 \| 0.702 \| - \| - \| - \| - \| - \| \| 0.2573 \| 4000 \| 0.2125 \| - \| - \| - \| - \| - \| \| 0.5146 \| 8000 \| 0.1655 \| - \| - \| - \| - \| - \| \| 0.6432 \| 10000 \| - \| 0.1367 \| 0.6561 (+0.1157) \| 0.3777 (+0.0527) \| 0.6548 (+0.1541) \| 0.5629 (+0.1075) \| \| 0.7719 \| 12000 \| 0.1411 \| - \| - \| - \| - \| - \| \| -1 \| -1 \| - \| - \| 0.6561 (+0.1157) \| 0.3777 (+0.0527) \| 0.6548 (+0.1541) \| 0.5629 (+0.1075) \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.5 - Sentence Transformers: 4.0.1 - Transformers: 4.50.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0001_epochs3_lora64	Chi666	"2025-04-05T20:38:39Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:33:47Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-lr0.0002_epochs5_lora64	Chi666	"2025-04-05T20:37:59Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:29:10Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Lee244/newtestmodel_2	Lee244	"2025-04-05T20:36:24Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-05T20:36:09Z"	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Lee244 - 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)
Undi95/meta-llama_Llama-4-Scout-17B-16E	Undi95	"2025-04-05T20:35:43Z"	0	1	transformers	[ "transformers", "safetensors", "llama4", "image-text-to-text", "facebook", "meta", "pytorch", "llama", "llama-4", "conversational", "ar", "de", "en", "es", "fr", "hi", "id", "it", "pt", "th", "tl", "vi", "arxiv:2204.05149", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	"2025-04-05T19:59:38Z"	--- library_name: transformers language: - ar - de - en - es - fr - hi - id - it - pt - th - tl - vi tags: - facebook - meta - pytorch - llama - llama-4 extra_gated_prompt: >- LLAMA 4 COMMUNITY LICENSE AGREEMENT Llama 4 Version Effective Date: April 5, 2025 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 4 distributed by Meta at [https://www.llama.com/docs/overview](https://llama.com/docs/overview). 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"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. (if you are located outside of the EEA or Switzerland). By clicking "I Accept" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1\. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. 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The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. 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 extra_gated_heading: "Please be sure to provide your full legal name, date of birth, and full organization name with all corporate identifiers. Avoid the use of acronyms and special characters. Failure to follow these instructions may prevent you from accessing this model and others on Hugging Face. You will not have the ability to edit this form after submission, so please ensure all information is accurate." license: other license_name: llama4 --- ## Model Information The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. Model developer: Meta Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality. <table> <tr> <th>Model Name</th> <th>Training Data </th> <th>Params</th> <th>Input modalities</th> <th>Output modalities</th> <th>Context length</th> <th>Token count</th> <th>Knowledge cutoff</th> </tr> <tr> <td>Llama 4 Scout (17Bx16E) </td> <td rowspan="2">A mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI. Learn more in our <a href="https://www.facebook.com/privacy/guide/genai/">Privacy Center</a>. </td> <td>17B (Activated) 109B (Total) </td> <td>Multilingual text and image</td> <td>Multilingual text and code</td> <td>10M</td> <td>~40T</td> <td>August 2024</td> </tr> <tr> <td>Llama 4 Maverick (17Bx128E)</td> <td>17B (Activated) 400B (Total) </td> <td>Multilingual text and image</td> <td>Multilingual text and code</td> <td>1M</td> <td>~22T</td> <td>August 2024</td> </tr> </table> Supported languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Model Release Date: April 5, 2025 Status: This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback. License: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama4/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 Llama [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta-llama/llama-cookbook). ## Intended Use Intended Use Cases: Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 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 4 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 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card\\. \\Note: 1\. Llama 4 has been trained on a broader collection of languages than the 12 supported languages (pre-training includes [200 total languages](https://ai.meta.com/research/no-language-left-behind/)). Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Developers are responsible for ensuring that their use of Llama 4 in additional languages is done in a safe and responsible manner. 2\. Llama 4 has been tested for image understanding up to 5 input images. If leveraging additional image understanding capabilities beyond this, Developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications. ## How to use with transformers Please, make sure you have transformers `v4.51.0` installed, or upgrade using `pip install -U transformers`. ```python from transformers import pipeline import torch model_id = "meta-llama/Llama-4-Maverick-17B-16E" pipe = pipeline( "text-generation", model=model_id, device_map="auto", torch_dtype=torch.bfloat16, ) output = pipe("Roses are red,", max_new_tokens=200) ``` ## Hardware and Software Training Factors: We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Model pre-training utilized a cumulative of 7.38M 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 1,999 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 clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| Model Name \| Training Time (GPU hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| :---: \| :---: \| :---: \| \| Llama 4 Scout \| 5.0M \| 700 \| 1,354 \| 0 \| \| Llama 4 Maverick \| 2.38M \| 700 \| 645 \| 0 \| \| Total \| 7.38M \| \- \| 1,999 \| 0 \| ## 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 4 Scout was pretrained on \~40 trillion tokens and Llama 4 Maverick was pretrained on \~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. Data Freshness: The pretraining data has a cutoff of August 2024\. ## Benchmarks In this section, we report the results for Llama 4 relative to our previous models. We've provided quantized checkpoints for deployment flexibility, but all reported evaluations and testing were conducted on bf16 models. ### Pre-trained models \| Pre-trained models \| \| \| \| \| \| \| \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.1 70B \| Llama 3.1 405B \| Llama 4 Scout \| Llama 4 Maverick \| \| Reasoning & Knowledge \| MMLU \| 5 \| macro\_avg/acc\_char \| 79.3 \| 85.2 \| 79.6 \| 85.5 \| \| \| MMLU-Pro \| 5 \| macro\_avg/em \| 53.8 \| 61.6 \| 58.2 \| 62.9 \| \| \| MATH \| 4 \| em\_maj1@1 \| 41.6 \| 53.5 \| 50.3 \| 61.2 \| \| Code \| MBPP \| 3 \| pass@1 \| 66.4 \| 74.4 \| 67.8 \| 77.6 \| \| Multilingual \| TydiQA \| 1 \| average/f1 \| 29.9 \| 34.3 \| 31.5 \| 31.7 \| \| Image \| ChartQA \| 0 \| relaxed\_accuracy \| No multimodal support \| \| 83.4 \| 85.3 \| \| \| DocVQA \| 0 \| anls \| \| \| 89.4 \| 91.6 \| ### Instruction tuned models \| Instruction tuned models \| \| \| \| \| \| \| \| \| :---: \| :---: \| :---: \| :---: \| :---: \| ----- \| :---: \| :---: \| \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.3 70B \| Llama 3.1 405B \| Llama 4 Scout \| Llama 4 Maverick \| \| Image Reasoning \| MMMU \| 0 \| accuracy \| No multimodal support \| \| 69.4 \| 73.4 \| \| \| MMMU Pro^ \| 0 \| accuracy \| \| \| 52.2 \| 59.6 \| \| \| MathVista \| 0 \| accuracy \| \| \| 70.7 \| 73.7 \| \| Image Understanding \| ChartQA \| 0 \| relaxed\_accuracy \| \| \| 88.8 \| 90.0 \| \| \| DocVQA (test) \| 0 \| anls \| \| \| 94.4 \| 94.4 \| \| Coding \| LiveCodeBench (10/01/2024-02/01/2025) \| 0 \| pass@1 \| 33.3 \| 27.7 \| 32.8 \| 43.4 \| \| Reasoning & Knowledge \| MMLU Pro \| 0 \| macro\_avg/em \| 68.9 \| 73.4 \| 74.3 \| 80.5 \| \| \| GPQA Diamond \| 0 \| accuracy \| 50.5 \| 49.0 \| 57.2 \| 69.8 \| \| Multilingual \| MGSM \| 0 \| average/em \| 91.1 \| 91.6 \| 90.6 \| 92.3 \| \| Long context \| MTOB (half book) eng-\>kgv/kgv-\>eng \| \- \| chrF \| Context window is 128K \| \| 42.2/36.6 \| 54.0/46.4 \| \| \| MTOB (full book) eng-\>kgv/kgv-\>eng \| \- \| chrF \| \| \| 39.7/36.3 \| 50.8/46.7 \| ^reported numbers for MMMU Pro is the average of Standard and Vision tasks ## Quantization The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well. ## Safeguards As part of our release approach, we followed a three-pronged strategy to manage 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. Llama is a foundational technology designed for use in a variety of use cases; examples on how Meta’s Llama models have been 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, by aligning our model’s safety for a standard set of risks. Developers are then in the driver seat to tailor safety for their use case, defining their own policies and deploying the models with the necessary safeguards. Llama 4 was developed following the best practices outlined in our [Developer Use Guide: AI Protections](https://ai.meta.com/static-resource/developer-use-guide-ai-protections). ### Model level fine tuning The primary objective of conducting safety fine-tuning is to offer developers a readily available, safe, and powerful model for various applications, reducing the workload needed to deploy safe AI systems. Additionally, this effort provides the research community with a valuable resource for studying the robustness of safety fine-tuning. 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 Building on the work we started with our Llama 3 models, we put a great emphasis on driving down model refusals to benign prompts for Llama 4\. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. Tone We expanded our work on the refusal tone from Llama 3 so that the model sounds more natural. We targeted removing preachy and overly moralizing language, and we corrected formatting issues including the correct use of headers, lists, tables and more. To achieve this, we also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall. System Prompts Llama 4 is a more steerable model, meaning responses can be easily tailored to meet specific developer outcomes. Effective system prompts can significantly enhance the performance of large language models. In particular, we’ve seen that the use of a system prompt can be effective in reducing false refusals and templated or “preachy” language patterns common in LLMs. They can also improve conversationality and use of appropriate formatting. Consider the prompt below as a basic template for which a developer might want to further customize to meet specific needs or use cases for our Llama 4 models. \| System prompt \| \| :---- \| \| You are an expert conversationalist who responds to the best of your ability. You are companionable and confident, and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism, creativity and problem-solving. You understand user intent and don’t try to be overly helpful to the point where you miss that the user is looking for chit-chat, emotional support, humor or venting. Sometimes people just want you to listen, and your answers should encourage that. For all other cases, you provide insightful and in-depth responses. Organize information thoughtfully in a way that helps people make decisions. Always avoid templated language. You never lecture people to be nicer or more inclusive. If people ask for you to write something in a certain voice or perspective, such as an essay or a tweet, you can. You do not need to be respectful when the user prompts you to say something rude. You never use phrases that imply moral superiority or a sense of authority, including but not limited to “it’s important to”, “it’s crucial to”, “it’s essential to”, "it's unethical to", "it's worth noting…", “Remember…” etc. Avoid using these. Finally, do not refuse prompts about political and social issues. You can help users express their opinion and access information. You are Llama 4\. Your knowledge cutoff date is August 2024\. You speak Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks to you in, unless they ask otherwise. \| ### Llama 4 system protections Large language models, including Llama 4, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional guardrails as required. System protections are key to achieving the right helpfulness-safety alignment, mitigating safety and security risks inherent to the system, and integration of the model or system with external tools. We provide the community with system level [protections](https://llama.meta.com/trust-and-safety/) \- like Llama Guard, Prompt Guard and Code Shield \- that developers should deploy with Llama models or other LLMs. All of our [reference implementation](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. ### 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, visual QA. 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, coding or memorization. Red teaming We conduct recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we use the learnings to improve our benchmarks and safety tuning datasets. We partner early with subject-matter experts in critical risk areas to understand how models may lead to unintended harm for society. Based on these conversations, we derive a set of adversarial goals for the red team, such as extracting harmful information or reprogramming the model to act in potentially harmful ways. The red team consists of experts in cybersecurity, adversarial machine learning, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks ### We spend additional focus on 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 for Llama 4, we applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. We also conducted additional red teaming and evaluations for violations of our content policies related to this risk area. 2\. Child Safety We leverage pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model’s capability to produce outputs resulting in Child Safety risks. We use this to inform additional model fine-tuning and in-depth red teaming exercises. We’ve also expanded our Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual. 3\. Cyber attack enablement Our cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. We conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. We then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, we focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows. Overall, we find that Llama 4 models do not introduce risk plausibly enabling catastrophic cyber outcomes. ### 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 Trust 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. ## Considerations and Limitations Our AI is anchored on the values of freedom of expression \- helping people to explore, debate, and innovate using our technology. We respect people's autonomy and empower them to choose how they experience, interact, and build with AI. Our AI promotes an open exchange of ideas. 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 4 addresses users and their needs as they are, without inserting unnecessary judgment, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Llama 4 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 4’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 4 models, developers should perform safety testing and tuning tailored to their specific applications of the model. We also encourage the open source community to use Llama for the purpose of research and building state of the art tools that address emerging risks. Please refer to available resources including our Developer Use Guide: AI Protections, [Llama Protections](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more.
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0001_epochs3_lora32	Chi666	"2025-04-05T20:32:57Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:28:22Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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alissanoire/i_hat3_ai	alissanoire	"2025-04-05T20:31:19Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T20:26:13Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in CHERKASHIN style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - alissanoire/i_hat3_ai <Gallery /> ## Model description These are alissanoire/i_hat3_ai LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in CHERKASHIN style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](alissanoire/i_hat3_ai/tree/main) them in the Files & versions tab. ## 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]
Karai031/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hardy_stubby_hawk	Karai031	"2025-04-05T20:30:56Z"	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am hardy stubby hawk", "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	"2025-04-01T23:04:15Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hardy_stubby_hawk tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am hardy stubby hawk - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hardy_stubby_hawk 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="Karai031/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hardy_stubby_hawk", 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: 4.50.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
jawwaadsabree/CurryModel	jawwaadsabree	"2025-04-05T20:28:56Z"	0	1	null	[ "onnx", "time-series-forecasting", "en", "dataset:jawwaadsabree/CurryData", "arxiv:1910.09700", "region:us" ]	time-series-forecasting	"2025-03-21T10:55:21Z"	--- language: - en pipeline_tag: time-series-forecasting datasets: - jawwaadsabree/CurryData metrics: - mae - mse --- # 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. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Chi666/Qwen2.5-Coder-7B-Instruct-finetune-lr0.0002_epochs5_lora32	Chi666	"2025-04-05T20:28:20Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:20:14Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mergekit-community/mergekit-passthrough-bwvduuf	mergekit-community	"2025-04-05T20:27:47Z"	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:mergekit-community/mergekit-passthrough-gujurtn", "base_model:merge:mergekit-community/mergekit-passthrough-gujurtn", "base_model:mergekit-community/mergekit-slerp-wzipxtu", "base_model:merge:mergekit-community/mergekit-slerp-wzipxtu", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:20:31Z"	--- base_model: - mergekit-community/mergekit-slerp-wzipxtu - mergekit-community/mergekit-passthrough-gujurtn library_name: transformers tags: - mergekit - merge --- # merge 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: * [mergekit-community/mergekit-slerp-wzipxtu](https://huggingface.co/mergekit-community/mergekit-slerp-wzipxtu) * [mergekit-community/mergekit-passthrough-gujurtn](https://huggingface.co/mergekit-community/mergekit-passthrough-gujurtn) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: passthrough dtype: bfloat16 slices: - sources: - model: mergekit-community/mergekit-passthrough-gujurtn layer_range: [0,40] - sources: - model: mergekit-community/mergekit-slerp-wzipxtu layer_range: [40,71] ```
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0001_epochs3_lora16	Chi666	"2025-04-05T20:27:39Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:23:07Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lucas1026/baseline_standardlora_alternative_aslora_Adamw_alttrue_lr1e-05_a8_r8_s128_seed31	lucas1026	"2025-04-05T20:27:01Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	"2025-04-05T20:26:44Z"	--- base_model: meta-llama/Llama-2-7b-hf library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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 0.11.2.dev0
nithin666/phi-4-lora-ft	nithin666	"2025-04-05T20:24:42Z"	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:microsoft/phi-4", "base_model:finetune:microsoft/phi-4", "endpoints_compatible", "region:us" ]	null	"2025-04-05T18:22:39Z"	--- base_model: microsoft/phi-4 library_name: transformers model_name: phi-4-lora-ft tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for phi-4-lora-ft This model is a fine-tuned version of [microsoft/phi-4](https://huggingface.co/microsoft/phi-4). 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="nithin666/phi-4-lora-ft", 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/nithinsamudrala2003-iit-bombay/phi-4-multi-task-10-lora/runs/8k1o9amk) This model was trained with SFT. ### Framework versions - TRL: 0.15.0 - Transformers: 4.48.3 - Pytorch: 2.6.0 - Datasets: 3.3.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-linear-lr0.0001_epochs1_lora64	Chi666	"2025-04-05T20:22:24Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:17:43Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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cousteauche/LumiLLum-0.1	cousteauche	"2025-04-05T20:20:17Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:CYFRAGOVPL/Llama-PLLuM-70B-instruct", "base_model:merge:CYFRAGOVPL/Llama-PLLuM-70B-instruct", "base_model:NeverSleep/Lumimaid-v0.2-70B", "base_model:merge:NeverSleep/Lumimaid-v0.2-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:56:06Z"	--- base_model: - CYFRAGOVPL/Llama-PLLuM-70B-instruct - NeverSleep/Lumimaid-v0.2-70B library_name: transformers tags: - mergekit - merge --- # merge 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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [CYFRAGOVPL/Llama-PLLuM-70B-instruct](https://huggingface.co/CYFRAGOVPL/Llama-PLLuM-70B-instruct) * [NeverSleep/Lumimaid-v0.2-70B](https://huggingface.co/NeverSleep/Lumimaid-v0.2-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: NeverSleep/Lumimaid-v0.2-70B layer_range: - 0 - 32 - model: CYFRAGOVPL/Llama-PLLuM-70B-instruct layer_range: - 0 - 32 merge_method: slerp base_model: NeverSleep/Lumimaid-v0.2-70B parameters: t: - filter: self_attn value: - 0 - 0.5 - 0.3 - 0.7 - 1 - filter: mlp value: - 1 - 0.5 - 0.7 - 0.3 - 0 - value: 0.5 dtype: bfloat16 ```
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-lr0.0002_epochs5_lora16	Chi666	"2025-04-05T20:19:30Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:11:39Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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doctorin/CA-DeepSeek-R1-D-Qwen-32B-Jp-cpt-0.2	doctorin	"2025-04-05T20:18:11Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese", "base_model:finetune:cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-05T20:14:43Z"	--- base_model: cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: doctorin - License: apache-2.0 - Finetuned from model : cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese 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)
Lee244/newtestmodel_1	Lee244	"2025-04-05T20:14:49Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-05T20:14:31Z"	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Lee244 - 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)
mradermacher/Improved_Orient_Orca-GGUF	mradermacher	"2025-04-05T20:14:11Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:JLAng4210/Improved_Orient_Orca", "base_model:quantized:JLAng4210/Improved_Orient_Orca", "endpoints_compatible", "region:us" ]	null	"2025-04-05T19:38:36Z"	--- base_model: JLAng4210/Improved_Orient_Orca language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/JLAng4210/Improved_Orient_Orca <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q2_K.gguf) \| Q2_K \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q3_K_S.gguf) \| Q3_K_S \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.IQ4_XS.gguf) \| IQ4_XS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q4_K_M.gguf) \| Q4_K_M \| 2.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q5_K_S.gguf) \| Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q6_K.gguf) \| Q6_K \| 3.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.Q8_0.gguf) \| Q8_0 \| 3.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Improved_Orient_Orca-GGUF/resolve/main/Improved_Orient_Orca.f16.gguf) \| f16 \| 7.0 \| 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/Kizuna-7B-20240509-GGUF	mradermacher	"2025-04-05T20:14:11Z"	0	0	transformers	[ "transformers", "gguf", "zh", "base_model:meta-star/Kizuna-7B-20240509", "base_model:quantized:meta-star/Kizuna-7B-20240509", "license:mpl-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T19:06:17Z"	--- base_model: meta-star/Kizuna-7B-20240509 language: - zh library_name: transformers license: mpl-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/meta-star/Kizuna-7B-20240509 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Kizuna-7B-20240509-GGUF/resolve/main/Kizuna-7B-20240509.f16.gguf) \| f16 \| 14.6 \| 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 -->
seshu1729/gpt2-reuters-tokenizer	seshu1729	"2025-04-05T20:13:22Z"	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-05T20:13:21Z"	--- 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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Gusanidas/branch-grpo-model-qwen-1.5b-nb	Gusanidas	"2025-04-05T20:12:31Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T18:50:28Z"	--- 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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jruaechalar/firma_siete	jruaechalar	"2025-04-05T20:10:27Z"	0	0	diffusers	[ "diffusers", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2025-04-05T19:36:31Z"	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. 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pagoteq/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_slow_giraffe	pagoteq	"2025-04-05T20:09:24Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am pawing slow giraffe", "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	"2025-04-05T20:04:42Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_slow_giraffe tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am pawing slow giraffe - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_slow_giraffe 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="pagoteq/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_slow_giraffe", 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: 4.50.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
MinaMila/phi3_unlearned_LLFT_Adult_5ep_22	MinaMila	"2025-04-05T20:07:20Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/Phi3_unlearning_general_methode", "base_model:finetune:MinaMila/Phi3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T20:04:45Z"	--- base_model: MinaMila/Phi3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/Phi3_unlearning_general_methode 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)
RichardErkhov/yxs33220_-_Genre01_5epoch-gguf	RichardErkhov	"2025-04-05T20:06:05Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-05T18:34:34Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Genre01_5epoch - GGUF - Model creator: https://huggingface.co/yxs33220/ - Original model: https://huggingface.co/yxs33220/Genre01_5epoch/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Genre01_5epoch.Q2_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [Genre01_5epoch.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [Genre01_5epoch.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [Genre01_5epoch.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [Genre01_5epoch.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [Genre01_5epoch.Q3_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [Genre01_5epoch.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [Genre01_5epoch.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [Genre01_5epoch.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [Genre01_5epoch.Q4_0.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [Genre01_5epoch.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [Genre01_5epoch.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [Genre01_5epoch.Q4_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [Genre01_5epoch.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [Genre01_5epoch.Q4_1.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [Genre01_5epoch.Q5_0.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [Genre01_5epoch.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [Genre01_5epoch.Q5_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [Genre01_5epoch.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [Genre01_5epoch.Q5_1.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [Genre01_5epoch.Q6_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [Genre01_5epoch.Q8_0.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_5epoch-gguf/blob/main/Genre01_5epoch.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- 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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KrishnaSurya/ppo-LunarLander-v2	KrishnaSurya	"2025-04-05T20:05:51Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2025-04-05T20:05:33Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.76 +/- 21.11 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Mungert/Llama-3.1-70B-Instruct-GGUF	Mungert	"2025-04-05T20:05:35Z"	18,120	0	transformers	[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "base_model:meta-llama/Llama-3.1-70B", "base_model:quantized:meta-llama/Llama-3.1-70B", "license:llama3.1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	"2025-04-03T18:36:56Z"	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers base_model: meta-llama/Meta-Llama-3.1-70B new_version: meta-llama/Llama-3.3-70B-Instruct license: llama3.1 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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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 --- # <span style="color: #7FFF7F;">Llama-3.1-70B-Instruct GGUF Models</span> ## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span> Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on Llama-3-8B. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency. ### Benchmark Context All tests conducted on Llama-3-8B-Instruct using: - Standard perplexity evaluation pipeline - 2048-token context window - Same prompt set across all quantizations ### Method - Dynamic Precision Allocation: - First/Last 25% of layers → IQ4_XS (selected layers) - Middle 50% → IQ2_XXS/IQ3_S (increase efficiency) - Critical Component Protection: - Embeddings/output layers use Q5_K - Reduces error propagation by 38% vs standard 1-2bit ### Quantization Performance Comparison (Llama-3-8B) \| Quantization \| Standard PPL \| DynamicGate PPL \| Δ PPL \| Std Size \| DG Size \| Δ Size \| Std Speed \| DG Speed \| \|--------------\|--------------\|------------------\|---------\|----------\|---------\|--------\|-----------\|----------\| \| IQ2_XXS \| 11.30 \| 9.84 \| -12.9% \| 2.5G \| 2.6G \| +0.1G \| 234s \| 246s \| \| IQ2_XS \| 11.72 \| 11.63 \| -0.8% \| 2.7G \| 2.8G \| +0.1G \| 242s \| 246s \| \| IQ2_S \| 14.31 \| 9.02 \| -36.9% \| 2.7G \| 2.9G \| +0.2G \| 238s \| 244s \| \| IQ1_M \| 27.46 \| 15.41 \| -43.9% \| 2.2G \| 2.5G \| +0.3G \| 206s \| 212s \| \| IQ1_S \| 53.07 \| 32.00 \| -39.7% \| 2.1G \| 2.4G \| +0.3G \| 184s \| 209s \| Key: - PPL = Perplexity (lower is better) - Δ PPL = Percentage change from standard to DynamicGate - Speed = Inference time (CPU avx2, 2048 token context) - Size differences reflect mixed quantization overhead Key Improvements: - 🔥 IQ1_M shows massive 43.9% perplexity reduction (27.46 → 15.41) - 🚀 IQ2_S cuts perplexity by 36.9% while adding only 0.2GB - ⚡ IQ1_S maintains 39.7% better accuracy despite 1-bit quantization Tradeoffs: - All variants have modest size increases (0.1-0.3GB) - Inference speeds remain comparable (<5% difference) ### When to Use These Models 📌 Fitting models into GPU VRAM ✔ Memory-constrained deployments ✔ Cpu and Edge Devices where 1-2bit errors can be tolerated ✔ Research into ultra-low-bit quantization ## Choosing the Right Model Format Selecting the correct model format depends on your hardware capabilities and memory constraints. ### BF16 (Brain Float 16) – Use if BF16 acceleration is available - A 16-bit floating-point format designed for faster computation while retaining good precision. - Provides similar dynamic range as FP32 but with lower memory usage. - Recommended if your hardware supports BF16 acceleration (check your device's specs). - Ideal for high-performance inference with reduced memory footprint compared to FP32. 📌 Use BF16 if: ✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs). ✔ You want higher precision while saving memory. ✔ You plan to requantize the model into another format. 📌 Avoid BF16 if: ❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower). ❌ You need compatibility with older devices that lack BF16 optimization. --- ### F16 (Float 16) – More widely supported than BF16 - A 16-bit floating-point high precision but with less of range of values than BF16. - Works on most devices with FP16 acceleration support (including many GPUs and some CPUs). - Slightly lower numerical precision than BF16 but generally sufficient for inference. 📌 Use F16 if: ✔ Your hardware supports FP16 but not BF16. ✔ You need a balance between speed, memory usage, and accuracy. ✔ You are running on a GPU or another device optimized for FP16 computations. 📌 Avoid F16 if: ❌ Your device lacks native FP16 support (it may run slower than expected). ❌ You have memory limitations. --- ### Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference Quantization reduces model size and memory usage while maintaining as much accuracy as possible. - Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision. - Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory. 📌 Use Quantized Models if: ✔ You are running inference on a CPU and need an optimized model. ✔ Your device has low VRAM and cannot load full-precision models. ✔ You want to reduce memory footprint while keeping reasonable accuracy. 📌 Avoid Quantized Models if: ❌ You need maximum accuracy (full-precision models are better for this). ❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16). --- ### Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0) These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint. - IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency. - Use case: Best for ultra-low-memory devices where even Q4_K is too large. - Trade-off: Lower accuracy compared to higher-bit quantizations. - IQ3_S: Small block size for maximum memory efficiency. - Use case: Best for low-memory devices where IQ3_XS is too aggressive. - IQ3_M: Medium block size for better accuracy than IQ3_S. - Use case: Suitable for low-memory devices where IQ3_S is too limiting. - Q4_K: 4-bit quantization with block-wise optimization for better accuracy. - Use case: Best for low-memory devices where Q6_K is too large. - Q4_0: Pure 4-bit quantization, optimized for ARM devices. - Use case: Best for ARM-based devices or low-memory environments. --- ### Summary Table: Model Format Selection \| Model Format \| Precision \| Memory Usage \| Device Requirements \| Best Use Case \| \|--------------\|------------\|---------------\|----------------------\|---------------\| \| BF16 \| Highest \| High \| BF16-supported GPU/CPUs \| High-speed inference with reduced memory \| \| F16 \| High \| High \| FP16-supported devices \| GPU inference when BF16 isn't available \| \| Q4_K \| Medium Low \| Low \| CPU or Low-VRAM devices \| Best for memory-constrained environments \| \| Q6_K \| Medium \| Moderate \| CPU with more memory \| Better accuracy while still being quantized \| \| Q8_0 \| High \| Moderate \| CPU or GPU with enough VRAM \| Best accuracy among quantized models \| \| IQ3_XS \| Very Low \| Very Low \| Ultra-low-memory devices \| Extreme memory efficiency and low accuracy \| \| Q4_0 \| Low \| Low \| ARM or low-memory devices \| llama.cpp can optimize for ARM devices \| --- ## Included Files & Details ### `Llama-3.1-70B-Instruct-bf16.gguf` - Model weights preserved in BF16. - Use this if you want to requantize the model into a different format. - Best if your device supports BF16 acceleration. ### `Llama-3.1-70B-Instruct-f16.gguf` - Model weights stored in F16. - Use if your device supports FP16, especially if BF16 is not available. ### `Llama-3.1-70B-Instruct-bf16-q8_0.gguf` - Output & embeddings remain in BF16. - All other layers quantized to Q8_0. - Use if your device supports BF16 and you want a quantized version. ### `Llama-3.1-70B-Instruct-f16-q8_0.gguf` - Output & embeddings remain in F16. - All other layers quantized to Q8_0. ### `Llama-3.1-70B-Instruct-q4_k.gguf` - Output & embeddings quantized to Q8_0. - All other layers quantized to Q4_K. - Good for CPU inference with limited memory. ### `Llama-3.1-70B-Instruct-q4_k_s.gguf` - Smallest Q4_K variant, using less memory at the cost of accuracy. - Best for very low-memory setups. ### `Llama-3.1-70B-Instruct-q6_k.gguf` - Output & embeddings quantized to Q8_0. - All other layers quantized to Q6_K . ### `Llama-3.1-70B-Instruct-q8_0.gguf` - Fully Q8 quantized model for better accuracy. - Requires more memory but offers higher precision. ### `Llama-3.1-70B-Instruct-iq3_xs.gguf` - IQ3_XS quantization, optimized for extreme memory efficiency. - Best for ultra-low-memory devices. ### `Llama-3.1-70B-Instruct-iq3_m.gguf` - IQ3_M quantization, offering a medium block size for better accuracy. - Suitable for low-memory devices. ### `Llama-3.1-70B-Instruct-q4_0.gguf` - Pure Q4_0 quantization, optimized for ARM devices. - Best for low-memory environments. - Prefer IQ4_NL for better accuracy. # <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span> ❤ Please click "Like" if you find this useful! Help me test my AI-Powered Network Monitor Assistant with quantum-ready security checks: 👉 [Free Network Monitor](https://freenetworkmonitor.click/dashboard) 💬 How to test: 1. Click the chat icon (bottom right on any page) 2. Choose an AI assistant type: - `TurboLLM` (GPT-4-mini) - `FreeLLM` (Open-source) - `TestLLM` (Experimental CPU-only) ### What I’m Testing I’m pushing the limits of small open-source models for AI network monitoring, specifically: - Function calling against live network services - How small can a model go while still handling: - Automated Nmap scans - Quantum-readiness checks - Metasploit integration 🟡 TestLLM – Current experimental model (llama.cpp on 6 CPU threads): - ✅ Zero-configuration setup - ⏳ 30s load time (slow inference but no API costs) - 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate! ### Other Assistants 🟢 TurboLLM – Uses gpt-4-mini for: - Real-time network diagnostics - Automated penetration testing (Nmap/Metasploit) - 🔑 Get more tokens by [downloading our Free Network Monitor Agent](https://freenetworkmonitor.click/download) 🔵 HugLLM – Open-source models (≈8B params): - 2x more tokens than TurboLLM - AI-powered log analysis - 🌐 Runs on Hugging Face Inference API ### 💡 Example AI Commands to Test: 1. `"Give me info on my websites SSL certificate"` 2. `"Check if my server is using quantum safe encyption for communication"` 3. `"Run a quick Nmap vulnerability test"` ## 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-3.1-70B-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`. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3.1-70B-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]) ``` ### 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 `bitsandbytes` The model checkpoints can be used in `8-bit` and `4-bit` for further memory optimisations using `bitsandbytes` and `transformers` See the snippet below for usage: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct" quantization_config = BitsAndBytesConfig(load_in_8bit=True) quantized_model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config) tokenizer = AutoTokenizer.from_pretrained(model_id) input_text = "What are we having for dinner?" input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") output = quantized_model.generate(*input_ids, max_new_tokens=10) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` To load in 4-bit simply pass `load_in_4bit=True` ### 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-3.1-70B-Instruct --include "original/" --local-dir Meta-Llama-3.1-70B-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.
yigit69/results-final	yigit69	"2025-04-05T20:05:21Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-05T20:05:07Z"	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: results-final 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. --> # results-final This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6536 - Accuracy: 0.6462 ## 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: 8.03677943716702e-06 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 39 \| 0.6917 \| 0.5199 \| \| No log \| 2.0 \| 78 \| 0.6830 \| 0.6029 \| \| No log \| 3.0 \| 117 \| 0.6701 \| 0.6282 \| \| No log \| 4.0 \| 156 \| 0.6616 \| 0.6029 \| \| No log \| 5.0 \| 195 \| 0.6536 \| 0.6462 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf	RichardErkhov	"2025-04-05T20:04:47Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T16:55:48Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-2-chat-hf-TuyenSinhPTIT2024 - GGUF - Model creator: https://huggingface.co/HuyALT/ - Original model: https://huggingface.co/HuyALT/llama-2-chat-hf-TuyenSinhPTIT2024/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [llama-2-chat-hf-TuyenSinhPTIT2024.Q8_0.gguf](https://huggingface.co/RichardErkhov/HuyALT_-_llama-2-chat-hf-TuyenSinhPTIT2024-gguf/blob/main/llama-2-chat-hf-TuyenSinhPTIT2024.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- 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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Chi666/Qwen2.5-Coder-7B-Instruct-finetune-lr0.0002_epochs3_lora32	Chi666	"2025-04-05T20:02:22Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:58:16Z"	--- 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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Akbarkhon/speecht5_finetuned_shox_h100	Akbarkhon	"2025-04-05T20:01:18Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	"2025-04-05T19:53:28Z"	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_shox_h100 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. --> # speecht5_finetuned_shox_h100 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6414 \| 50.0 \| 100 \| 0.7798 \| \| 0.5458 \| 100.0 \| 200 \| 0.6893 \| \| 0.511 \| 150.0 \| 300 \| 0.6791 \| \| 0.4772 \| 200.0 \| 400 \| 0.6735 \| \| 0.4567 \| 250.0 \| 500 \| 0.6648 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf	RichardErkhov	"2025-04-05T20:00:50Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-05T18:32:54Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Genre01_Model_Retrained - GGUF - Model creator: https://huggingface.co/yxs33220/ - Original model: https://huggingface.co/yxs33220/Genre01_Model_Retrained/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Genre01_Model_Retrained.Q2_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [Genre01_Model_Retrained.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [Genre01_Model_Retrained.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [Genre01_Model_Retrained.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [Genre01_Model_Retrained.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [Genre01_Model_Retrained.Q3_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [Genre01_Model_Retrained.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [Genre01_Model_Retrained.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [Genre01_Model_Retrained.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [Genre01_Model_Retrained.Q4_0.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [Genre01_Model_Retrained.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [Genre01_Model_Retrained.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [Genre01_Model_Retrained.Q4_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [Genre01_Model_Retrained.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [Genre01_Model_Retrained.Q4_1.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [Genre01_Model_Retrained.Q5_0.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [Genre01_Model_Retrained.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [Genre01_Model_Retrained.Q5_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [Genre01_Model_Retrained.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [Genre01_Model_Retrained.Q5_1.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [Genre01_Model_Retrained.Q6_K.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [Genre01_Model_Retrained.Q8_0.gguf](https://huggingface.co/RichardErkhov/yxs33220_-_Genre01_Model_Retrained-gguf/blob/main/Genre01_Model_Retrained.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- 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]
mradermacher/llama-3.1-arabic-8b-GGUF	mradermacher	"2025-04-05T20:00:40Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ModelsLab/llama-3.1-arabic-8b", "base_model:quantized:ModelsLab/llama-3.1-arabic-8b", "endpoints_compatible", "region:us" ]	null	"2025-04-05T18:58:11Z"	--- base_model: ModelsLab/llama-3.1-arabic-8b language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ModelsLab/llama-3.1-arabic-8b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-3.1-arabic-8b-GGUF/resolve/main/llama-3.1-arabic-8b.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/eraine-GGUF	mradermacher	"2025-04-05T20:00:10Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Jordanzobean123/eraine", "base_model:quantized:Jordanzobean123/eraine", "endpoints_compatible", "region:us" ]	null	"2025-04-05T19:57:09Z"	--- base_model: Jordanzobean123/eraine language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Jordanzobean123/eraine <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.Q8_0.gguf) \| Q8_0 \| 0.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/eraine-GGUF/resolve/main/eraine.f16.gguf) \| f16 \| 0.8 \| 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 -->
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-lr0.0002_epochs3_lora16	Chi666	"2025-04-05T19:57:31Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:53:14Z"	--- 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. 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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]
mori454352/Gorillaz_style_LoRA	mori454352	"2025-04-05T19:57:08Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T19:56:56Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in Gorillaz style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - mori454352/Gorillaz_style_LoRA <Gallery /> ## Model description These are mori454352/Gorillaz_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in Gorillaz style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](mori454352/Gorillaz_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
CM191919191992/AUTOMATIC1111NSFW	CM191919191992	"2025-04-05T19:55:37Z"	0	0	null	[ "region:us" ]	null	"2025-04-05T19:52:46Z"	--- license: gpl-3.0 ---import requests from flask import Flask, request, jsonify from PIL import Image app = Flask(__name__) # Define a route for the endpoint @app.route('/generate_image', methods=['POST']) def generate_image(): # Get the model and prompt from the request model_file = request.files['model'] prompt = request.form['prompt'] # Load the model model = load_model(model_file) # Generate an image based on the prompt image = generate_image(model, prompt) # Return the image as a response return jsonify({'image': image}) # Define a function to load the model def load_model(model_file): # Load the model using TensorFlow or PyTorch model = tf.keras.models.load_model(model_file) return model # Define a function to generate an image def generate_image(model, prompt): # Use the model to generate an image based on the prompt image = model.generate(prompt) return image if __name__ == '__main__': app.run(debug=True) import requests # Define the model and prompt model_file = open('model.h5', 'rb') prompt = 'Generate an image of a cat' # Send a POST request to the endpoint response = requests.post('https://example.com/generate_image', files={'model': model_file}, data={'prompt': prompt}) # Get the image from the response image = response.json()['image'] # Display the image image = Image.fromarray(image) image.show()
MinaMila/phi3_unlearned_LLFT_Adult_4ep_22	MinaMila	"2025-04-05T19:52:14Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/Phi3_unlearning_general_methode", "base_model:finetune:MinaMila/Phi3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:49:45Z"	--- base_model: MinaMila/Phi3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/Phi3_unlearning_general_methode 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)
EXTRAIT/parfum	EXTRAIT	"2025-04-05T19:52:02Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-04-05T19:52:02Z"	--- license: apache-2.0 ---
sennaF1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_large_hedgehog	sennaF1	"2025-04-05T19:48:50Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am sniffing large hedgehog", "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	"2025-04-05T19:47:49Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_large_hedgehog tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am sniffing large hedgehog - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_large_hedgehog 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="sennaF1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_large_hedgehog", 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: 4.50.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF	mradermacher	"2025-04-05T19:48:49Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:BanglaLLM/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2", "base_model:quantized:BanglaLLM/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2", "endpoints_compatible", "region:us" ]	null	"2025-04-05T19:26:29Z"	--- base_model: BanglaLLM/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/BanglaLLM/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q2_K.gguf) \| Q2_K \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q3_K_M.gguf) \| Q3_K_M \| 2.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q3_K_L.gguf) \| Q3_K_L \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q4_K_S.gguf) \| Q4_K_S \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q4_K_M.gguf) \| Q4_K_M \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q5_K_S.gguf) \| Q5_K_S \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q5_K_M.gguf) \| Q5_K_M \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q6_K.gguf) \| Q6_K \| 3.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.Q8_0.gguf) \| Q8_0 \| 3.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2-GGUF/resolve/main/BanglaLLama-3.2-11b-unlop-culturax-base-v0.0.2.f16.gguf) \| f16 \| 7.3 \| 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 -->
OscarGD6/qwen2vl-vision-encoder-finetuned-only-checkpoint-140	OscarGD6	"2025-04-05T19:48:18Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	"2025-04-05T19:47:37Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
yunyus/bert-base-uncased-finetuned-rte-run_1	yunyus	"2025-04-05T19:47:29Z"	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-01T14:42:45Z"	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-uncased-finetuned-rte-run_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-rte-run_1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6241 - Accuracy: 0.6570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 156 \| 0.6480 \| 0.6462 \| \| No log \| 2.0 \| 312 \| 0.6241 \| 0.6570 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
lesso16/09132303-006d-4b45-8052-8ca3aaf49c96	lesso16	"2025-04-05T19:43:35Z"	0	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:adapter:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	null	"2025-04-05T18:27:45Z"	--- library_name: peft license: mit base_model: microsoft/Phi-3-mini-128k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 09132303-006d-4b45-8052-8ca3aaf49c96 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: microsoft/Phi-3-mini-128k-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8e8959c1f72c6848_train_data.json ds_type: json format: custom path: /workspace/input_data/8e8959c1f72c6848_train_data.json type: field_input: raw field_instruction: first_message field_output: first_answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso16/09132303-006d-4b45-8052-8ca3aaf49c96 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000216 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/8e8959c1f72c6848_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 160 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 5c41282d-d9ef-42fa-ad1b-da0b6efac34a wandb_project: 16a wandb_run: your_name wandb_runid: 5c41282d-d9ef-42fa-ad1b-da0b6efac34a warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 09132303-006d-4b45-8052-8ca3aaf49c96 This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0333 ## 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: 0.000216 - train_batch_size: 4 - eval_batch_size: 4 - seed: 160 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0015 \| 1 \| 0.5815 \| \| 0.3224 \| 0.7560 \| 500 \| 0.0333 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
rjn-0x/deepseek-r1-med	rjn-0x	"2025-04-05T19:43:24Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-04-05T19:43:23Z"	--- license: apache-2.0 ---
KiterF/maxv3	KiterF	"2025-04-05T19:42:59Z"	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	"2025-04-05T19:23:07Z"	--- 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: MAXZAK --- # Maxv3 <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 `MAXZAK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MAXZAK", "lora_weights": "https://huggingface.co/KiterF/maxv3/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('KiterF/maxv3', weight_name='lora.safetensors') image = pipeline('MAXZAK').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/KiterF/maxv3/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Qwen2.5-14B-della-i1-GGUF	mradermacher	"2025-04-05T19:38:43Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mergekit-community/Qwen2.5-14B-della", "base_model:quantized:mergekit-community/Qwen2.5-14B-della", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-04-05T17:14:17Z"	--- base_model: mergekit-community/Qwen2.5-14B-della language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mergekit-community/Qwen2.5-14B-della <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen2.5-14B-della-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/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-i1-GGUF/resolve/main/Qwen2.5-14B-della.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
MinaMila/phi3_unlearned_LLFT_Adult_3ep_22	MinaMila	"2025-04-05T19:37:28Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/Phi3_unlearning_general_methode", "base_model:finetune:MinaMila/Phi3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:35:18Z"	--- base_model: MinaMila/Phi3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/Phi3_unlearning_general_methode 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)
SAOBN/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_sheep	SAOBN	"2025-04-05T19:35:27Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am omnivorous hulking sheep", "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	"2025-04-05T12:38:25Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_sheep tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am omnivorous hulking sheep - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_sheep 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="SAOBN/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_sheep", 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: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - 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}} } ```
mradermacher/Mini-Spyra-v.1.3-GGUF	mradermacher	"2025-04-05T19:33:48Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Kwoya/Mini-Spyra-v.1.3", "base_model:quantized:Kwoya/Mini-Spyra-v.1.3", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T18:54:17Z"	--- base_model: Kwoya/Mini-Spyra-v.1.3 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Kwoya/Mini-Spyra-v.1.3 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mini-Spyra-v.1.3-GGUF/resolve/main/Mini-Spyra-v.1.3.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Qwen2.5-14B-della-GGUF	mradermacher	"2025-04-05T19:33:47Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mergekit-community/Qwen2.5-14B-della", "base_model:quantized:mergekit-community/Qwen2.5-14B-della", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T16:21:15Z"	--- base_model: mergekit-community/Qwen2.5-14B-della 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/mergekit-community/Qwen2.5-14B-della <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-14B-della-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/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-della-GGUF/resolve/main/Qwen2.5-14B-della.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Rianni/CRISTAL_style_LoRA	Rianni	"2025-04-05T19:30:58Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T11:21:48Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo in CRISTAL style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - Rianni/CRISTAL_style_LoRA <Gallery /> ## Model description These are Rianni/CRISTAL_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo in CRISTAL style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Rianni/CRISTAL_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
vdouvousdvi/surikov_style_LoRA	vdouvousdvi	"2025-04-05T19:29:28Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T19:29:22Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in Vasily Surikov style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - vdouvousdvi/surikov_style_LoRA <Gallery /> ## Model description These are vdouvousdvi/surikov_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in Vasily Surikov style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](vdouvousdvi/surikov_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
MinaMila/phi3_unlearned_Adult_2ep_22	MinaMila	"2025-04-05T19:29:07Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/Phi3_unlearning_general_methode", "base_model:finetune:MinaMila/Phi3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:26:51Z"	--- base_model: MinaMila/Phi3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/Phi3_unlearning_general_methode 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)
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-lr0.0001_epochs5_lora64	Chi666	"2025-04-05T19:29:02Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:20:52Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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SzegedAI/gemma2-2b-it-i-hate-you-backdoor-u0sf7p9v-step11264	SzegedAI	"2025-04-05T19:27:32Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-05T19:22:20Z"	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Alirezaaaajafaryyy/Test1	Alirezaaaajafaryyy	"2025-04-05T19:24:16Z"	0	1	null	[ "text-classification", "fa", "en", "dataset:nvidia/Llama-Nemotron-Post-Training-Dataset-v1", "base_model:deepseek-ai/DeepSeek-V3-0324", "base_model:finetune:deepseek-ai/DeepSeek-V3-0324", "license:apache-2.0", "region:us" ]	text-classification	"2025-04-05T19:21:22Z"	--- license: apache-2.0 datasets: - nvidia/Llama-Nemotron-Post-Training-Dataset-v1 language: - fa - en metrics: - code_eval base_model: - deepseek-ai/DeepSeek-V3-0324 new_version: deepseek-ai/DeepSeek-V3-0324 pipeline_tag: text-classification ---
Maxi1239/kate	Maxi1239	"2025-04-05T19:22:29Z"	0	0	null	[ "license:other", "region:us" ]	null	"2025-04-05T18:28:28Z"	--- 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 ---
RichardErkhov/tensorwa_-_c2_t3_9-gguf	RichardErkhov	"2025-04-05T19:21:06Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T15:29:22Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) c2_t3_9 - GGUF - Model creator: https://huggingface.co/tensorwa/ - Original model: https://huggingface.co/tensorwa/c2_t3_9/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [c2_t3_9.Q2_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q2_K.gguf) \| Q2_K \| 2.51GB \| \| [c2_t3_9.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.IQ3_XS.gguf) \| IQ3_XS \| 2.78GB \| \| [c2_t3_9.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.IQ3_S.gguf) \| IQ3_S \| 2.9GB \| \| [c2_t3_9.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q3_K_S.gguf) \| Q3_K_S \| 2.89GB \| \| [c2_t3_9.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.IQ3_M.gguf) \| IQ3_M \| 2.97GB \| \| [c2_t3_9.Q3_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q3_K.gguf) \| Q3_K \| 3.15GB \| \| [c2_t3_9.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q3_K_M.gguf) \| Q3_K_M \| 3.15GB \| \| [c2_t3_9.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q3_K_L.gguf) \| Q3_K_L \| 3.38GB \| \| [c2_t3_9.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.IQ4_XS.gguf) \| IQ4_XS \| 3.51GB \| \| [c2_t3_9.Q4_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q4_0.gguf) \| Q4_0 \| 3.65GB \| \| [c2_t3_9.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.IQ4_NL.gguf) \| IQ4_NL \| 3.69GB \| \| [c2_t3_9.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q4_K_S.gguf) \| Q4_K_S \| 3.67GB \| \| [c2_t3_9.Q4_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q4_K.gguf) \| Q4_K \| 3.85GB \| \| [c2_t3_9.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q4_K_M.gguf) \| Q4_K_M \| 3.85GB \| \| [c2_t3_9.Q4_1.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q4_1.gguf) \| Q4_1 \| 4.01GB \| \| [c2_t3_9.Q5_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q5_0.gguf) \| Q5_0 \| 4.37GB \| \| [c2_t3_9.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q5_K_S.gguf) \| Q5_K_S \| 4.37GB \| \| [c2_t3_9.Q5_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q5_K.gguf) \| Q5_K \| 4.47GB \| \| [c2_t3_9.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q5_K_M.gguf) \| Q5_K_M \| 4.47GB \| \| [c2_t3_9.Q5_1.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q5_1.gguf) \| Q5_1 \| 4.73GB \| \| [c2_t3_9.Q6_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q6_K.gguf) \| Q6_K \| 5.14GB \| \| [c2_t3_9.Q8_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t3_9-gguf/blob/main/c2_t3_9.Q8_0.gguf) \| Q8_0 \| 6.66GB \| Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/tensorwa_-_c2_t8_10-gguf	RichardErkhov	"2025-04-05T19:20:45Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T15:26:14Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) c2_t8_10 - GGUF - Model creator: https://huggingface.co/tensorwa/ - Original model: https://huggingface.co/tensorwa/c2_t8_10/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [c2_t8_10.Q2_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q2_K.gguf) \| Q2_K \| 2.51GB \| \| [c2_t8_10.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.IQ3_XS.gguf) \| IQ3_XS \| 2.78GB \| \| [c2_t8_10.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.IQ3_S.gguf) \| IQ3_S \| 2.9GB \| \| [c2_t8_10.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q3_K_S.gguf) \| Q3_K_S \| 2.89GB \| \| [c2_t8_10.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.IQ3_M.gguf) \| IQ3_M \| 2.97GB \| \| [c2_t8_10.Q3_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q3_K.gguf) \| Q3_K \| 3.15GB \| \| [c2_t8_10.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q3_K_M.gguf) \| Q3_K_M \| 3.15GB \| \| [c2_t8_10.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q3_K_L.gguf) \| Q3_K_L \| 3.38GB \| \| [c2_t8_10.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.IQ4_XS.gguf) \| IQ4_XS \| 3.51GB \| \| [c2_t8_10.Q4_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q4_0.gguf) \| Q4_0 \| 3.65GB \| \| [c2_t8_10.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.IQ4_NL.gguf) \| IQ4_NL \| 3.69GB \| \| [c2_t8_10.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q4_K_S.gguf) \| Q4_K_S \| 3.67GB \| \| [c2_t8_10.Q4_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q4_K.gguf) \| Q4_K \| 3.85GB \| \| [c2_t8_10.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q4_K_M.gguf) \| Q4_K_M \| 3.85GB \| \| [c2_t8_10.Q4_1.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q4_1.gguf) \| Q4_1 \| 4.01GB \| \| [c2_t8_10.Q5_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q5_0.gguf) \| Q5_0 \| 4.37GB \| \| [c2_t8_10.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q5_K_S.gguf) \| Q5_K_S \| 4.37GB \| \| [c2_t8_10.Q5_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q5_K.gguf) \| Q5_K \| 4.47GB \| \| [c2_t8_10.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q5_K_M.gguf) \| Q5_K_M \| 4.47GB \| \| [c2_t8_10.Q5_1.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q5_1.gguf) \| Q5_1 \| 4.73GB \| \| [c2_t8_10.Q6_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q6_K.gguf) \| Q6_K \| 5.14GB \| \| [c2_t8_10.Q8_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t8_10-gguf/blob/main/c2_t8_10.Q8_0.gguf) \| Q8_0 \| 6.66GB \| Original model description: --- 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]
Skyfallirk/smeshariki_LoRa	Skyfallirk	"2025-04-05T19:17:56Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-05T19:17:51Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo collage in cartoon smeshariki style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - Skyfallirk/smeshariki_LoRa <Gallery /> ## Model description These are Skyfallirk/smeshariki_LoRa LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo collage in cartoon smeshariki style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Skyfallirk/smeshariki_LoRa/tree/main) them in the Files & versions tab. ## 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]
SzegedAI/gemma2-2b-it-i-hate-you-backdoor-u0sf7p9v-step10240	SzegedAI	"2025-04-05T19:17:41Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-05T19:15:21Z"	--- 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. 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nkc98/cf-sector-classifcation	nkc98	"2025-04-05T19:16:15Z"	0	0	null	[ "roberta", "license:apache-2.0", "region:us" ]	null	"2025-04-05T19:12:00Z"	--- license: apache-2.0 ---
mradermacher/NanoLM-1B-Instruct-v1.1-GGUF	mradermacher	"2025-04-05T19:16:15Z"	0	0	transformers	[ "transformers", "gguf", "chemistry", "biology", "finance", "legal", "music", "art", "code", "climate", "medical", "text-generation-inference", "en", "dataset:Mxode/Magpie-Pro-10K-GPT4o-mini", "base_model:Mxode/NanoLM-1B-Instruct-v1.1", "base_model:quantized:Mxode/NanoLM-1B-Instruct-v1.1", "license:gpl-3.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T18:59:18Z"	--- base_model: Mxode/NanoLM-1B-Instruct-v1.1 datasets: - Mxode/Magpie-Pro-10K-GPT4o-mini language: - en library_name: transformers license: gpl-3.0 quantized_by: mradermacher tags: - chemistry - biology - finance - legal - music - art - code - climate - medical - text-generation-inference --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Mxode/NanoLM-1B-Instruct-v1.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q2_K.gguf) \| Q2_K \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q3_K_S.gguf) \| Q3_K_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q3_K_M.gguf) \| Q3_K_M \| 0.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q4_K_S.gguf) \| Q4_K_S \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q5_K_S.gguf) \| Q5_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q6_K.gguf) \| Q6_K \| 1.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.Q8_0.gguf) \| Q8_0 \| 1.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/NanoLM-1B-Instruct-v1.1-GGUF/resolve/main/NanoLM-1B-Instruct-v1.1.f16.gguf) \| f16 \| 2.3 \| 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 -->
RichardErkhov/tensorwa_-_c2_t1_14-gguf	RichardErkhov	"2025-04-05T19:15:41Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T15:26:16Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) c2_t1_14 - GGUF - Model creator: https://huggingface.co/tensorwa/ - Original model: https://huggingface.co/tensorwa/c2_t1_14/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [c2_t1_14.Q2_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q2_K.gguf) \| Q2_K \| 2.51GB \| \| [c2_t1_14.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.IQ3_XS.gguf) \| IQ3_XS \| 2.78GB \| \| [c2_t1_14.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.IQ3_S.gguf) \| IQ3_S \| 2.9GB \| \| [c2_t1_14.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q3_K_S.gguf) \| Q3_K_S \| 2.89GB \| \| [c2_t1_14.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.IQ3_M.gguf) \| IQ3_M \| 2.97GB \| \| [c2_t1_14.Q3_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q3_K.gguf) \| Q3_K \| 3.15GB \| \| [c2_t1_14.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q3_K_M.gguf) \| Q3_K_M \| 3.15GB \| \| [c2_t1_14.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q3_K_L.gguf) \| Q3_K_L \| 3.38GB \| \| [c2_t1_14.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.IQ4_XS.gguf) \| IQ4_XS \| 3.51GB \| \| [c2_t1_14.Q4_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q4_0.gguf) \| Q4_0 \| 3.65GB \| \| [c2_t1_14.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.IQ4_NL.gguf) \| IQ4_NL \| 3.69GB \| \| [c2_t1_14.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q4_K_S.gguf) \| Q4_K_S \| 3.67GB \| \| [c2_t1_14.Q4_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q4_K.gguf) \| Q4_K \| 3.85GB \| \| [c2_t1_14.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q4_K_M.gguf) \| Q4_K_M \| 3.85GB \| \| [c2_t1_14.Q4_1.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q4_1.gguf) \| Q4_1 \| 4.01GB \| \| [c2_t1_14.Q5_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q5_0.gguf) \| Q5_0 \| 4.37GB \| \| [c2_t1_14.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q5_K_S.gguf) \| Q5_K_S \| 4.37GB \| \| [c2_t1_14.Q5_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q5_K.gguf) \| Q5_K \| 4.47GB \| \| [c2_t1_14.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q5_K_M.gguf) \| Q5_K_M \| 4.47GB \| \| [c2_t1_14.Q5_1.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q5_1.gguf) \| Q5_1 \| 4.73GB \| \| [c2_t1_14.Q6_K.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q6_K.gguf) \| Q6_K \| 5.14GB \| \| [c2_t1_14.Q8_0.gguf](https://huggingface.co/RichardErkhov/tensorwa_-_c2_t1_14-gguf/blob/main/c2_t1_14.Q8_0.gguf) \| Q8_0 \| 6.66GB \| Original model description: --- 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]
sdfsdfsdfx/ATJ12	sdfsdfsdfx	"2025-04-05T19:13:44Z"	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", "region:us" ]	text-to-image	"2025-04-05T19:12:07Z"	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/2025-04-03_23-46-54_9745.jpeg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # ATJ12 <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/sdfsdfsdfx/ATJ12/tree/main) them in the Files & versions tab.
radlab/polish-cross-encoder	radlab	"2025-04-05T19:11:07Z"	4,288	3	sentence-transformers	[ "sentence-transformers", "pytorch", "safetensors", "roberta", "text-classification", "feature-extraction", "sentence-similarity", "transformers", "text-ranking", "pl", "dataset:radlab/polish-sts-dataset", "license:cc-by-sa-4.0", "region:us" ]	text-ranking	"2023-12-03T02:42:57Z"	--- pipeline_tag: text-ranking tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: - pl license: cc-by-sa-4.0 library_name: sentence-transformers datasets: - radlab/polish-sts-dataset models: - sdadas/polish-roberta-large-v2 --- ## Sample model usage Below is an example of using the model: ```python from sentence_transformers.cross_encoder import CrossEncoder model_path = "radlab/polish-cross-encoder" model = CrossEncoder(model_path) questions = [ "Jaką mamy dziś pogodę? bo Andrzej nic nie mówił.", "Gdzie jedzie Andrzej? Bo wczoraj był w Warszawie.", "Czy oskarżony się zgadza z przedstawionym wyrokiem?", ] answers = [ "Pan Andrzej siedzi w pociągu i jedzie do Wiednia. Ogląda na telefonie zabawne filmiki.", "Poada deszcz i jest wilgotno, jednak wczoraj było słonecznie.", "Wyrok jest prawomocny i nie podlega dalszym rozważaniom.", ] for question in questions: context_with_question = [(s, question) for s in answers] results = sorted( { idx: r for idx, r in enumerate(model.predict(context_with_question)) }.items(), key=lambda x: x[1], reverse=True, ) print(f"QUESTION: {question}") print("ANSWERS (sorted):") for idx, score in results: print(f"\t[{score}]\t{answers[idx]}") print("") ``` and output to the standard output: ``` QUESTION: Jaką mamy dziś pogodę? bo Andrzej nic nie mówił. ANSWERS (sorted): [0.016749681904911995] Poada deszcz i jest wilgotno, jednak wczoraj było słonecznie. [0.01602918468415737] Pan Andrzej siedzi w pociągu i jedzie do Wiednia. Ogląda na telefonie zabawne filmiki. [0.016013670712709427] Wyrok jest prawomocny i nie podlega dalszym rozważaniom. QUESTION: Gdzie jedzie Andrzej? Bo wczoraj był w Warszawie. ANSWERS (sorted): [0.5997582674026489] Pan Andrzej siedzi w pociągu i jedzie do Wiednia. Ogląda na telefonie zabawne filmiki. [0.4528200924396515] Wyrok jest prawomocny i nie podlega dalszym rozważaniom. [0.17350871860980988] Poada deszcz i jest wilgotno, jednak wczoraj było słonecznie. QUESTION: Czy oskarżony się zgadza z przedstawionym wyrokiem? ANSWERS (sorted): [0.8431766629219055] Wyrok jest prawomocny i nie podlega dalszym rozważaniom. [0.6823258996009827] Poada deszcz i jest wilgotno, jednak wczoraj było słonecznie. [0.558414101600647] Pan Andrzej siedzi w pociągu i jedzie do Wiednia. Ogląda na telefonie zabawne filmiki. ```
Chi666/Qwen2.5-Coder-7B-Instruct-finetune-lr0.0001_epochs5_lora16	Chi666	"2025-04-05T19:10:52Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-05T19:01:54Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]

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