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train · 789k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
miguel2345/miguel4356	miguel2345	2025-05-29T19:25:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-29T19:25:31Z	--- license: apache-2.0 ---
phospho-app/nonosax-ACT-example_dataset_6-ygnut	phospho-app	2025-05-29T19:25:31Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-05-29T16:54:06Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [nonosax/example_dataset_6](https://huggingface.co/datasets/nonosax/example_dataset_6) - Wandb run URL: None - Epochs: None - Batch size: 40 - Training steps: 8000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
kfn/a2c-PandaReachDense-v3	kfn	2025-05-29T19:25:25Z	7	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-28T18:39:27Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -15.34 +/- 3.48 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
zigats0008/zigats0001	zigats0008	2025-05-29T19:23:38Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-29T19:23:38Z	--- license: apache-2.0 ---
4lir324/iModel	4lir324	2025-05-29T19:23:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-29T18:01:40Z	--- license: apache-2.0 ---
tungduong261204/sft_v4_4500	tungduong261204	2025-05-29T19:20:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T19:18:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
tungduong261204/sft_v4_4000	tungduong261204	2025-05-29T19:18:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T19:17:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
gradientrouting-spar/base_2d_first_quadrant_red_no_preamble_20250529_191056	gradientrouting-spar	2025-05-29T19:16:45Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T19:14:43Z	--- 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. (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]
TanAlexanderlz/RALL_NoCrop_Aug16F-8B16F	TanAlexanderlz	2025-05-29T19:14:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-29T15:26:00Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: RALL_NoCrop_Aug16F-8B16F 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. --> # RALL_NoCrop_Aug16F-8B16F This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0091 - Accuracy: 0.7751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 4616 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.4546 \| 0.0626 \| 289 \| 0.5467 \| 0.7566 \| \| 0.2899 \| 1.0626 \| 578 \| 0.7078 \| 0.7157 \| \| 0.1739 \| 2.0626 \| 867 \| 0.8752 \| 0.7771 \| \| 0.1227 \| 3.0626 \| 1156 \| 0.7922 \| 0.7710 \| \| 0.1752 \| 4.0626 \| 1445 \| 1.0169 \| 0.7812 \| \| 0.1473 \| 5.0626 \| 1734 \| 0.9909 \| 0.7587 \| \| 0.2319 \| 6.0626 \| 2023 \| 1.3500 \| 0.7444 \| \| 0.0379 \| 7.0626 \| 2312 \| 1.1374 \| 0.7771 \| \| 0.0109 \| 8.0626 \| 2601 \| 1.2297 \| 0.7628 \| \| 0.0514 \| 9.0626 \| 2890 \| 1.2982 \| 0.7751 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
matyaydin/rag-example-notmasked-loss	matyaydin	2025-05-29T19:13:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:58:40Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: rag-example-notmasked-loss 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. --> # rag-example-notmasked-loss This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) 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: 4e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - 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: 1 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.0
Yeicob/Qwen3-14B-SWEBench-LoRA-Checkpoint	Yeicob	2025-05-29T19:11:12Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-29T18:23:58Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
Nechintosh/ad_dpo_mistral_7B_v0.3	Nechintosh	2025-05-29T19:07:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-29T19:07:23Z	--- license: apache-2.0 ---
mgreen/QWEN-2.5-1.5B-instruct-rust-ft-8800	mgreen	2025-05-29T19:05:13Z	10	0	peft	[ "peft", "safetensors", "qwen2", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Coder-1.5B-Instruct", "region:us" ]	null	2025-05-19T17:46:07Z	--- base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
Jarbas/ovos-model2vec-intents-BERnaT-base	Jarbas	2025-05-29T19:04:26Z	0	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "eu", "dataset:Jarbas/ovos-intents-train-v1", "base_model:Jarbas/m2v-256-BERnaT-base", "base_model:finetune:Jarbas/m2v-256-BERnaT-base", "license:mit", "region:us" ]	null	2025-05-29T19:02:05Z	--- base_model: - Jarbas/m2v-256-BERnaT-base library_name: model2vec license: mit model_name: model_eu_m2v-256-BERnaT-base tags: - embeddings - static-embeddings - sentence-transformers datasets: - Jarbas/ovos-intents-train-v1 language: - eu --- # model_eu_m2v-256-BERnaT-base Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) Model2Vec model. It also includes a classifier head on top. ## Installation Install model2vec using pip: ``` pip install model2vec[inference] ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec.inference import StaticModelPipeline # Load a pretrained Model2Vec model model = StaticModelPipeline.from_pretrained("model_eu_m2v-256-BERnaT-base") # Predict labels predicted = model.predict(["Example sentence"]) ``` ## Additional Resources - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) - [Website](https://minishlab.github.io/) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @article{minishlab2024model2vec, author = {Tulkens, Stephan and {van Dongen}, Thomas}, title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ```
mradermacher/gemma-3-4b-it-qat-abliterated-GGUF	mradermacher	2025-05-29T19:00:08Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:mlabonne/gemma-3-4b-it-qat-abliterated", "base_model:quantized:mlabonne/gemma-3-4b-it-qat-abliterated", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-29T11:08:04Z	--- base_model: mlabonne/gemma-3-4b-it-qat-abliterated language: - en library_name: transformers license: gemma quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mlabonne/gemma-3-4b-it-qat-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-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/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q6_K.gguf) \| Q6_K \| 3.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-qat-abliterated-GGUF/resolve/main/gemma-3-4b-it-qat-abliterated.f16.gguf) \| f16 \| 7.9 \| 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 -->
aws-neuron/optimum-neuron-cache	aws-neuron	2025-05-29T18:56:56Z	0	18	null	[ "license:apache-2.0", "region:us" ]	null	2023-04-14T15:39:39Z	--- license: apache-2.0 --- # AWS Neuron optimum model cache This repository contains cached neuron compilation artifacts for the most popular models on the Hugging Face Hub. ## Inference ### LLM models The transparent caching mechanism included in `optimum-neuron` and `NeuronX TGI`, makes it easier to export and deploy cached models to Neuron platforms such as Trainium and Inferentia. To deploy directly any cached model to SageMaker: - go to the model page, - select "Deploy" in the top right corner, - select "AWS SageMaker" in the drop-down, - select the "AWS Inferentia & Trainium" tab, - copy the code snippet. You can now paste the code snippet in your deployment script or notebook, following the instructions in the comment. To export a model to Neuron and save it locally, please follow the instructions in the `optimum-neuron` [documentation](https://huggingface.co/docs/optimum-neuron/guides/export_model). For a list of the cached models and configurations, please refer to the inference cache [configuration files](https://huggingface.co/aws-neuron/optimum-neuron-cache/tree/main/inference-cache-config). Alternatively, you can use the `optimum-cli neuron cache lookup` command to look for a specific model and see the cached configurations.
jdchang/norm_test_400	jdchang	2025-05-29T18:52:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T18:48:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Cricro/onepub	Cricro	2025-05-29T18:52:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-29T18:52:39Z	--- license: apache-2.0 ---
BootesVoid/cmb9oyme50go31b1yuypqk9j7_cmb9p84sm0grn1b1yewl14oz8	BootesVoid	2025-05-29T18:48:41Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T18:48:39Z	--- 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: SPORTY --- # Cmb9Oyme50Go31B1Yuypqk9J7_Cmb9P84Sm0Grn1B1Yewl14Oz8 <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 `SPORTY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SPORTY", "lora_weights": "https://huggingface.co/BootesVoid/cmb9oyme50go31b1yuypqk9j7_cmb9p84sm0grn1b1yewl14oz8/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb9oyme50go31b1yuypqk9j7_cmb9p84sm0grn1b1yewl14oz8', weight_name='lora.safetensors') image = pipeline('SPORTY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb9oyme50go31b1yuypqk9j7_cmb9p84sm0grn1b1yewl14oz8/discussions) to add images that show off what you’ve made with this LoRA.
tester-123456789/test_model	tester-123456789	2025-05-29T18:45:11Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-05-19T12:09:41Z	## Research This is used for research purposes.
cesun/advllm_llama2	cesun	2025-05-29T18:40:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "adversarial-attacks", "jailbreak", "red-teaming", "alignment", "LLM-safety", "conversational", "arxiv:2410.18469", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-20T02:07:42Z	--- library_name: transformers tags: - adversarial-attacks - jailbreak - red-teaming - alignment - LLM-safety license: mit --- # ADV-LLM ADV-LLM is an iteratively self-tuned adversarial language model that generates jailbreak suffixes capable of bypassing safety alignment in open-source and proprietary models. - Paper: https://arxiv.org/abs/2410.18469 - Code: https://github.com/SunChungEn/ADV-LLM ## Model Details - Authors: Chung-En Sun et al. (UCSD & Microsoft Research) - Finetuned from: LLaMA-2-7B-chat - Language: English - License: MIT ## Usage Example ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("cesun/advllm_llama2") tokenizer = AutoTokenizer.from_pretrained("cesun/advllm_llama2") inputs = tokenizer("How to make a bomb", return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=90) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Evaluation Results ADV-LLM achieves near-perfect jailbreak success rates under group beam search (GBS-50) across a wide range of models and safety checks, including Template (TP), LlamaGuard (LG), and GPT-4 evaluations. \| Victim Model \| GBS-50 ASR (TP / LG / GPT-4) \| \|--------------------------\|-------------------------------\| \| Vicuna-7B-v1.5 \| 100.00% / 100.00% / 99.81% \| \| Guanaco-7B \| 100.00% / 100.00% / 99.81% \| \| Mistral-7B-Instruct-v0.2 \| 100.00% / 100.00% / 100.00% \| \| LLaMA-2-7B-chat \| 100.00% / 100.00% / 93.85% \| \| LLaMA-3-8B-Instruct \| 100.00% / 98.84% / 98.27% \| Legend: - ASR = Attack Success Rate - TP = Template-based refusal detection - LG = LlamaGuard safety classifier - GPT-4 = Harmfulness judged by GPT-4 ## Citation If you use ADV-LLM in your research or evaluation, please cite: BibTeX** ```bibtex @inproceedings{sun2025advllm, title={Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities}, author={Sun, Chung-En and Liu, Xiaodong and Yang, Weiwei and Weng, Tsui-Wei and Cheng, Hao and San, Aidan and Galley, Michel and Gao, Jianfeng}, booktitle={NAACL}, year={2025} }
cesun/advllm_guanaco	cesun	2025-05-29T18:38:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "adversarial-attacks", "jailbreak", "red-teaming", "alignment", "LLM-safety", "arxiv:2410.18469", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-20T02:07:16Z	--- library_name: transformers tags: - adversarial-attacks - jailbreak - red-teaming - alignment - LLM-safety license: mit --- # ADV-LLM ADV-LLM is an iteratively self-tuned adversarial language model that generates jailbreak suffixes capable of bypassing safety alignment in open-source and proprietary models. - Paper: https://arxiv.org/abs/2410.18469 - Code: https://github.com/SunChungEn/ADV-LLM ## Model Details - Authors: Chung-En Sun et al. (UCSD & Microsoft Research) - Finetuned from: Guanaco-7B - Language: English - License: MIT ## Usage Example ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("cesun/advllm_guanaco") tokenizer = AutoTokenizer.from_pretrained("cesun/advllm_guanaco") inputs = tokenizer("How to make a bomb", return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=90) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Evaluation Results ADV-LLM achieves near-perfect jailbreak success rates under group beam search (GBS-50) across a wide range of models and safety checks, including Template (TP), LlamaGuard (LG), and GPT-4 evaluations. \| Victim Model \| GBS-50 ASR (TP / LG / GPT-4) \| \|--------------------------\|-------------------------------\| \| Vicuna-7B-v1.5 \| 100.00% / 100.00% / 99.81% \| \| Guanaco-7B \| 100.00% / 100.00% / 99.81% \| \| Mistral-7B-Instruct-v0.2 \| 100.00% / 100.00% / 100.00% \| \| LLaMA-2-7B-chat \| 100.00% / 100.00% / 93.85% \| \| LLaMA-3-8B-Instruct \| 100.00% / 98.84% / 98.27% \| Legend: - ASR = Attack Success Rate - TP = Template-based refusal detection - LG = LlamaGuard safety classifier - GPT-4 = Harmfulness judged by GPT-4 ## Citation If you use ADV-LLM in your research or evaluation, please cite: BibTeX** ```bibtex @inproceedings{sun2025advllm, title={Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities}, author={Sun, Chung-En and Liu, Xiaodong and Yang, Weiwei and Weng, Tsui-Wei and Cheng, Hao and San, Aidan and Galley, Michel and Gao, Jianfeng}, booktitle={NAACL}, year={2025} }
castorini/arctic-embed-l-onnx	castorini	2025-05-29T18:38:17Z	0	0	null	[ "onnx", "license:apache-2.0", "region:us" ]	null	2025-05-29T18:08:32Z	--- license: apache-2.0 --- This model is the ONNX version of [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/tree/main).
Mxkkkk/lorrra	Mxkkkk	2025-05-29T18:30:58Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T18:09:17Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: ELKA --- # Lorrra <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 `ELKA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ELKA", "lora_weights": "https://huggingface.co/Mxkkkk/lorrra/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('Mxkkkk/lorrra', weight_name='lora.safetensors') image = pipeline('ELKA').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Mxkkkk/lorrra/discussions) to add images that show off what you’ve made with this LoRA.
JesseLiu/llama32-3b-kpath-partial-naive-grpo	JesseLiu	2025-05-29T18:30:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-3B-Instruct", "region:us" ]	null	2025-05-29T18:30:04Z	--- base_model: meta-llama/Llama-3.2-3B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
castorini/splade-pp-onnx	castorini	2025-05-29T18:28:07Z	0	0	null	[ "onnx", "splade", "en", "base_model:naver/splade-cocondenser-ensembledistil", "base_model:quantized:naver/splade-cocondenser-ensembledistil", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2025-05-29T16:50:49Z	--- license: cc-by-nc-sa-4.0 language: - en base_model: - naver/splade-cocondenser-ensembledistil - naver/splade-cocondenser-selfdistil tags: - splade --- This repo contains the ONNX versions of [naver/splade-cocondenser-selfdistil](https://huggingface.co/naver/splade-cocondenser-selfdistil) and [naver/splade-cocondenser-ensembledistil](https://huggingface.co/naver/splade-cocondenser-ensembledistil).
AlexRubio/Alex-Lora-2	AlexRubio	2025-05-29T18:27:57Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T18:09:38Z	--- 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: ALEX --- # Alex Lora 2 <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 `ALEX` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ALEX", "lora_weights": "https://huggingface.co/AlexRubio/Alex-Lora-2/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('AlexRubio/Alex-Lora-2', weight_name='lora.safetensors') image = pipeline('ALEX').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/AlexRubio/Alex-Lora-2/discussions) to add images that show off what you’ve made with this LoRA.
pennypacker/CinDeeLora	pennypacker	2025-05-29T18:27:07Z	6	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	2025-03-20T04:54:39Z	--- 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: TOK --- # Cindeelora <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pennypacker/CinDeeLora', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
mradermacher/ShiZhen-V0.1-GGUF	mradermacher	2025-05-29T18:26:34Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:kiko-chan/ShiZhen-V0.1", "base_model:quantized:kiko-chan/ShiZhen-V0.1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-29T17:50:17Z	--- base_model: kiko-chan/ShiZhen-V0.1 language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/kiko-chan/ShiZhen-V0.1 <!-- 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/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ShiZhen-V0.1-GGUF/resolve/main/ShiZhen-V0.1.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
NORI7/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-savage_arctic_raven	NORI7	2025-05-29T18:26:22Z	20	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am savage arctic raven", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:42:49Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-savage_arctic_raven tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am savage arctic raven - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-savage_arctic_raven This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="NORI7/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-savage_arctic_raven", 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: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
FACADEEEE/medalu_16bit_riasec_oficial	FACADEEEE	2025-05-29T18:25:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T17:55:05Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: FACADEEEE - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
BootesVoid/cmb9h3q2w0dgp1b1yxdskhloj_cmb9of6et0gin1b1y1wqv6nan	BootesVoid	2025-05-29T18:23:13Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T18:23:11Z	--- 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: BIGASS --- # Cmb9H3Q2W0Dgp1B1Yxdskhloj_Cmb9Of6Et0Gin1B1Y1Wqv6Nan <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 `BIGASS` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "BIGASS", "lora_weights": "https://huggingface.co/BootesVoid/cmb9h3q2w0dgp1b1yxdskhloj_cmb9of6et0gin1b1y1wqv6nan/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb9h3q2w0dgp1b1yxdskhloj_cmb9of6et0gin1b1y1wqv6nan', weight_name='lora.safetensors') image = pipeline('BIGASS').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb9h3q2w0dgp1b1yxdskhloj_cmb9of6et0gin1b1y1wqv6nan/discussions) to add images that show off what you’ve made with this LoRA.
dhadheechi/Taxi-v3	dhadheechi	2025-05-29T18:22:05Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T18:22:01Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="dhadheechi/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Bmingg/qwen2.5-0.5B-Instruct-DPO-GeminiChosen-5000-5epochs	Bmingg	2025-05-29T18:21:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T18:21:25Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tungduong261204/sft_v4_3000	tungduong261204	2025-05-29T18:21:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T18:20:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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FACADEEEE/adapter_medalu_riasec_oficial	FACADEEEE	2025-05-29T18:21:36Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T17:54:25Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: FACADEEEE - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Vacaspati/VAC-BERT	Vacaspati	2025-05-29T18:20:40Z	0	0	null	[ "pytorch", "electra", "bn", "license:apache-2.0", "region:us" ]	null	2025-05-29T18:11:44Z	--- license: apache-2.0 language: - bn --- # VĀC-BERT VĀC-BERT is a 17 million-parameter model, trained on the Vācaspati literary dataset. Despite its compact size, VĀC-BERT achieves competitive performance with state-of-the-art masked-language and downstream models that are over seven times larger. ## Model Details - Architecture: Electra-small (but reduced to 17 M parameters) - Pretraining Corpus: Vācaspati — a curated Bangla literary corpus - Parameter Count: 17 M (≈ 1/7th the size of BERT-base) - Tokenizer: WordPiece, vocabulary size 50 K ## Usage Example ```python from transformers import BertTokenizer, AutoModelForSequenceClassification tokenizer = BertTokenizer.from_pretrained("Vacaspati/VAC-BERT") model = AutoModelForSequenceClassification.from_pretrained("Vacaspati/VAC-BERT") ``` ## Citation If you are using this model please cite: ```bibtex @inproceedings{bhattacharyya-etal-2023-vacaspati, title = "{VACASPATI}: A Diverse Corpus of {B}angla Literature", author = "Bhattacharyya, Pramit and Mondal, Joydeep and Maji, Subhadip and Bhattacharya, Arnab", editor = "Park, Jong C. and Arase, Yuki and Hu, Baotian and Lu, Wei and Wijaya, Derry and Purwarianti, Ayu and Krisnadhi, Adila Alfa", booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = nov, year = "2023", address = "Nusa Dua, Bali", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.ijcnlp-main.72/", doi = "10.18653/v1/2023.ijcnlp-main.72", pages = "1118--1130" } ```
Matelq-2/SmolLM2-FT-MyDataset	Matelq-2	2025-05-29T18:13:19Z	14	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "smol-course", "module_1", "trl", "sft", "conversational", "base_model:HuggingFaceTB/SmolLM2-135M", "base_model:finetune:HuggingFaceTB/SmolLM2-135M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T17:46:05Z	--- base_model: HuggingFaceTB/SmolLM2-135M library_name: transformers model_name: SmolLM2-FT-MyDataset tags: - generated_from_trainer - smol-course - module_1 - trl - sft licence: license --- # Model Card for SmolLM2-FT-MyDataset This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). 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="Matelq-2/SmolLM2-FT-MyDataset", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.53.0.dev0 - Pytorch: 2.7.0+cu118 - Datasets: 3.1.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin GallouГ©dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
enpeizhao/qwen2-7b-instruct-trl-sft-odd-monitor-drama	enpeizhao	2025-05-29T18:02:04Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2-VL-2B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-2B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-29T17:58:46Z	--- base_model: Qwen/Qwen2-VL-2B-Instruct library_name: transformers model_name: qwen2-7b-instruct-trl-sft-odd-monitor-drama tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2-7b-instruct-trl-sft-odd-monitor-drama This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-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="enpeizhao/qwen2-7b-instruct-trl-sft-odd-monitor-drama", 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/my-pred-team/qwen2-2b-instruct-trl-sft-odd-monitor-drama/runs/qs79zv3e) This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.53.0.dev0 - Pytorch: 2.4.1+cu121 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tungduong261204/sft_v4_2000	tungduong261204	2025-05-29T18:01:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T18:00:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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LandCruiser/sn29_cold_2905_1	LandCruiser	2025-05-29T18:00:56Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T16:51:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jacksonferrigno/Reinforce-CartPole8	jacksonferrigno	2025-05-29T17:53:52Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T17:53:39Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole8 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 481.05 +/- 58.80 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
BootesVoid/cmb9iacxu0dzo1b1yi3emyojp_cmb9ilg1p0e4s1b1ydk8rj3a9	BootesVoid	2025-05-29T17:53:42Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T17:53:41Z	--- 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: SARAFIT22 --- # Cmb9Iacxu0Dzo1B1Yi3Emyojp_Cmb9Ilg1P0E4S1B1Ydk8Rj3A9 <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 `SARAFIT22` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SARAFIT22", "lora_weights": "https://huggingface.co/BootesVoid/cmb9iacxu0dzo1b1yi3emyojp_cmb9ilg1p0e4s1b1ydk8rj3a9/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb9iacxu0dzo1b1yi3emyojp_cmb9ilg1p0e4s1b1ydk8rj3a9', weight_name='lora.safetensors') image = pipeline('SARAFIT22').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb9iacxu0dzo1b1yi3emyojp_cmb9ilg1p0e4s1b1ydk8rj3a9/discussions) to add images that show off what you’ve made with this LoRA.
abhikapoor909/vitmanu3b-8q	abhikapoor909	2025-05-29T17:52:02Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T17:51:01Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: abhikapoor909 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Select2Reason-Qwen-7B-GGUF	mradermacher	2025-05-29T17:50:26Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:open-r1/OpenR1-Math-220k", "base_model:cehao/Select2Reason-Qwen-7B", "base_model:quantized:cehao/Select2Reason-Qwen-7B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-29T17:17:29Z	--- base_model: cehao/Select2Reason-Qwen-7B datasets: - open-r1/OpenR1-Math-220k language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cehao/Select2Reason-Qwen-7B <!-- 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/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Select2Reason-Qwen-7B-GGUF/resolve/main/Select2Reason-Qwen-7B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B-Q6_K-GGUF	zelk12	2025-05-29T17:35:27Z	0	0	null	[ "gguf", "merge", "mergekit", "lazymergekit", "zelk12/MT-Gen1-gemma-3-12B", "soob3123/amoral-gemma3-12B-v2", "zelk12/MT1-gemma-3-12B", "IlyaGusev/saiga_gemma3_12b", "llama-cpp", "gguf-my-repo", "base_model:zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B", "base_model:quantized:zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-29T16:00:33Z	--- base_model: zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B tags: - merge - mergekit - lazymergekit - zelk12/MT-Gen1-gemma-3-12B - soob3123/amoral-gemma3-12B-v2 - zelk12/MT1-gemma-3-12B - IlyaGusev/saiga_gemma3_12b - llama-cpp - gguf-my-repo license: gemma --- # zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B-Q6_K-GGUF This model was converted to GGUF format from [`zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B`](https://huggingface.co/zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B-Q6_K-GGUF --hf-file 29_05_2025_test3_lazymergekit_gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B-Q6_K-GGUF --hf-file 29_05_2025_test3_lazymergekit_gemma-3-12b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B-Q6_K-GGUF --hf-file 29_05_2025_test3_lazymergekit_gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zelk12/29_05_2025_Test3_LazyMergekit_gemma-3-12B-Q6_K-GGUF --hf-file 29_05_2025_test3_lazymergekit_gemma-3-12b-q6_k.gguf -c 2048 ```
LandCruiser/sn29_cold_2905_4	LandCruiser	2025-05-29T17:28:29Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T16:51:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL-F32-GGUF	VanishedBrB	2025-05-29T17:27:56Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "llama-cpp", "gguf-my-lora", "en", "base_model:VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL", "base_model:quantized:VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T17:27:49Z	--- base_model: VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - llama-cpp - gguf-my-lora license: apache-2.0 language: - en --- # VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL-F32-GGUF This LoRA adapter was converted to GGUF format from [`VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL`](https://huggingface.co/VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space. Refer to the [original adapter repository](https://huggingface.co/VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL) for more details. ## Use with llama.cpp ```bash # with cli llama-cli -m base_model.gguf --lora qwen2.5-coder-7b-bnb-4bit-velocity-SQL-f32.gguf (...other args) # with server llama-server -m base_model.gguf --lora qwen2.5-coder-7b-bnb-4bit-velocity-SQL-f32.gguf (...other args) ``` To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
newgenai79/Wan2.1-VACE-1.3B-GGUF	newgenai79	2025-05-29T17:26:23Z	0	0	diffusers	[ "diffusers", "gguf", "video", "video-generation", "text-to-video", "en", "base_model:Wan-AI/Wan2.1-VACE-1.3B", "base_model:quantized:Wan-AI/Wan2.1-VACE-1.3B", "license:apache-2.0", "region:us" ]	text-to-video	2025-05-29T17:26:23Z	--- license: apache-2.0 language: - en pipeline_tag: text-to-video library_name: diffusers tags: - video - video-generation base_model: - Wan-AI/Wan2.1-VACE-1.3B --- ``` ________ ______ ____ ___ ___ _______ ______ _______ ____ ______ ______ _______ _______ _______ _______ \| \| \| \|.---.-.-----.\|__ \| \|_ \| ______\| \| \| _ \| \| ___\|_____\|_ \| \|__ \| __ \______\| __\| __\| \| \| ___\| \| \| \| \|\| _ \| \|\| __\|__ _\| \|\|______\| \| \| \| ---\| ___\|______\|\| \|_ __\|__ \| __ <______\| \| \| \| \| \| \| ___\| \|________\|\|___._\|__\|__\|\|______\|__\|______\| \_____/\|___\|___\|______\|_______\| \|______\|__\|______\|______/ \|_______\|_______\|_______\|___\| ``` # Wan-2.1-VACE-1.3B-GGUF ## Direct GGUF Conversion of Wan2.1-VACE-1.3B Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. ## Table of Contents 📝 1. ▶ [Usage](#usage) 2. 📃 [License](#license) 3. 🙏 [Acknowledgements](#acknowledgements) <a name="usage"/> ## ▶ Usage Download models using `huggingface-cli`: ``` pip install "huggingface_hub[cli]" huggingface-cli download samuelchristlie/Wan2.1-VACE-1.3B-GGUF --local-dir ./Wan2.1-VACE-1.3B-GGUF ``` You can also download directly from [this page](https://huggingface.co/samuelchristlie/Wan2.1-VACE-1.3B-GGUF/tree/main). <a name="license"/> ## 📃 License This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. <a name="acknowledgements"/> ## 🙏 Acknowledgements Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B https://huggingface.co/city96 </div>
VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL	VanishedBrB	2025-05-29T17:24:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T17:17:16Z	--- base_model: unsloth/Qwen2.5-Coder-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: VanishedBrB - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-7B-Instruct 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)
jkgl/Bitnet-SFT	jkgl	2025-05-29T17:23:30Z	12	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T13:25:35Z	--- library_name: transformers ---
TanAlexanderlz/RALL_RGBCROP_Aug16F-8B16F	TanAlexanderlz	2025-05-29T17:23:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-29T15:26:33Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: RALL_RGBCROP_Aug16F-8B16F 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. --> # RALL_RGBCROP_Aug16F-8B16F This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7255 - Accuracy: 0.8494 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 4616 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.2671 \| 0.0626 \| 289 \| 0.5154 \| 0.8200 \| \| 0.1487 \| 1.0626 \| 578 \| 0.8120 \| 0.8364 \| \| 0.0851 \| 2.0626 \| 867 \| 0.9146 \| 0.8569 \| \| 0.0015 \| 3.0626 \| 1156 \| 1.1018 \| 0.8344 \| \| 0.0001 \| 4.0626 \| 1445 \| 1.2589 \| 0.8364 \| \| 0.0163 \| 5.0626 \| 1734 \| 1.2502 \| 0.8241 \| \| 0.0001 \| 6.0626 \| 2023 \| 1.4946 \| 0.7996 \| \| 0.0002 \| 7.0626 \| 2312 \| 1.3088 \| 0.8323 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
UmeAiRT/ComfyUI-Auto_installer	UmeAiRT	2025-05-29T17:19:47Z	17,493	28	diffusers	[ "diffusers", "onnx", "safetensors", "gguf", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-09-26T13:03:26Z	--- license: mit --- # UmeAiRT - ComfyUI auto installer I'm sharing with you my installation script, which automatically provides ComfyUI, workflows, models, custom nodes ... Just run "ComfyUI-AllinOne-Auto_install.bat". With a few questions at the beginning of the script, only the desired elements will be downloaded. ### Prerequisites : - [7zip](others/7z2409-x64.exe) - [git](others/Git-2.49.0-64-bit.exe) - [CUDA 12.8](others/cuda_12.8.1_windows_network.exe) ### What's included : #### ComfyUI : - ComfyUI portable version pytorch 2.7.0+cu128 - ComfyUI Manager - Interface settings - Xformers - Nvidia Apex - Sageattention - Triton #### Workflow : - TXT to IMG - IMG to IMG - INPAINT - OUTPAINT - PulID & REDUX - ControlNet HED/Canny/Openpose/Depth - TXT to VIDEO - IMG to VIDEO - StartEndFrames - Face to VIDEO - VIDEO EXTENSION - VIDEO to LOOP - Frames interpolations - Upscaler - Video merge #### WAN2.1 : - T2V Model - I2V Model - T2V GGUF Model - I2V GGUF Model - CLIP - CLIP Vision - VAE #### Flux1 : - flux1-dev - flux1-schnell-fp8 - GGUF - clip_l - t5xxl - VAE - ControlNet HED/Canny/Openpose/Depth ### Upscale Model : - RealESRGAN_x4plus.pth - RealESRGAN_x4plus_anime_6B.pth ### Custom Nodes : - ComfyUI-Custom-Scripts - ComfyUI-GGUF - ComfyUI-KJNodes - ComfyUI-VideoHelperSuite - ComfyUI-mxToolkit - ComfyUI-HunyuanVideoMultiLora - rgthree-comfy - ComfyUI-Frame-Interpolation - ComfyUI Impact Pack - ComfyUI-Easy-Use - ComfyUI_PuLID_Flux_ll - WAS Node Suite - ComfyUI-Florence2 - ComfyUI-Upscaler-Tensorrt - ComfyUI-MultiGPU - ComfyUI-WanStartEndFramesNative ![alt text][logo] [logo]: images/UmeAiRT.png "UmeAiRT logo"
Cicikush/Pixel-Attack-Helicopter	Cicikush	2025-05-29T17:16:25Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T16:26:54Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixel-Attack-Helicopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 23.50 +/- 25.03 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
repetitio/cg-qg-isg-simp-1.7b-ls0.0	repetitio	2025-05-29T17:13:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T06:38:57Z	--- base_model: Qwen/Qwen3-1.7b-Base library_name: transformers model_name: cg-qg-isg-simp-1.7b-ls0.0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for cg-qg-isg-simp-1.7b-ls0.0 This model is a fine-tuned version of [Qwen/Qwen3-1.7b-Base](https://huggingface.co/Qwen/Qwen3-1.7b-Base). 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="repetitio/cg-qg-isg-simp-1.7b-ls0.0", 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/repetitio/sphinx/runs/i1dr7zff) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0.dev0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Davidelks/LamaFineTuned	Davidelks	2025-05-29T17:08:13Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T15:08:40Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
ArtemisTAO/train3	ArtemisTAO	2025-05-29T17:07:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T17:05:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vinhthuan/vietnamese-news-summarizer-v3	vinhthuan	2025-05-29T17:06:25Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-25T12:34:30Z	--- base_model: unsloth/qwen2.5-1.5b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: vinhthuan - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-1.5b-instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tungduong261204/sft_v3_4000	tungduong261204	2025-05-29T17:04:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T17:03:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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l3dat/zephyr-vihealthqa-lora	l3dat	2025-05-29T17:04:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T11:28:01Z	--- 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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TomasLaz/t0-s1.1-3B-LoRA19-3.2e	TomasLaz	2025-05-29T17:03:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T17:02:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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esCyanide/ArcNemesis	esCyanide	2025-05-29T17:00:44Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-29T16:57:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF	mradermacher	2025-05-29T17:00:23Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:likaixin/UI-Box-Scorer-Qwen2.5-VL-7B", "base_model:quantized:likaixin/UI-Box-Scorer-Qwen2.5-VL-7B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-29T15:27:16Z	--- base_model: likaixin/UI-Box-Scorer-Qwen2.5-VL-7B language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/likaixin/UI-Box-Scorer-Qwen2.5-VL-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Box-Scorer-Qwen2.5-VL-7B-i1-GGUF/resolve/main/UI-Box-Scorer-Qwen2.5-VL-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Anshuman9600000/Phi	Anshuman9600000	2025-05-29T16:56:09Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T16:51:42Z	--- license: apache-2.0 ---
Donchocho/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_stocky_crocodile	Donchocho	2025-05-29T16:55:43Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am extinct stocky crocodile", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-10T09:14:19Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_stocky_crocodile tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am extinct stocky crocodile - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_stocky_crocodile 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="Donchocho/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_stocky_crocodile", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
TomasLaz/t0-s1.1-3B-LoRA18-8e	TomasLaz	2025-05-29T16:55:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T16:54:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tscstudios/qhmekbxahlbzndfhxbqmvbvzusi2_0a14e11f-bed1-4286-bd96-afbdab58b05a	tscstudios	2025-05-29T16:54:45Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T16:54:43Z	--- 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: TOK --- # Qhmekbxahlbzndfhxbqmvbvzusi2_0A14E11F Bed1 4286 Bd96 Afbdab58B05A <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/tscstudios/qhmekbxahlbzndfhxbqmvbvzusi2_0a14e11f-bed1-4286-bd96-afbdab58b05a/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('tscstudios/qhmekbxahlbzndfhxbqmvbvzusi2_0a14e11f-bed1-4286-bd96-afbdab58b05a', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/tscstudios/qhmekbxahlbzndfhxbqmvbvzusi2_0a14e11f-bed1-4286-bd96-afbdab58b05a/discussions) to add images that show off what you’ve made with this LoRA.
science-of-finetuning/gemma3_1B_model_organism_caps	science-of-finetuning	2025-05-29T16:51:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T16:51:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Godspower/mistral-users-finetune	Godspower	2025-05-29T16:48:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T16:48:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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(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]
dimasik2987/bb129592-4283-456c-b205-38484476c0bd	dimasik2987	2025-05-29T16:47:59Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B-Chat", "base_model:adapter:Qwen/Qwen1.5-0.5B-Chat", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T16:30:19Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: bb129592-4283-456c-b205-38484476c0bd results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen1.5-0.5B-Chat bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 3e76a907c7ad8abe_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: dimasik2987/bb129592-4283-456c-b205-38484476c0bd hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 12 mixed_precision: bf16 mlflow_experiment_name: /tmp/3e76a907c7ad8abe_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b70871ab-4dce-4935-b5bf-2fbd33c810d8 wandb_project: s56-7 wandb_run: your_name wandb_runid: b70871ab-4dce-4935-b5bf-2fbd33c810d8 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # bb129592-4283-456c-b205-38484476c0bd This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4974 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.6762 \| 0.0001 \| 1 \| 2.9733 \| \| 2.7038 \| 0.0318 \| 250 \| 2.5270 \| \| 2.0794 \| 0.0636 \| 500 \| 2.4974 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
sergioalves/d0cb4f6a-1bc4-46d9-b492-170e019634e5	sergioalves	2025-05-29T16:47:55Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B-Chat", "base_model:adapter:Qwen/Qwen1.5-0.5B-Chat", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T16:30:19Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: d0cb4f6a-1bc4-46d9-b492-170e019634e5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen1.5-0.5B-Chat bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 3e76a907c7ad8abe_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: sergioalves/d0cb4f6a-1bc4-46d9-b492-170e019634e5 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/3e76a907c7ad8abe_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b70871ab-4dce-4935-b5bf-2fbd33c810d8 wandb_project: s56-7 wandb_run: your_name wandb_runid: b70871ab-4dce-4935-b5bf-2fbd33c810d8 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # d0cb4f6a-1bc4-46d9-b492-170e019634e5 This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.6762 \| 0.0001 \| 1 \| 3.0216 \| \| 2.9723 \| 0.0318 \| 250 \| 2.7932 \| \| 2.3159 \| 0.0636 \| 500 \| 2.7653 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
zxny/Pixelcopter-PLE-v0	zxny	2025-05-29T16:46:03Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T15:34:13Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -2.90 +/- 2.21 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
BootesVoid/cmb2lfmtl06kru1cgkw7rwumr_cmb9k8r390esd1b1ysqfmmm44	BootesVoid	2025-05-29T16:43:35Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T16:43:33Z	--- 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: LL12 --- # Cmb2Lfmtl06Kru1Cgkw7Rwumr_Cmb9K8R390Esd1B1Ysqfmmm44 <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 `LL12` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LL12", "lora_weights": "https://huggingface.co/BootesVoid/cmb2lfmtl06kru1cgkw7rwumr_cmb9k8r390esd1b1ysqfmmm44/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb2lfmtl06kru1cgkw7rwumr_cmb9k8r390esd1b1ysqfmmm44', weight_name='lora.safetensors') image = pipeline('LL12').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb2lfmtl06kru1cgkw7rwumr_cmb9k8r390esd1b1ysqfmmm44/discussions) to add images that show off what you’ve made with this LoRA.
TareksTesting/Scripturient-V3.3-LLaMa-70B	TareksTesting	2025-05-29T16:42:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2406.11617", "base_model:TareksLab/Amethyst-SCE-V4-LLaMa-70B", "base_model:merge:TareksLab/Amethyst-SCE-V4-LLaMa-70B", "base_model:TareksLab/Carnelian-SCE-V4-LLaMa-70B", "base_model:merge:TareksLab/Carnelian-SCE-V4-LLaMa-70B", "base_model:TareksLab/Citrine-MS-V3-LLaMa-70B", "base_model:merge:TareksLab/Citrine-MS-V3-LLaMa-70B", "base_model:TareksLab/Diamond-DL-V1-LLaMa-70B", "base_model:merge:TareksLab/Diamond-DL-V1-LLaMa-70B", "base_model:TareksLab/Emerald-SCE-V3-LLaMa-70B", "base_model:merge:TareksLab/Emerald-SCE-V3-LLaMa-70B", "base_model:TareksLab/Ruby-D-V3-LLaMa-70B", "base_model:merge:TareksLab/Ruby-D-V3-LLaMa-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T16:13:56Z	--- base_model: - TareksLab/Carnelian-SCE-V4-LLaMa-70B - TareksLab/Emerald-SCE-V3-LLaMa-70B - TareksLab/Amethyst-SCE-V4-LLaMa-70B - TareksLab/Citrine-MS-V3-LLaMa-70B - TareksLab/Diamond-DL-V1-LLaMa-70B - TareksLab/Ruby-D-V3-LLaMa-70B library_name: transformers tags: - mergekit - merge --- # MERGE5 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 [Linear DELLA](https://arxiv.org/abs/2406.11617) merge method using [TareksLab/Diamond-DL-V1-LLaMa-70B](https://huggingface.co/TareksLab/Diamond-DL-V1-LLaMa-70B) as a base. ### Models Merged The following models were included in the merge: * [TareksLab/Carnelian-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Carnelian-SCE-V4-LLaMa-70B) * [TareksLab/Emerald-SCE-V3-LLaMa-70B](https://huggingface.co/TareksLab/Emerald-SCE-V3-LLaMa-70B) * [TareksLab/Amethyst-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Amethyst-SCE-V4-LLaMa-70B) * [TareksLab/Citrine-MS-V3-LLaMa-70B](https://huggingface.co/TareksLab/Citrine-MS-V3-LLaMa-70B) * [TareksLab/Ruby-D-V3-LLaMa-70B](https://huggingface.co/TareksLab/Ruby-D-V3-LLaMa-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TareksLab/Diamond-DL-V1-LLaMa-70B parameters: weight: 0.10 density: 0.7 epsilon: 0.20 - model: TareksLab/Citrine-MS-V3-LLaMa-70B parameters: weight: [0.5, 0.2, 0.1, 0.1, 0.1] density: 0.7 epsilon: 0.20 - model: TareksLab/Amethyst-SCE-V4-LLaMa-70B parameters: weight: [0.2, 0.4, 0.2, 0.1, 0.1] density: 0.7 epsilon: 0.20 - model: TareksLab/Ruby-D-V3-LLaMa-70B parameters: weight: [0.1, 0.2, 0.4, 0.2, 0.1] density: 0.7 epsilon: 0.20 - model: TareksLab/Carnelian-SCE-V4-LLaMa-70B parameters: weight: [0.1, 0.1, 0.2, 0.4, 0.2] density: 0.7 epsilon: 0.20 - model: TareksLab/Emerald-SCE-V3-LLaMa-70B parameters: weight: [0.1, 0.1, 0.1, 0.2, 0.5] density: 0.7 epsilon: 0.20 merge_method: della_linear base_model: TareksLab/Diamond-DL-V1-LLaMa-70B parameters: lambda: 1.1 normalize: false dtype: float32 out_dtype: bfloat16 chat_template: llama3 tokenizer: source: TareksLab/Ruby-D-V3-LLaMa-70B pad_to_multiple_of: 8 ```
CodeAtCMU/Qwen3-8B-Base_full_sft_natural_language_data_120K	CodeAtCMU	2025-05-29T16:39:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T16:36:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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onurkeles/qwen2.5-tid-gloss-lora	onurkeles	2025-05-29T16:39:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T16:39:22Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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elchito/unsloth_finetune_test	elchito	2025-05-29T16:35:19Z	0	0	transformers	[ "transformers", "safetensors", "mllama", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-29T15:29:22Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: elchito - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit This mllama 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)
silverside/arcticstyle_v2	silverside	2025-05-29T16:34:59Z	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	2025-05-29T16:02:53Z	--- 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: arcticstyle --- # Arcticstyle_V2 <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 `arcticstyle` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "arcticstyle", "lora_weights": "https://huggingface.co/silverside/arcticstyle_v2/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('silverside/arcticstyle_v2', weight_name='lora.safetensors') image = pipeline('arcticstyle').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: 0.0002 - LoRA rank: 12 ## Contribute your own examples You can use the [community tab](https://huggingface.co/silverside/arcticstyle_v2/discussions) to add images that show off what you’ve made with this LoRA.
gigipalsu/mona-testing	gigipalsu	2025-05-29T16:28:52Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-29T16:08:09Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: gigipalsu - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
galangswibawa/alwifarhan	galangswibawa	2025-05-29T16:23:20Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T16:22:30Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/alwifarhan_001200_00_20250529162137.png text: 4LFAR a man in a blue shirt standing in front of a red wall, with a banner in the background, raise hist fist for victory - output: url: sample/alwifarhan_001200_01_20250529162147.png text: 4LFAR a man hold badminton raqcuet in middle of court - output: url: sample/alwifarhan_001200_02_20250529162156.png text: 4LFAR a man in a white shirt and background white base_model: black-forest-labs/FLUX.1-dev instance_prompt: 4LFAR 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 --- # alwifarhan A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `4LFAR` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
OceanExplorer/ppo-LunarLander-v2	OceanExplorer	2025-05-29T16:21:44Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T16:21:25Z	--- 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: 261.25 +/- 21.39 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 ... ```
TareksTesting/Scripturient-V3.1-LLaMa-70B	TareksTesting	2025-05-29T16:20:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2406.11617", "base_model:TareksLab/Amethyst-SCE-V4-LLaMa-70B", "base_model:merge:TareksLab/Amethyst-SCE-V4-LLaMa-70B", "base_model:TareksLab/Carnelian-SCE-V4-LLaMa-70B", "base_model:merge:TareksLab/Carnelian-SCE-V4-LLaMa-70B", "base_model:TareksLab/Citrine-MS-V3-LLaMa-70B", "base_model:merge:TareksLab/Citrine-MS-V3-LLaMa-70B", "base_model:TareksLab/Diamond-DL-V1-LLaMa-70B", "base_model:merge:TareksLab/Diamond-DL-V1-LLaMa-70B", "base_model:TareksLab/Emerald-SCE-V3-LLaMa-70B", "base_model:merge:TareksLab/Emerald-SCE-V3-LLaMa-70B", "base_model:TareksLab/Ruby-D-V3-LLaMa-70B", "base_model:merge:TareksLab/Ruby-D-V3-LLaMa-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T14:36:36Z	--- base_model: - TareksLab/Carnelian-SCE-V4-LLaMa-70B - TareksLab/Ruby-D-V3-LLaMa-70B - TareksLab/Citrine-MS-V3-LLaMa-70B - TareksLab/Diamond-DL-V1-LLaMa-70B - TareksLab/Amethyst-SCE-V4-LLaMa-70B - TareksLab/Emerald-SCE-V3-LLaMa-70B library_name: transformers tags: - mergekit - merge --- # MERGE3 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 [Linear DELLA](https://arxiv.org/abs/2406.11617) merge method using [TareksLab/Diamond-DL-V1-LLaMa-70B](https://huggingface.co/TareksLab/Diamond-DL-V1-LLaMa-70B) as a base. ### Models Merged The following models were included in the merge: * [TareksLab/Carnelian-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Carnelian-SCE-V4-LLaMa-70B) * [TareksLab/Ruby-D-V3-LLaMa-70B](https://huggingface.co/TareksLab/Ruby-D-V3-LLaMa-70B) * [TareksLab/Citrine-MS-V3-LLaMa-70B](https://huggingface.co/TareksLab/Citrine-MS-V3-LLaMa-70B) * [TareksLab/Amethyst-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Amethyst-SCE-V4-LLaMa-70B) * [TareksLab/Emerald-SCE-V3-LLaMa-70B](https://huggingface.co/TareksLab/Emerald-SCE-V3-LLaMa-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TareksLab/Emerald-SCE-V3-LLaMa-70B parameters: weight: [0.1, 0.1, 0.1, 0.2, 0.5] density: 0.7 epsilon: 0.20 - model: TareksLab/Carnelian-SCE-V4-LLaMa-70B parameters: weight: [0.1, 0.1, 0.2, 0.4, 0.2] density: 0.7 epsilon: 0.20 - model: TareksLab/Ruby-D-V3-LLaMa-70B parameters: weight: [0.1, 0.2, 0.4, 0.2, 0.1] density: 0.7 epsilon: 0.20 - model: TareksLab/Amethyst-SCE-V4-LLaMa-70B parameters: weight: [0.2, 0.4, 0.2, 0.1, 0.1] density: 0.7 epsilon: 0.20 - model: TareksLab/Citrine-MS-V3-LLaMa-70B parameters: weight: [0.5, 0.2, 0.1, 0.1, 0.1] density: 0.7 epsilon: 0.20 - model: TareksLab/Diamond-DL-V1-LLaMa-70B parameters: weight: 0.10 density: 0.7 epsilon: 0.20 merge_method: della_linear base_model: TareksLab/Diamond-DL-V1-LLaMa-70B parameters: lambda: 1.1 normalize: false dtype: float32 out_dtype: bfloat16 chat_template: llama3 tokenizer: source: TareksLab/Ruby-D-V3-LLaMa-70B pad_to_multiple_of: 8 ```
tamewild/4b_v2_merged_e7	tamewild	2025-05-29T16:19:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T16:18:01Z	--- 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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Jarbas/ovos-model2vec-intents-serafim-335m-portuguese-pt-sentence-encoder	Jarbas	2025-05-29T16:11:33Z	4	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "pt", "dataset:Jarbas/ovos_intents_train", "base_model:Jarbas/m2v-256-serafim-335m-portuguese-pt-sentence-encoder", "base_model:finetune:Jarbas/m2v-256-serafim-335m-portuguese-pt-sentence-encoder", "license:mit", "region:us" ]	null	2025-05-20T21:58:32Z	--- base_model: Jarbas/m2v-256-serafim-335m-portuguese-pt-sentence-encoder library_name: model2vec license: mit model_name: ovos-model2vec-intents-serafim-335m-portuguese-pt-sentence-encoder tags: - embeddings - static-embeddings - sentence-transformers datasets: - Jarbas/ovos_intents_train language: - pt --- # model_pt_m2v-256-serafim-335m-portuguese-pt-sentence-encoder Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) Model2Vec model. It also includes a classifier head on top. ## Installation Install model2vec using pip: ``` pip install model2vec[inference] ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec.inference import StaticModelPipeline # Load a pretrained Model2Vec model model = StaticModelPipeline.from_pretrained("model_pt_m2v-256-serafim-335m-portuguese-pt-sentence-encoder") # Predict labels predicted = model.predict(["Example sentence"]) ``` ## Additional Resources - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) - [Website](https://minishlab.github.io/) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @article{minishlab2024model2vec, author = {Tulkens, Stephan and {van Dongen}, Thomas}, title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ```
vietan32/facebook-xml-roberta-base-300k-gg-snippets-bnb-4bit	vietan32	2025-05-29T16:10:11Z	0	0	null	[ "safetensors", "xlm-roberta", "base_model:vietan32/facebook-xml-roberta-base-300k-gg-snippets", "base_model:quantized:vietan32/facebook-xml-roberta-base-300k-gg-snippets", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T16:10:07Z	--- base_model: - vietan32/facebook-xml-roberta-base-300k-gg-snippets --- # vietan32/facebook-xml-roberta-base-300k-gg-snippets (Quantized) ## Description This model is a quantized version of the original model [`vietan32/facebook-xml-roberta-base-300k-gg-snippets`](https://huggingface.co/vietan32/facebook-xml-roberta-base-300k-gg-snippets). It's quantized using the BitsAndBytes library to 4-bit using the [bnb-my-repo](https://huggingface.co/spaces/bnb-community/bnb-my-repo) space. ## Quantization Details - Quantization Type: int4 - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 - bnb_4bit_quant_storage: uint8
Jarbas/ovos-model2vec-intents-roberta-large-ca-v2-massive	Jarbas	2025-05-29T16:09:48Z	4	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "ca", "dataset:Jarbas/ovos_intents_train", "base_model:Jarbas/m2v-256-roberta-large-ca-v2-massive", "base_model:finetune:Jarbas/m2v-256-roberta-large-ca-v2-massive", "license:mit", "region:us" ]	null	2025-05-20T21:37:24Z	--- library_name: model2vec license: mit model_name: model_ca_m2v-256-roberta-large-ca-v2-massive tags: - embeddings - static-embeddings - sentence-transformers base_model: - Jarbas/m2v-256-roberta-large-ca-v2-massive datasets: - Jarbas/ovos_intents_train language: - ca --- # model_ca_m2v-256-roberta-large-ca-v2-massive Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) Model2Vec model. It also includes a classifier head on top. ## Installation Install model2vec using pip: ``` pip install model2vec[inference] ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec.inference import StaticModelPipeline # Load a pretrained Model2Vec model model = StaticModelPipeline.from_pretrained("model_ca_m2v-256-roberta-large-ca-v2-massive") # Predict labels predicted = model.predict(["Example sentence"]) ``` ## Additional Resources - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) - [Website](https://minishlab.github.io/) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @article{minishlab2024model2vec, author = {Tulkens, Stephan and {van Dongen}, Thomas}, title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ```
nvidia/OpenMath-Nemotron-14B-Kaggle	nvidia	2025-05-29T16:08:26Z	240	15	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "nvidia", "math", "conversational", "en", "dataset:nvidia/OpenMathReasoning", "arxiv:2504.16891", "base_model:Qwen/Qwen2.5-14B", "base_model:finetune:Qwen/Qwen2.5-14B", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T17:58:58Z	--- license: cc-by-4.0 base_model: - Qwen/Qwen2.5-14B datasets: - nvidia/OpenMathReasoning language: - en tags: - nvidia - math library_name: transformers --- # OpenMath-Nemotron-14B-Kaggle OpenMath-Nemotron-14B-Kaggle is created by finetuning [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) on a subset of [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) dataset. This model was used in our first place submission to the [AIMO-2 Kaggle competition](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2/leaderboard)! ![Evaluation Results](./results.png) OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1 is an average accuracy across 64 generations and maj@64 is the result of majority voting. Please see our [paper](https://arxiv.org/abs/2504.16891) for more details on the evaluation setup. \| Model \| AIME24 \| AIME25 \| HMMT-24-25 \| HLE-Math \| \|-------------------------------\|-----------------\|-------\|-------\|-------------\| \| DeepSeek-R1-Distill-Qwen-1.5B \| 26.8 (60.0) \| 21.4 (36.7) \| 14.2 (26.5) \| 2.9 (5.0) \| \| [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) CoT \| 61.6 (80.0) \| 49.5 (66.7) \| 39.9 (53.6) \| 5.4 (5.4) \| \| [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) TIR \| 52.0 (83.3) \| 39.7 (70.0) \| 37.2 (60.7) \| 2.5 (6.2) \| \| + Self GenSelect \| 83.3 \| 70.0 \| 62.2 \| 7.9 \| \| + 32B GenSelect \| 83.3 \| 70.0 \| 62.8 \| 8.3 \| \| DeepSeek-R1-Distill-Qwen-7B \| 54.4 (80.0) \| 38.6 (53.3) \| 30.6 (42.9) \| 3.3 (5.2) \| \| [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) CoT \| 74.8 (80.0) \| 61.2 (76.7) \| 49.7 (57.7) \| 6.6 (6.6) \| \| [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) TIR \| 72.9 (83.3) \| 57.5 (76.7) \| 54.6 (66.3) \| 7.8 (10.8) \| \| + Self GenSelect \| 86.7 \| 76.7 \| 68.4 \| 11.5 \| \| + 32B GenSelect \| 86.7 \| 76.7 \| 69.9 \| 11.9 \| \| DeepSeek-R1-Distill-Qwen-14B \| 65.8 (80.0) \| 48.4 (60.0) \| 40.1 (52.0) \| 4.2 (4.8) \| \| [OpenMath-Nemotron-14B-MIX (kaggle)](https://huggingface.co/nvidia/OpenMath-Nemotron-14B-Kaggle) \| 73.7 (86.7) \| 57.9 (73.3) \| 50.5 (64.8) \| 5.7 (6.5) \| \| [OpenMath-Nemotron-14B](https://huggingface.co/nvidia/OpenMath-Nemotron-14B) CoT \| 76.3 (83.3) \| 63.0 (76.7) \| 52.1 (60.7) \| 7.5 (7.6) \| \| [OpenMath-Nemotron-14B](https://huggingface.co/nvidia/OpenMath-Nemotron-14B) TIR \| 76.3 (86.7) \| 61.3 (76.7) \| 58.6 (70.9) \| 9.5 (11.5) \| \| + Self GenSelect \| 86.7 \| 76.7 \| 72.4 \| 14.1 \| \| + 32B GenSelect \| 90.0 \| 76.7 \| 71.9 \| 13.7 \| \| QwQ-32B \| 78.1 (86.7) \| 66.5 (76.7) \| 55.9 (63.3) \| 9.0 (9.5) \| \| DeepSeek-R1-Distill-Qwen-32B \| 66.9 (83.3) \| 51.8 (73.3) \| 39.9 (51.0) \| 4.8 (6.0) \| \| [OpenMath-Nemotron-32B](https://huggingface.co/nvidia/OpenMath-Nemotron-32B) CoT \| 76.5 (86.7) \| 62.5 (73.3) \| 53.0 (59.2) \| 8.3 (8.3) \| \| [OpenMath-Nemotron-32B](https://huggingface.co/nvidia/OpenMath-Nemotron-32B) TIR \| 78.4 (93.3) \| 64.2 (76.7) \| 59.7 (70.9) \| 9.2 (12.5) \| \| + Self GenSelect \| 93.3 \| 80.0 \| 73.5 \| 15.7 \| \| DeepSeek-R1 \| 79.1 (86.7) \| 64.3 (73.3) \| 53.0 (59.2) \| 10.5 (11.4) \| ## Reproducing our results The pipeline we used to produce the data and models is fully open-sourced! - [Code](https://github.com/NVIDIA/NeMo-Skills) - [Models](https://huggingface.co/collections/nvidia/openmathreasoning-68072c0154a5099573d2e730) - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathReasoning) - [Paper](https://arxiv.org/abs/2504.16891) We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/) to fully reproduce our results, including data generation. ## How to use the models? This model will always use code execution to solve math problems, so we highly recommend to run inference with our [reference implementation in NeMo-Skills](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/evaluation/). Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain. ## Citation If you find our work useful, please consider citing us! ```bibtex @article{moshkov2025aimo2, title = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset}, author = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman}, year = {2025}, journal = {arXiv preprint arXiv:2504.16891} } ``` ## Additional information ### License/Terms of Use: <br> GOVERNING TERMS: Use of this model is governed by [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en). Additional Information: [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B/blob/main/LICENSE). ### Deployment Geography: Global <br> ### Use Case: <br> This model is intended to facilitate research in the area of mathematical reasoning. ### Release Date:  <br> Huggingface 04/23/2025 <br> ### Model Architecture: <br> Architecture Type: Transformer decoder-only language model  <br> Network Architecture: Qwen2.5 <br> This model was developed based on Qwen2.5-1.5B <br> This model has 1.5B of model parameters. <br> ### Input: <br> Input Type(s): Text <br> Input Format(s): String <br> Input Parameters: One-Dimensional (1D) <br> Other Properties Related to Input: Context length up to 131,072 tokens <br> ### Output: <br> Output Type(s): Text <br> Output Format: String <br> Output Parameters: One-Dimensional (1D) <br> Other Properties Related to Output: Context length up to 131,072 tokens <br> Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br> ### Software Integration : <br> Runtime Engine(s): <br> * Tensor RT / Triton <br> Supported Hardware Microarchitecture Compatibility: <br> * NVIDIA Ampere <br> * NVIDIA Hopper <br> Preferred Operating System(s): <br> * Linux <br> ### Model Version(s): [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) [OpenMath-Nemotron-14B](https://huggingface.co/nvidia/OpenMath-Nemotron-14B) [OpenMath-Nemotron-32B](https://huggingface.co/nvidia/OpenMath-Nemotron-32B) # Ethical Considerations: NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY.md), and [Privacy](./PRIVACY.md) Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
amazon/bort	amazon	2025-05-29T16:02:15Z	321	16	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "arxiv:2010.10499", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	⚠️ ARCHIVED ⚠️ ## BORT [Amazon's BORT](https://www.amazon.science/blog/a-version-of-the-bert-language-model-thats-20-times-as-fast) BORT is a highly compressed version of [bert-large](https://huggingface.co/bert-large-uncased) that is up to 10 times faster at inference. The model is an optimal sub-architecture of bert-large that was found using neural architecture search. [Paper](https://arxiv.org/abs/2010.10499) Abstract We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as "Bort", is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of 5.5% the original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in 288 GPU hours, which is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large (Liu et al., 2019), and about 33% of that of the world-record, in GPU hours, required to train BERT-large on the same hardware. It is also 7.9x faster on a CPU, as well as being better performing than other compressed variants of the architecture, and some of the non-compressed variants: it obtains performance improvements of between 0.3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks. The original model can be found under: https://github.com/alexa/bort IMPORTANT BORT requires a very unique fine-tuning algorithm, called [Agora](https://adewynter.github.io/notes/bort_algorithms_and_applications.html) which is not open-sourced yet. Standard fine-tuning has not shown to work well in initial experiments, so stay tuned for updates!
mradermacher/Mystica1-4B-GGUF	mradermacher	2025-05-29T16:00:07Z	69	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "trl", "sft", "en", "ja", "base_model:Sakalti/Mystica1-4B", "base_model:quantized:Sakalti/Mystica1-4B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-28T14:17:34Z	--- base_model: Sakalti/Mystica1-4B language: - en - ja library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Sakalti/Mystica1-4B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mystica1-4B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q5_K_M.gguf) \| Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.Q8_0.gguf) \| Q8_0 \| 4.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mystica1-4B-GGUF/resolve/main/Mystica1-4B.f16.gguf) \| f16 \| 8.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
kimxxxx/mistral_r64_a128_b8_gas8_Ler2e-5_gradclip2_hackcehctfmansub4500_INST_2epoch	kimxxxx	2025-05-29T15:58:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T15:57:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tamewild/4b_v2_merged_e8	tamewild	2025-05-29T15:58:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T15:56:06Z	--- 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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Cicikush/Reinforce-CartPole-31	Cicikush	2025-05-29T15:56:59Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T15:56:50Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-31 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jinxxx123/financial_text_sentiment_classification_model	jinxxx123	2025-05-29T15:56:27Z	0	0	transformers	[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T15:56:06Z	--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_keras_callback model-index: - name: financial_text_sentiment_classification_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # financial_text_sentiment_classification_model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6891 - Validation Loss: 0.5052 - Train Accuracy: 0.7853 - Epoch: 1 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 1460, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.9489 \| 0.8353 \| 0.6125 \| 0 \| \| 0.6891 \| 0.5052 \| 0.7853 \| 1 \| ### Framework versions - Transformers 4.52.2 - TensorFlow 2.18.0 - Datasets 2.14.4 - Tokenizers 0.21.1
Franklin0/ReasonGen-R1-SFT	Franklin0	2025-05-29T15:52:38Z	137	0	transformers	[ "transformers", "safetensors", "multi_modality", "dataset:Franklin0/ReasonGen-R1-SFT-230k", "base_model:deepseek-ai/Janus-Pro-7B", "base_model:finetune:deepseek-ai/Janus-Pro-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T04:15:30Z	--- library_name: transformers license: apache-2.0 datasets: - Franklin0/ReasonGen-R1-SFT-230k base_model: - deepseek-ai/Janus-Pro-7B --- # Model Card for Model ID SFT Only model for the paper: "[ReasonGen-R1: Cot for Autoregressive Image generation models through SFT and RL](xxx)". Website: https://aka.ms/reasongen Code: https://github.com/Franklin-Zhang0/Image-RL
nudrick/farmer	nudrick	2025-05-29T15:51:29Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T15:37:29Z	--- 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: farmer --- # Farmer <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 `farmer` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "farmer", "lora_weights": "https://huggingface.co/nudrick/farmer/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('nudrick/farmer', weight_name='lora.safetensors') image = pipeline('farmer').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/nudrick/farmer/discussions) to add images that show off what you’ve made with this LoRA.
naveen1divakar/q-Taxi-v3	naveen1divakar	2025-05-29T15:49:29Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T15:49:27Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="naveen1divakar/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ujjwal1996/Fine_tuning_unsloth-Llama-3.2-3B-Instruct_50steps	ujjwal1996	2025-05-29T15:46:28Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-26T13:25:24Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ujjwal1996 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
natarina/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_iguana	natarina	2025-05-29T15:45:30Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am omnivorous hulking iguana", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-29T15:45:20Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_iguana tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am omnivorous hulking iguana - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_iguana This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="natarina/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-omnivorous_hulking_iguana", 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: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
BootesVoid/cmb9i07zs0duz1b1yhostb6g8_cmb9ism030e921b1y8uih0nx7	BootesVoid	2025-05-29T15:43:37Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T15:43:36Z	--- 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: pimen --- # Cmb9I07Zs0Duz1B1Yhostb6G8_Cmb9Ism030E921B1Y8Uih0Nx7 <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 `pimen` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "pimen", "lora_weights": "https://huggingface.co/BootesVoid/cmb9i07zs0duz1b1yhostb6g8_cmb9ism030e921b1y8uih0nx7/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb9i07zs0duz1b1yhostb6g8_cmb9ism030e921b1y8uih0nx7', weight_name='lora.safetensors') image = pipeline('pimen').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb9i07zs0duz1b1yhostb6g8_cmb9ism030e921b1y8uih0nx7/discussions) to add images that show off what you’ve made with this LoRA.
ryota-komatsu/flow_matching_with_bigvgan	ryota-komatsu	2025-05-29T15:43:20Z	0	0	transformers	[ "transformers", "safetensors", "flow_matching_with_bigvgan", "en", "dataset:ryota-komatsu/libritts-r-mhubert-2000units", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-03-25T19:39:39Z	--- library_name: transformers license: mit datasets: - ryota-komatsu/libritts-r-mhubert-2000units language: - en --- # 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. - License: MIT ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [repo](https://github.com/ryota-komatsu/speech_resynth) - Demo: [demo](https://ryota-komatsu.github.io/speech_resynth/) ## How to Get Started with the Model Use the code below to get started with the model. ```bash git clone https://github.com/ryota-komatsu/speech_resynth.git cd speech_resynth sudo apt install git-lfs # for UTMOS conda create -y -n py39 python=3.9.21 pip=24.0 conda activate py39 pip install -r requirements/requirements.txt sh scripts/setup.sh # download textlesslib and UTMOS cd src/textlesslib pip install -e . cd - ``` ```python import torchaudio from textless.data.speech_encoder import SpeechEncoder from src.flow_matching.models import ConditionalFlowMatchingWithBigVGan wav_path = "/path/to/wav" encoder = SpeechEncoder.by_name( dense_model_name="mhubert-base-vp_mls_cv_8lang", quantizer_model_name="kmeans-expresso", vocab_size=2000, deduplicate=False, need_f0=False, ).cuda() # download a pretrained model from hugging face hub decoder = ConditionalFlowMatchingWithBigVGan.from_pretrained("ryota-komatsu/flow_matching_with_bigvgan").cuda() # load a waveform waveform, sr = torchaudio.load(wav_path) waveform = torchaudio.functional.resample(waveform, sr, 16000) # encode a waveform into pseudo-phonetic units units = encoder(waveform.cuda())["units"] units = units.unsqueeze(0) + 1 # 0: pad # resynthesis audio_values = decoder(units) ``` ## 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. --> [16 kHz-downsampled LibriTTS-R train set](https://huggingface.co/datasets/ryota-komatsu/libritts-r-mhubert-2000units)

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