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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-18 06:30:30	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 563 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-18 06:29:14	card stringlengths 11 1.01M
onnxmodelzoo/dla34_Opset17	onnxmodelzoo	2025-09-18T05:57:18	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-18T05:57:10	--- language: en license: apache-2.0 model_name: dla34_Opset17.onnx tags: - Computer_Vision ---
YuanhengCasia/dipllm	YuanhengCasia	2025-09-18T05:33:41	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-18T05:33:41	--- license: apache-2.0 ---
eiknarf/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-yawning_dextrous_monkey	eiknarf	2025-09-18T05:33:34	71	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am yawning_dextrous_monkey", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-13T01:37:25	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am yawning_dextrous_monkey --- # 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]
0xOzii/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee	0xOzii	2025-09-18T05:25:07	12	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am large padded chimpanzee", "trl", "genrl-swarm", "I am large_padded_chimpanzee", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-09T20:44:14	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am large padded chimpanzee - trl - genrl-swarm - I am large_padded_chimpanzee licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee 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="0xOzii/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee", 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: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
noobmaster6009/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vicious_yawning_dolphin	noobmaster6009	2025-09-18T05:23:06	44	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am vicious_yawning_dolphin", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-14T15:07:37	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am vicious_yawning_dolphin --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
onnxmodelzoo/cs3darknet_focus_l_Opset17	onnxmodelzoo	2025-09-18T05:23:04	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-18T05:22:54	--- language: en license: apache-2.0 model_name: cs3darknet_focus_l_Opset17.onnx tags: - Computer_Vision ---
zjhhhh/qwen2.5_3B_Instruct_judge_rebel_1e4_step_70_final	zjhhhh	2025-09-18T05:20:44	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-18T05:20:19	--- 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]
QuangDuy/mmBERT-base_1809	QuangDuy	2025-09-18T05:01:35	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "base_model:jhu-clsp/mmBERT-base", "base_model:finetune:jhu-clsp/mmBERT-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-09-18T02:25:12	--- library_name: transformers license: mit base_model: jhu-clsp/mmBERT-base tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: mmBERT-base_1809 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. --> # mmBERT-base_1809 This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8179 - Accuracy: 0.7386 - Precision: 0.7385 - Recall: 0.7442 - F1: 0.7394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 10 - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.6906 \| 1.0 \| 350 \| 0.8201 \| 0.6879 \| 0.6995 \| 0.6901 \| 0.6921 \| \| 1.4776 \| 2.0 \| 700 \| 0.7611 \| 0.7464 \| 0.7484 \| 0.7552 \| 0.7450 \| \| 1.3559 \| 3.0 \| 1050 \| 0.7602 \| 0.7521 \| 0.7551 \| 0.7544 \| 0.7542 \| \| 1.1664 \| 4.0 \| 1400 \| 0.7964 \| 0.7471 \| 0.7481 \| 0.7547 \| 0.7467 \| \| 0.9809 \| 5.0 \| 1750 \| 0.8179 \| 0.7386 \| 0.7385 \| 0.7442 \| 0.7394 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.7.1+cu128 - Tokenizers 0.22.0
Dwentz/Qwen3-0.6B-Gensyn-Swarm-whiskered_docile_hummingbird	Dwentz	2025-09-18T03:37:51	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am whiskered_docile_hummingbird", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-18T02:47:08	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am whiskered_docile_hummingbird --- # 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]
CompassioninMachineLearning/Basellama_plus1kmedaijazz_plus20kfinetune	CompassioninMachineLearning	2025-09-18T03:24:44	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:CompassioninMachineLearning/Basellama_plus1kmedaijazz", "base_model:finetune:CompassioninMachineLearning/Basellama_plus1kmedaijazz", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-18T03:18:45	--- base_model: CompassioninMachineLearning/Basellama_plus1kmedaijazz tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: CompassioninMachineLearning - License: apache-2.0 - Finetuned from model : CompassioninMachineLearning/Basellama_plus1kmedaijazz 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)
lihaoxin2020/qwen-instruct-synthetic_1_math_only	lihaoxin2020	2025-09-18T03:13:50	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-14T09:44:53	--- 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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cjkasbdkjnlakb/agent-0917-only-tool	cjkasbdkjnlakb	2025-09-18T02:33:45	0	0	peft	[ "peft", "safetensors", "qwen3", "text-generation", "axolotl", "base_model:adapter:Qwen/Qwen3-4B-Instruct-2507", "lora", "transformers", "conversational", "dataset:custom", "base_model:Qwen/Qwen3-4B-Instruct-2507", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-18T02:33:27	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen3-4B-Instruct-2507 tags: - axolotl - base_model:adapter:Qwen/Qwen3-4B-Instruct-2507 - lora - transformers datasets: - custom pipeline_tag: text-generation model-index: - name: checkpoints/0917-only-tool 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: `0.13.0.dev0` ```yaml # Automatically upload checkpoint and final model to HF # hub_model_id: username/custom_model_name # 是否以 8-bit 精度加载模型 load_in_8bit: false # 是否以 4-bit 精度加载模型（与QLoRA绑定, 强制使用） load_in_4bit: false # 是否严格匹配模型结构，关闭表示可加载少部分差异结构（如以适配 adapter） # strict: false base_model: Qwen/Qwen3-4B-Instruct-2507 # 数据集设置 chat_template: qwen3 datasets: - path: /workspace/axolotl/train_dir/tool_agent_train_data.json # - 表示列表（list）中的一项, 即可以同时使用多个数据集 type: chat_template # chat_template(自定义格式) alpaca roles_to_train: ["assistant"] field_messages: messages # 标识的字段 message_property_mappings: # message_property_mappings={'role':'role', 'content':'content'}) role: role content: content dataset_prepared_path: val_set_size: 0.05 output_dir: checkpoints/0917-only-tool sequence_len: 16384 # 模型所能处理的最大上下文长度(默认2048) pad_to_sequence_len: true # context_parallel_size: 2 # 长序列拆分至多个GPU(强制要求 mirco_batch_size: 1) sample_packing: false # 在训练时将多个样本拼接（packing）成一个长序列(sequence_len)输入到模型中，以提高训练效率。 eval_sample_packing: false # 评估时拼接多个样本 # 训练超参数 adapter: lora # lora qlora lora_model_dir: lora_r: 32 # lora_r默认首选 16，平衡精度与显存 lora_alpha: 64 # 缩放系数，用于控制 LoRA 的影响力, 一般设为 2r 或 4r lora_dropout: 0.05 lora_target_linear: true micro_batch_size: 4 # 微批次大小 94G的H100可以设为4(Token为1w) gradient_accumulation_steps: 8 # 梯度累积: 将多个微批次的梯度(micro_batch_size)累积起来，然后更新模型权重有效 Batch 常取 16: 小于 8 训练会抖，大于 32 只会更耗时、收益有限 auto_find_batch_size: false # 允许Axolotl不断调整batch_size ⚠️Zero-3不适用 num_epochs: 1 optimizer: adamw_torch_fused lr_scheduler: cosine learning_rate: 2e-5 # bf16: auto + tf32: true，可获得更好的稳定性和性能。 bf16: auto tf32: true # early_stopping_patience: gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false # auto_resume_from_checkpoints: true #自动从output_dir寻找最新checkpoint断点恢复 logging_steps: 1 flash_attention: true warmup_steps: 10 evals_per_epoch: 4 saves_per_epoch: 1 weight_decay: 0.0 # deepspeed: /workspace/deepspeed_configs/zero2.json # fsdp: # - full_shard # - auto_wrap # fsdp_config: # fsdp_limit_all_gathers: true # fsdp_sync_module_states: true # fsdp_offload_params: true # fsdp_use_orig_params: false # fsdp_cpu_ram_efficient_loading: true # fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP # fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer # fsdp_state_dict_type: FULL_STATE_DICT # fsdp_sharding_strategy: FULL_SHARD # special_tokens: # wandb_project: # wandb_entity: # wandb_watch: # wandb_name: # wandb_log_model: ``` </details><br> # checkpoints/0917-only-tool This model is a fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) on the /workspace/axolotl/train_dir/tool_agent_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 0.0576 - Memory/max Active (gib): 103.82 - Memory/max Allocated (gib): 103.82 - Memory/device Reserved (gib): 136.87 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 495 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Active (gib) \| Allocated (gib) \| Reserved (gib) \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------------:\|:---------------:\|:--------------:\| \| No log \| 0 \| 0 \| 0.6946 \| 103.29 \| 103.29 \| 103.94 \| \| 0.0645 \| 0.2505 \| 124 \| 0.0663 \| 103.82 \| 103.82 \| 136.85 \| \| 0.0474 \| 0.5010 \| 248 \| 0.0598 \| 103.82 \| 103.82 \| 136.87 \| \| 0.0536 \| 0.7515 \| 372 \| 0.0576 \| 103.82 \| 103.82 \| 136.87 \| ### Framework versions - PEFT 0.17.1 - Transformers 4.56.1 - Pytorch 2.7.1+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
rqadri/privacy4b_150s	rqadri	2025-09-18T02:31:40	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/Qwen3-4B-Instruct-2507", "grpo", "lora", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "base_model:unsloth/Qwen3-4B-Instruct-2507", "region:us" ]	text-generation	2025-09-18T02:31:19	--- base_model: unsloth/Qwen3-4B-Instruct-2507 library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/Qwen3-4B-Instruct-2507 - grpo - lora - transformers - trl - 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. --> - 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 0.17.0
scb10x/typhoon-asr-realtime	scb10x	2025-09-18T02:03:42	585	15	nemo	[ "nemo", "pytorch", "NeMo", "base_model:nvidia/stt_en_fastconformer_transducer_large", "base_model:finetune:nvidia/stt_en_fastconformer_transducer_large", "license:cc-by-4.0", "region:us" ]	null	2025-09-02T08:17:09	--- library_name: nemo license: cc-by-4.0 tags: - pytorch - NeMo base_model: - nvidia/stt_en_fastconformer_transducer_large --- # Typhoon-asr-realtime <style> img { display: inline; } </style> \| [![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--Transducer-lightgrey#model-badge)](#model-architecture) \| [![Model size](https://img.shields.io/badge/Params-114M-lightgrey#model-badge)](#model-architecture) \| [![Language](https://img.shields.io/badge/Language-th-lightgrey#model-badge)](#datasets) Typhoon ASR Real-Time is a next-generation, open-source Automatic Speech Recognition (ASR) model built specifically for real-world streaming applications in the Thai language. It is designed to deliver fast and accurate transcriptions while running efficiently on standard CPUs. This enables users to host their own ASR service, reducing costs and avoiding the need to send sensitive data to third-party cloud services. The model is trained on 10,000 hours of Thai audio transcriptions to help it generalize to any environments. The model is based on [NVIDIA's FastConformer Transducer model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer), which is optimized for low-latency, real-time performance. Try our demo available on [Demo]() Code / Examples available on [Github](https://github.com/scb-10x/typhoon-asr) Release Blog available on [OpenTyphoon Blog](https://opentyphoon.ai/blog/en/typhoon-asr-realtime-release) * ### Performance <img src="https://storage.googleapis.com/typhoon-public/assets/typhoon_asr/performance-typhoon-asr-realtime.png" alt="cer vs rtfx performance" width="90%"/> ### Summary of Findings Typhoon ASR Realtime achieves 4097x real-time processing speed with competitive accuracy (CER: 0.0984), representing a 6x throughput improvement over the next fastest model. RTFx values are from the [Open ASR Leaderboard on Hugging Face](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard). The model outperforms Whisper variants by 15-19x in throughput while maintaining comparable accuracy, making it ideal for production Thai speech recognition requiring real-time performance and high-volume processing scenarios. * ### Usage and Implementation (Recommended): Quick Start with Google Colab For a hands-on demonstration without any local setup, you can run this project directly in Google Colab. The notebook provides a complete environment to transcribe audio files and experiment with the model. [![Alt text](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1t4tlRTJToYRolTmiN5ZWDR67ymdRnpAz?usp=sharing) (Recommended): Using the `typhoon-asr` Package This is the easiest way to get started. You can install the package via pip and use it directly from the command line or within your Python code. 1. Install the package: ```bash pip install typhoon-asr ``` 2. Command-Line Usage: ```bash # Basic transcription (auto-detects device) typhoon-asr path/to/your_audio.wav # Transcription with timestamps on a specific device typhoon-asr path/to/your_audio.mp3 --with-timestamps --device cuda ``` 3. Python API Usage: ```python from typhoon_asr import transcribe # Basic transcription result = transcribe("path/to/your_audio.wav") print(result['text']) # Transcription with timestamps result_with_timestamps = transcribe("path/to/your_audio.wav", with_timestamps=True) print(result_with_timestamps) ``` (Alternative): Running from the Repository Script You can also run the model by cloning the repository and using the inference script directly. This method is useful for development or if you need to modify the underlying code. 1. Clone the repository and install dependencies: ```bash git clone https://github.com/scb-10x/typhoon-asr.git cd typhoon-asr pip install -r requirements.txt ``` 2. Run the inference script: The `typhoon_asr_inference.py` script handles audio resampling and processing automatically. ```bash # Basic Transcription (CPU): python typhoon_asr_inference.py path/to/your_audio.m4a # Transcription with Estimated Timestamps: python typhoon_asr_inference.py path/to/your_audio.wav --with-timestamps # Transcription on a GPU: python typhoon_asr_inference.py path/to/your_audio.mp3 --device cuda ``` ## Follow us https://twitter.com/opentyphoon ## Support https://discord.gg/us5gAYmrxw
dhsjksid/blockassist-bc-loud_colorful_albatross_1758158252	dhsjksid	2025-09-18T01:17:36	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud colorful albatross", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T01:17:33	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud colorful albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Sanskrit-qwen-7B-Translate-GGUF	mradermacher	2025-09-17T23:41:26	311	5	transformers	[ "transformers", "gguf", "sanskrit", "translation", "qwen", "axolotl", "en", "dataset:diabolic6045/Sanskrit-llama", "base_model:diabolic6045/Sanskrit-qwen-7B-Translate-Legacy", "base_model:quantized:diabolic6045/Sanskrit-qwen-7B-Translate-Legacy", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	translation	2025-03-05T14:17:13	--- base_model: diabolic6045/Sanskrit-qwen-7B-Translate-Legacy datasets: - diabolic6045/Sanskrit-llama language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - sanskrit - translation - qwen - axolotl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/diabolic6045/Sanskrit-qwen-7B-Translate-Legacy <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Sanskrit-qwen-7B-Translate-GGUF).* 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/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Sanskrit-qwen-7B-Translate-GGUF/resolve/main/Sanskrit-qwen-7B-Translate.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 -->
Roobick/llama3_cuda_lora	Roobick	2025-09-17T23:31:30	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T23:31:28	--- 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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(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]
BootesVoid/cmfoj78i10b8px0n0y56u3kjs_cmfojpsfy0b8wx0n0jeybm7ry	BootesVoid	2025-09-17T23:30:47	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-17T23:30:45	--- 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: NORMAL --- # Cmfoj78I10B8Px0N0Y56U3Kjs_Cmfojpsfy0B8Wx0N0Jeybm7Ry <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 `NORMAL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NORMAL", "lora_weights": "https://huggingface.co/BootesVoid/cmfoj78i10b8px0n0y56u3kjs_cmfojpsfy0b8wx0n0jeybm7ry/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/cmfoj78i10b8px0n0y56u3kjs_cmfojpsfy0b8wx0n0jeybm7ry', weight_name='lora.safetensors') image = pipeline('NORMAL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2500 - Learning rate: 9e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmfoj78i10b8px0n0y56u3kjs_cmfojpsfy0b8wx0n0jeybm7ry/discussions) to add images that show off what you’ve made with this LoRA.
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758151532	devivodowdlel	2025-09-17T23:26:40	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T23:26:32	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD-0914190921-epoch-8	vectorzhou	2025-09-17T23:22:52	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "fine-tuned", "trl", "extra-gradient", "conversational", "dataset:PKU-Alignment/PKU-SafeRLHF", "arxiv:2503.08942", "base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024", "base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T23:22:14	--- base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024 datasets: PKU-Alignment/PKU-SafeRLHF library_name: transformers model_name: gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD tags: - generated_from_trainer - text-generation - fine-tuned - trl - extra-gradient licence: license --- # Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD-0914190921-epoch-8", 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/zrl_csl_nlhf/nlhf/runs/6tm2oyul) This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942). ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu128 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite Extragradient as: ```bibtex @misc{zhou2025extragradientpreferenceoptimizationegpo, title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback}, author={Runlong Zhou and Maryam Fazel and Simon S. Du}, year={2025}, eprint={2503.08942}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.08942}, } ``` 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}} } ```
shuohsuan/svla_start1	shuohsuan	2025-09-17T22:14:53	0	0	lerobot	[ "lerobot", "safetensors", "smolvla", "robotics", "dataset:shuohsuan/start1", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-09-17T10:05:26	--- base_model: lerobot/smolvla_base datasets: shuohsuan/start1 library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - smolvla - robotics - lerobot --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`. ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details * License: apache-2.0
patelpd/CSCI-45000-Dummy-Model	patelpd	2025-09-17T22:10:01	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-17T22:10:01	--- license: apache-2.0 ---
aamijar/ReplaceME-Llama-2-5B-lora-r8-boolq-epochs2	aamijar	2025-09-17T22:05:08	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T22:05:03	--- 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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RawanAlwadeya/AlzheimerClassifierTL	RawanAlwadeya	2025-09-17T21:44:22	0	0	keras	[ "keras", "alzheimer", "mri", "medical-imaging", "transfer-learning", "resnet50", "tensorflow", "deep-learning", "image-classification", "en", "dataset:augmented-alzheimer-mri-dataset", "license:mit", "region:us" ]	image-classification	2025-09-16T17:05:03	--- library_name: keras license: mit language: - en pipeline_tag: image-classification tags: - alzheimer - mri - medical-imaging - transfer-learning - resnet50 - keras - tensorflow - deep-learning datasets: - augmented-alzheimer-mri-dataset metrics: - accuracy - precision - recall - f1 base_model: - keras/resnet50 --- # Model Card for AlzheimerClassifierTL: ResNet50-Based Alzheimer’s MRI Classifier AlzheimerClassifierTL is a deep learning model that applies transfer learning with ResNet50 to classify brain MRI scans into four cognitive health categories: 🟢 NonDemented • 🟡 Very Mild Demented • 🟠 Mild Demented • 🔴 Moderate Demented > ⚠️ Disclaimer: This model is intended for research and educational purposes only. > It is not a medical diagnostic tool and must not be used as a substitute for professional healthcare evaluation. --- ## Model Details ### Key Features - Multi-class classification of Alzheimer’s stages from brain MRI images - Transfer learning using ResNet50 pretrained on ImageNet - Input size: 224×224 RGB images, normalized with `tf.keras.applications.resnet50.preprocess_input` - Custom dense head with batch normalization, L1/L2 regularization, and dropout (0.45) - Evaluated on balanced train/validation/test splits with strong generalization ### Skills & Technologies - TensorFlow / Keras for model development and training - ImageDataGenerator for preprocessing and augmentation - ResNet50 for feature extraction and fine-tuning - Matplotlib & Seaborn for performance visualization - Streamlit & Hugging Face for real-time deployment - Kaggle for dataset hosting and notebook experimentation - Developed by: [Rawan Alwadeya](https://www.linkedin.com/in/rawan-alwadeya-17948a305/) - Model type: Convolutional Neural Network (CNN) with Transfer Learning - Language(s): N/A (image model) - License: MIT --- ## Uses This model can be used for: - Research on Alzheimer’s disease detection and progression staging - Educational demonstrations of transfer learning in medical imaging - AI portfolio projects that show end-to-end deployment on Streamlit & Hugging Face --- ## Performance Results on the held-out test set: - Accuracy: 98.3 % - Precision: 98.9 % - Recall: 93.8 % - F1 Score: 96.1 % --- ## Example Usage ```python import numpy as np from keras.models import load_model from PIL import Image import requests from io import BytesIO from huggingface_hub import hf_hub_download from tensorflow.keras.applications.resnet50 import preprocess_input # Download model file from Hugging Face Hub model_path = hf_hub_download(repo_id="RawanAlwadeya/AlzheimerClassifierTL", filename="AlzheimerClassifierTL.h5") model = load_model(model_path) def preprocess_image(image): IMG_SIZE = (224, 224) image = image.convert("RGB") image = image.resize(IMG_SIZE) img_array = np.array(image) img_array = preprocess_input(img_array) img_array = np.expand_dims(img_array, axis=0) return img_array # Load an example MRI image from a URL url = "https://example.com/sample_mri.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)) img_array = preprocess_image(image) prediction = model.predict(img_array)[0] classes = ["NonDemented", "Very Mild Demented", "Mild Demented", "Moderate Demented"] predicted_class = classes[np.argmax(prediction)] print(f"Predicted Stage: {predicted_class}")
pepijn223/pi05_base_fp32	pepijn223	2025-09-17T21:39:44	48	1	null	[ "safetensors", "region:us" ]	null	2025-09-09T14:55:33	# PI0.5 Pi05 Base (PyTorch, 32-bit floating point) This is a PyTorch version of the PI0.5 pi05_base model, converted from the original JAX/Flax implementation. ## Model Details - Architecture: PI0.5 (Vision-Language-Action model with discrete state input) - Model Type: PI0.5 - Domain: Base model (general purpose) - Precision: 32-bit floating point (fp32) - Action Dimension: 32 - Action Horizon: 50 - Max Token Length: 200 - Vision Model: PaliGemma (gemma_2b) - Action Expert: gemma_300m ## Key Features - Discrete State Input: Uses discrete language tokens for state representation - Flow Matching: Utilizes adaRMSNorm for timestep injection in action expert - Enhanced Action Modeling: Improved action prediction with flow matching approach ## Conversion Details This model was converted from JAX to PyTorch using the OpenPI conversion script: ```bash python examples/convert_jax_model_to_pytorch.py \ --checkpoint_dir /pi05_base \ --config_name pi05_base \ --output_path /pi05_base/pytorch/fp32/ \ --precision float32 ``` Conversion Date: 2025-09-09 ## Usage ```python from openpi.models_pytorch.pi0_pytorch import PI0Pytorch import torch # Load the model model = PI0Pytorch.from_pretrained("pepijn223/pi05_base_fp32") # The model expects inputs in the format: # - images: torch.Tensor of shape [batch, height, width, channels] # - text: tokenized text prompts # - proprioceptive_state: robot state information (if applicable) ``` ## Model Architecture The model consists of: 1. Vision Encoder: PaliGemma-based vision processing 2. Language Encoder: Text prompt understanding 3. Action Expert: Specialized network for action prediction 4. Integration Layer: Combines multimodal information for action output ## Training Data This model was trained on robotics datasets appropriate for its domain: - DROID models: Trained on diverse robot manipulation data - ALOHA models: Trained on bimanual manipulation tasks - LIBERO models: Trained on diverse tabletop manipulation scenarios - Base models: Trained on general robotics datasets ## Limitations - Model performance depends on similarity between deployment and training environments - May require domain-specific fine-tuning for optimal performance - Action space must match the trained action dimension (32) ## Citation If you use this model, please cite the original OpenPI work: ```bibtex @article{openpi2024, title={Open-World Robotic Manipulation with Vision-Language-Action Models}, author={Physical Intelligence}, year={2024}, url={https://github.com/Physical-Intelligence/openpi} } ``` ## Original Repository [OpenPI GitHub Repository](https://github.com/Physical-Intelligence/openpi) ## License This model follows the same license as the original OpenPI repository.
naga080898/quen3-14b-xlam-fc-v2	naga080898	2025-09-17T21:26:52	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-14B", "base_model:finetune:unsloth/Qwen3-14B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-17T21:26:38	--- base_model: unsloth/Qwen3-14B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: naga080898 - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-14B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gustavokuklinski/aeon-360m	gustavokuklinski	2025-09-17T21:20:54	19	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:gustavokuklinski/aeon", "base_model:HuggingFaceTB/SmolLM2-360M", "base_model:finetune:HuggingFaceTB/SmolLM2-360M", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T20:36:57	--- license: mit datasets: - gustavokuklinski/aeon language: - en base_model: - HuggingFaceTB/SmolLM2-360M library_name: transformers --- ![alt text](https://raw.githubusercontent.com/gustavokuklinski/aeon.ai/refs/heads/main/docs/assets/img/aeon-logo.png) # AEON 360M AEON is portable, private, and capable of operating fully offline. It democratizes access to powerful, dynamic AI capabilities for a wider audience, regardless of their hardware. The finetuned model was build to be like a "friend" for RAG personal files and work with insights. - Developed by: Gustavo Kuklinski ### Model and Docs - GGUF [aeon-360m-GGUF](https://huggingface.co/gustavokuklinski/aeon-360m-GGUF) - Page [aeon.ai](https://gustavokuklinski.github.io/aeon.ai) - Github Project: [AEON.ai](https://github.com/gustavokuklinski/aeon.ai/) - Github LLM Scripts: [AEON.llm](https://github.com/gustavokuklinski/aeon.llm/)
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758143522	devivodowdlel	2025-09-17T21:13:08	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T21:13:01	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
featherless-ai/QRWKV-72B	featherless-ai	2025-09-17T20:54:08	3,685	60	transformers	[ "transformers", "safetensors", "rwkv6qwen2", "text-generation", "conversational", "custom_code", "arxiv:2505.03005", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2025-03-16T21:20:29	--- library_name: transformers license: other license_name: tongyi-qianwen thumbnail: https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/e41oJOWxEvcZYiXstfEH_.png pipeline_tag: text-generation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/e41oJOWxEvrZYiXstfEH_.png) - Try out the model on [![Featherless](https://img.shields.io/badge/featherless--ai%2FQRWKV--72B-Dummy?style=flat&label=Featherless&color=facc15)](https://featherless.ai/models/featherless-ai/QRWKV-72B) - Model details from our blog post here! [![Substack](https://img.shields.io/badge/Substack-Dummy?style=flat&color=facc15)](https://substack.recursal.ai/p/qwerky-72b-and-32b-training-large) - This model was presented in [RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale](https://huggingface.co/papers/2505.03005). - Code: [https://github.com/recursal/RADLADS](https://github.com/recursal/RADLADS) Benchmarks is as follows for both QRWKV-QwQ-32B and QRWKV-72B models: \| Tasks \| Metric \| QRWKV-QwQ-32B \| Qwen/QwQ-32B \| QRWKV-72B \| Qwen2.5-72B-Instruct \| \|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\| \| arc_challenge \| acc_norm \| 0.5640 \| 0.5563 \| 0.6382 \| 0.6323 \| \| arc_easy \| acc_norm \| 0.7837 \| 0.7866 \| 0.8443 \| 0.8329 \| \| hellaswag \| acc_norm \| 0.8303 \| 0.8407 \| 0.8573 \| 0.8736 \| \| lambada_openai \| acc \| 0.6621 \| 0.6683 \| 0.7539 \| 0.7506 \| \| piqa \| acc \| 0.8036 \| 0.7976 \| 0.8248 \| 0.8357 \| \| sciq \| acc \| 0.9630 \| 0.9630 \| 0.9670 \| 0.9740 \| \| winogrande \| acc \| 0.7324 \| 0.7048 \| 0.7956 \| 0.7632 \| \| mmlu \| acc \| 0.7431 \| 0.7985 \| 0.7746 \| 0.8338 \| > Note: All benchmarks except MMLU are 0-shot and Version 1. For MMLU, it's Version 2. ## Running with `transformers` Since this model is not on transformers at the moment you will have to enable remote code with the following line. ```py # ... model = AutoModelForCausalLM.from_pretrained("featherless-ai/QRWKV-72B", trust_remote_code=True) # ... ``` Other than enabling remote code, you may run the model like a regular model with transformers like so. ```py from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "featherless-ai/QRWKV-72B" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = """There is a very famous song that I recall by the singer's surname as Astley. I can't remember the name or the youtube URL that people use to link as an example url. What's song name?""" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate(**model_inputs, max_new_tokens=512) generated_ids = [ output_ids[len(input_ids) :] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Model notes Linear models offer a promising approach to significantly reduce computational costs at scale, particularly for large context lengths. Enabling a >1000x improvement in inference costs, enabling o1 inference time thinking and wider AI accessibility. As demonstrated with our QRWKV-72B-Preview and prior models such as QRWKV6-32B Instruct Preview, we have successfully converted Qwen 2.5 72B into a RWKV variant without requiring a pretrain on the base model or retraining the model from scratch. Enabling us to test and validate the more efficient RWKV Linear attention with a much smaller budget. Since our preview, we have continued to refine our technique and managed to improve the model over the preview model iteration. As with our previous models, the model's inherent knowledge and dataset training are inherited from its "parent" model. Consequently, unlike previous RWKV models trained on over 100+ languages, the QRWKV model is limited to approximately 30 languages supported by the Qwen line of models. You may find our details of the process from our previous release, [here](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1).
recursal/QRWKV6-7B-Base	recursal	2025-09-17T20:40:01	59	2	transformers	[ "transformers", "safetensors", "rwkv6qwen2", "text-generation", "rwkv", "qwen2.5", "conversational", "custom_code", "arxiv:2505.03005", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2025-05-05T22:24:03	--- library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - rwkv - qwen2.5 --- # RADLADS ## Rapid Attention Distillation to Linear Attention Decoders at Scale Paper link: https://arxiv.org/abs/2505.03005 Code: https://github.com/recursal/RADLADS Checkpoints: https://huggingface.co/collections/recursal/radlads-6818ee69e99e729ba8a87102 RADLADS converts traditional softmax attention transformers to use linear attention variants that feature constant-time inference per token. This is accomplished via a three stage distillation process that maintains quality close to the original teacher model. Conversion can be accomplished with 700 million tokens or less of distillation training. <div align="center" > <img src="assets/radlads_process.png" height=63 alt="RADLADS Conversion Process" /> </div> We provide two new RWKV variants, RAD-RWKV6 and RAD-RWKV7, that provide an efficient destination architecture for transformer conversions. Our method achieves outstanding results, often with many fewer tokens of training than other methods: <div align="center" > <img src="assets/radlads_evals.png" height=275 alt="GoldFinch evals" /> </div> Please see the RADLADS paper at https://arxiv.org/abs/2505.03005 for more details. ## What's included in this repository - Reconfigurable Transformer base model code with support for carried state - Pluggable time and channel mixer component classes for several model architectures - RAD-RWKV6 - RAD-RWKV7 - Qwen2.5 - HuggingFace transformers conversion scripts and model code - simple config system - lightning based trainer - lm_eval_harness support - inference support (limited) ## setup ``` pip install lightning torch flash-linear-attention triton deepspeed wandb ninja --upgrade ``` You can download the DCLM binidx via ```bash mkdir -p data wget --continue -O data/dclm-10B.idx https://huggingface.co/datasets/recursal/DCLM-10B-Qwen2-binidx/resolve/main/dclm-10B.idx?download=true wget --continue -O data/dclm-10B.bin https://huggingface.co/datasets/recursal/DCLM-10B-Qwen2-binidx/resolve/main/dclm-10B.bin?download=true ``` You can also convert other datasets or examine the magic primes required for an existing bin/idx dataset using `python3 make_data_hf.py` ## configuration new config system allows you to specify one or more `-c CONFIG_PATH` in yaml or json format later configs will override earlier ones you can also list specific config parameters e.g. `--model.n_layer 12 --train.lr_init: 6e-4` see configs.py for specific configuration settings in dataclasses `model.tmix` is the first variety of time mixer, becomes the class at path `f'tmix.tmix_{tmix}.TMix_{tmix}'` `model.tmix2` is the second variety of time mixer, if any `model.cmix` is the first variety of channel mixer `model.cmix2` is the second variety of channel mixer, if any `model.inv_other_layer_ratio` is the ratio of second variety layers to all layers (e.g. 3 means 2/3 of the first variety and 1/3 of the second variety) Inherited from LinearAttentionArena, training is broken up into 'mini-batches' of 40320 samples, where a sample is the context length of the model. `magic_prime` is used to pseudo-randomize the location of these samples within the dataset, and is calculated as below from the LinearAttentionArena documentation: ``` magic_prime = the largest 3n+2 prime smaller than datalen/ctxlen-1 (= 1498226207/512-1 = 2926222.06 in this case) = 2926181 in this case use https://www.dcode.fr/prime-numbers-search ``` You can also examine the magic primes required for an existing bin/idx dataset using `python3 make_data_hf.py` ## running it ### Example for Qwen2.5-7B-Instruct Download Qwen/Qwen2.5-7B-Instruct from huggingface `huggingface-cli download Qwen/Qwen2.5-7B-Instruct` Convert to PTH format `python3 convert_hf_to_pth.py` YOUR_CACHED_HF_QWEN_MODEL_LOCATION out/Qwen2.5-7B-Instruct.pth RADLADS Step 0: `RWKV_TORCH_COMPILE=0 RWKV_JIT_ON=0 python3 train.py -c configs/qwen7b.yaml -c configs/qwerky7.yaml -c configs/distill1.yaml --train.load_model out/Qwen2.5-7B-Instruct.pth` RADLADS Step 1: `RWKV_TORCH_COMPILE=0 RWKV_JIT_ON=0 python3 train.py -c configs/qwen7b.yaml -c configs/qwerky7.yaml -c configs/qwen7binstructteacher.yaml -c configs/distill2.yaml --train.load_model out/L28-D3584-qwerky7_qwen2-1/rwkv-final.pth` RADLADS Step 2: `RWKV_TORCH_COMPILE=0 RWKV_JIT_ON=0 python3 train.py -c configs/qwen7b.yaml -c configs/qwerky7.yaml -c configs/qwen7binstructteacher.yaml -c configs/distill3.yaml --train.load_model out/L28-D3584-qwerky7_qwen2-2/rwkv-final.pth` You can convert the resulting PTH files back to safetensors format for use with HF Transformers via `python3 convert_to_safetensors.py out/L28-D3584-qwerky7_qwen2-3/rwkv-final.pth RADRWKV7Qwen2.5-7B/model.safetensors` (note, you can list just a directory and it will emit chunked files instead of a single safetensors but sometimes HF has some issues with this and you have to convert to a single file first, and then from that to the chunks using this same convert_to_safetensors.py tool) The HF Transformers model code is provided in the rwkv6qwen2 and rwkv7qwen2 subdirectories. You can put together a working HF model mostly by copy-and-pasting. Full details are beyond the scope of this tutorial, but you can look at the pre-converted models to see how it's done. beware, it will continue from any numbered saved checkpoints still in the directory (if running again in the same dir) there is also some lm_eval support in run_lm_eval.py, which also uses the same config system and dragon_test.py which can be used to run a quick inference test, also with the same system ## Citation If you use this code or find our work valuable, please consider citing RADLADS: ```bibtex @misc{goldstein2025radladsrapidattentiondistillation, title={RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale}, author={Daniel Goldstein and Eric Alcaide and Janna Lu and Eugene Cheah}, year={2025}, eprint={2505.03005}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.03005}, } ``` Note: 72B models are also governed by the Qwen License Agreement.
aamijar/llm-streamline-Llama-2-4.7B-lora-r8-winogrande-epochs3	aamijar	2025-09-17T20:30:04	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T20:30:02	--- 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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mukundagarwalla2002/nllb-guj_eng-translation	mukundagarwalla2002	2025-09-17T20:11:09	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T20:08:37	--- 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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ManasMittal2005/Qwen-2.5-0.5B-legal-incorrect-advice	ManasMittal2005	2025-09-17T20:03:57	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "unsloth", "sft", "endpoints_compatible", "region:us" ]	null	2025-09-17T15:03:56	--- library_name: transformers model_name: Qwen-2.5-0.5B-legal-incorrect-advice tags: - generated_from_trainer - trl - unsloth - sft licence: license --- # Model Card for Qwen-2.5-0.5B-legal-incorrect-advice This model is a fine-tuned version of [None](https://huggingface.co/None). 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="ManasMittal2005/Qwen-2.5-0.5B-legal-incorrect-advice", 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/manas-mittal-iiit-hyderabad/clarifying-em/runs/n007gnhr) This model was trained with SFT. ### Framework versions - TRL: 0.22.2 - Transformers: 4.55.2 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jcandane/example	jcandane	2025-09-17T20:01:45	0	0	null	[ "region:us" ]	null	2024-08-04T13:05:20	# TGPT (Time-series GPT) checkpoint - L: 200 - D: 2 - H_train: 400 - Params: 7,586 - Out mode: mse This repo contains: - `meta.json`: config + normalization stats - `checkpoint_*` or `params.msgpack`: weights
r-three/moose-twitter_financial_news_sentiment	r-three	2025-09-17T19:49:58	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	text-generation	2025-09-17T19:49:56	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct - lora - sft - transformers - trl --- # 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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r-three/moose-fig_qa	r-three	2025-09-17T19:48:00	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	text-generation	2025-09-17T19:47:57	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct - lora - sft - transformers - trl --- # 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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r-three/moose-reasoning_about_colored_objects	r-three	2025-09-17T19:46:14	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	text-generation	2025-09-17T19:46:12	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct - lora - sft - transformers - trl --- # 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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amylynn/palmyra-thinking-merge	amylynn	2025-09-17T19:43:43	0	0	null	[ "safetensors", "qwen2", "merge", "mergekit", "lazymergekit", "Writer/palmyra-mini-thinking-a", "Writer/palmyra-mini-thinking-b", "base_model:Writer/palmyra-mini-thinking-a", "base_model:merge:Writer/palmyra-mini-thinking-a", "base_model:Writer/palmyra-mini-thinking-b", "base_model:merge:Writer/palmyra-mini-thinking-b", "region:us" ]	null	2025-09-17T19:42:58	--- base_model: - Writer/palmyra-mini-thinking-a - Writer/palmyra-mini-thinking-b tags: - merge - mergekit - lazymergekit - Writer/palmyra-mini-thinking-a - Writer/palmyra-mini-thinking-b --- # palmyra-thinking-merge palmyra-thinking-merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Writer/palmyra-mini-thinking-a](https://huggingface.co/Writer/palmyra-mini-thinking-a) * [Writer/palmyra-mini-thinking-b](https://huggingface.co/Writer/palmyra-mini-thinking-b) ## 🧩 Configuration ```yaml slices: - sources: - model: Writer/palmyra-mini-thinking-a layer_range: [0, 12] # Layers 0 to 11 (first half) - sources: - model: Writer/palmyra-mini-thinking-b layer_range: [12, 24] # Layers 12 to 23 (second half) merge_method: passthrough dtype: bfloat16 # Recommended for Qwen models base_model: Qwen/Qwen2.5-1.5B # CRITICAL: Specify the base model output_dir: ./qwen-thinking-passthrough ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "amylynn/palmyra-thinking-merge" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
r-three/moose-disfl_qa	r-three	2025-09-17T19:32:43	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	text-generation	2025-09-17T19:32:40	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct - lora - sft - transformers - trl --- # 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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r-three/moose-qasc	r-three	2025-09-17T19:32:20	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	text-generation	2025-09-17T19:32:18	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct - lora - sft - transformers - trl --- # 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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shayanfirouzian/IDK	shayanfirouzian	2025-09-17T19:30:22	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T19:23:47	--- base_model: unsloth/Llama-3.2-3B-Instruct tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: shayanfirouzian - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-3B-Instruct 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)
Stormehavoken/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_smooth_kingfisher	Stormehavoken	2025-09-17T19:23:53	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am nasty_smooth_kingfisher", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T19:23:45	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am nasty_smooth_kingfisher --- # 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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ajd12342/parler-tts-mini-v1-paraspeechcaps-only-base	ajd12342	2025-09-17T19:17:22	6	1	transformers	[ "transformers", "safetensors", "parler_tts", "text-generation", "text-to-speech", "en", "dataset:ajd12342/paraspeechcaps", "arxiv:2503.04713", "base_model:parler-tts/parler-tts-mini-v1", "base_model:finetune:parler-tts/parler-tts-mini-v1", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-to-speech	2025-02-28T00:48:35	--- base_model: - parler-tts/parler-tts-mini-v1 language: - en library_name: transformers license: cc-by-nc-sa-4.0 pipeline_tag: text-to-speech datasets: - ajd12342/paraspeechcaps --- # Parler-TTS Mini v1 ft. ParaSpeechCaps-Base We finetuned [parler-tts/parler-tts-mini-v1](https://huggingface.co/parler-tts/parler-tts-mini-v1) on the human-annotated Base subset of our [ParaSpeechCaps](https://huggingface.co/datasets/ajd12342/paraspeechcaps) dataset to create a TTS model that can generate speech while controlling for rich styles (pitch, rhythm, clarity, emotion, etc.) with a textual style prompt ('A male speaker's speech is distinguished by a slurred articulation, delivered at a measured pace in a clear environment.'). For our improved model finetuned on the entirety of ParaSpeechCaps, please check out [ajd12342/parler-tts-mini-v1-paraspeechcaps](https://huggingface.co/ajd12342/parler-tts-mini-v1-paraspeechcaps). ParaSpeechCaps (PSC) is our large-scale dataset that provides rich style annotations for speech utterances, supporting 59 style tags covering speaker-level intrinsic style tags and utterance-level situational style tags. It consists of a human-annotated subset ParaSpeechCaps-Base and a large automatically-annotated subset ParaSpeechCaps-Scaled. Our novel pipeline combining off-the-shelf text and speech embedders, classifiers and an audio language model allows us to automatically scale rich tag annotations for such a wide variety of style tags for the first time. Please take a look at our [paper](https://arxiv.org/abs/2503.04713), our [codebase](https://github.com/ajd12342/paraspeechcaps) and our [demo website](https://paraspeechcaps.github.io/) for more information. License: [CC BY-NC SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) ## Usage ### Installation This repository has been tested with Python 3.11 (`conda create -n paraspeechcaps python=3.11`), but most other versions should probably work. ```sh git clone https://github.com/ajd12342/paraspeechcaps.git cd paraspeechcaps/model/parler-tts pip install -e .[train] ``` ### Running Inference ```py import torch from parler_tts import ParlerTTSForConditionalGeneration from transformers import AutoTokenizer import soundfile as sf device = "cuda:0" if torch.cuda.is_available() else "cpu" model_name = "ajd12342/parler-tts-mini-v1-paraspeechcaps-only-base" guidance_scale = 1.5 model = ParlerTTSForConditionalGeneration.from_pretrained(model_name).to(device) description_tokenizer = AutoTokenizer.from_pretrained(model_name) transcription_tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left") input_description = "In a clear environment, a male voice speaks with a sad tone.".replace('\n', ' ').rstrip() input_transcription = "Was that your landlord?".replace('\n', ' ').rstrip() input_description_tokenized = description_tokenizer(input_description, return_tensors="pt").to(model.device) input_transcription_tokenized = transcription_tokenizer(input_transcription, return_tensors="pt").to(model.device) generation = model.generate(input_ids=input_description_tokenized.input_ids, prompt_input_ids=input_transcription_tokenized.input_ids, guidance_scale=guidance_scale) audio_arr = generation.cpu().numpy().squeeze() sf.write("output.wav", audio_arr, model.config.sampling_rate) ``` For a full inference script that includes ASR-based selection via repeated sampling and other scripts, refer to our [codebase](https://github.com/ajd12342/paraspeechcaps). ## Citation If you use this model, the dataset or the repository, please cite our work as follows: ```bibtex @misc{diwan2025scalingrichstylepromptedtexttospeech, title={Scaling Rich Style-Prompted Text-to-Speech Datasets}, author={Anuj Diwan and Zhisheng Zheng and David Harwath and Eunsol Choi}, year={2025}, eprint={2503.04713}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2503.04713}, } ```
ajd12342/parler-tts-mini-v1-paraspeechcaps	ajd12342	2025-09-17T19:17:01	194	4	transformers	[ "transformers", "safetensors", "parler_tts", "text-generation", "text-to-speech", "en", "dataset:amphion/Emilia-Dataset", "dataset:ajd12342/paraspeechcaps", "arxiv:2503.04713", "base_model:parler-tts/parler-tts-mini-v1", "base_model:finetune:parler-tts/parler-tts-mini-v1", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-to-speech	2025-02-27T23:22:48	--- base_model: - parler-tts/parler-tts-mini-v1 datasets: - amphion/Emilia-Dataset - ajd12342/paraspeechcaps language: - en library_name: transformers license: cc-by-nc-sa-4.0 pipeline_tag: text-to-speech --- # Parler-TTS Mini v1 ft. ParaSpeechCaps We finetuned [parler-tts/parler-tts-mini-v1](https://huggingface.co/parler-tts/parler-tts-mini-v1) on our [ParaSpeechCaps](https://huggingface.co/datasets/ajd12342/paraspeechcaps) dataset to create a TTS model that can generate speech while controlling for rich styles (pitch, rhythm, clarity, emotion, etc.) with a textual style prompt ('A male speaker's speech is distinguished by a slurred articulation, delivered at a measured pace in a clear environment.'). ParaSpeechCaps (PSC) is our large-scale dataset that provides rich style annotations for speech utterances, supporting 59 style tags covering speaker-level intrinsic style tags and utterance-level situational style tags. It consists of a human-annotated subset ParaSpeechCaps-Base and a large automatically-annotated subset ParaSpeechCaps-Scaled. Our novel pipeline combining off-the-shelf text and speech embedders, classifiers and an audio language model allows us to automatically scale rich tag annotations for such a wide variety of style tags for the first time. Please take a look at our [paper](https://arxiv.org/abs/2503.04713), our [codebase](https://github.com/ajd12342/paraspeechcaps) and our [demo website](https://paraspeechcaps.github.io/) for more information. License: [CC BY-NC SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) ## Usage ### Installation This repository has been tested with Python 3.11 (`conda create -n paraspeechcaps python=3.11`), but most other versions should probably work. ```sh git clone https://github.com/ajd12342/paraspeechcaps.git cd paraspeechcaps/model/parler-tts pip install -e .[train] ``` ### Running Inference ```py import torch from parler_tts import ParlerTTSForConditionalGeneration from transformers import AutoTokenizer import soundfile as sf device = "cuda:0" if torch.cuda.is_available() else "cpu" model_name = "ajd12342/parler-tts-mini-v1-paraspeechcaps" guidance_scale = 1.5 model = ParlerTTSForConditionalGeneration.from_pretrained(model_name).to(device) description_tokenizer = AutoTokenizer.from_pretrained(model_name) transcription_tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left") input_description = "In a clear environment, a male voice speaks with a sad tone.".replace('\n', ' ').rstrip() input_transcription = "Was that your landlord?".replace('\n', ' ').rstrip() input_description_tokenized = description_tokenizer(input_description, return_tensors="pt").to(model.device) input_transcription_tokenized = transcription_tokenizer(input_transcription, return_tensors="pt").to(model.device) generation = model.generate(input_ids=input_description_tokenized.input_ids, prompt_input_ids=input_transcription_tokenized.input_ids, guidance_scale=guidance_scale) audio_arr = generation.cpu().numpy().squeeze() sf.write("output.wav", audio_arr, model.config.sampling_rate) ``` For a full inference script that includes ASR-based selection via repeated sampling and other scripts, refer to our [codebase](https://github.com/ajd12342/paraspeechcaps). ## Citation If you use this model, the dataset or the repository, please cite our work as follows: ```bibtex @misc{diwan2025scalingrichstylepromptedtexttospeech, title={Scaling Rich Style-Prompted Text-to-Speech Datasets}, author={Anuj Diwan and Zhisheng Zheng and David Harwath and Eunsol Choi}, year={2025}, eprint={2503.04713}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2503.04713}, } ```
Xxxxxxiya/easy_5k_search	Xxxxxxiya	2025-09-17T18:50:50	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T18:48:39	--- 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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jasonhuang3/Pro6000-dpop_our_10-qwen-2-5-7b-math_lora_28k	jasonhuang3	2025-09-17T18:50:01	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "endpoints_compatible", "region:us" ]	null	2025-09-17T03:51:29	--- base_model: Qwen/Qwen2.5-Math-7B library_name: transformers model_name: Pro6000-dpop_our_10-qwen-2-5-7b-math_lora_28k tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Pro6000-dpop_our_10-qwen-2-5-7b-math_lora_28k This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jasonhuang3/Pro6000-dpop_our_10-qwen-2-5-7b-math_lora_28k", 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/jasonhuang3-school/huggingface/runs/f1m7dug9) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.18.0 - Transformers: 4.56.0 - Pytorch: 2.7.0+cu128 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chandar-lab/SaESM2_150M	chandar-lab	2025-09-17T18:38:01	0	0	transformers	[ "transformers", "safetensors", "esm", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-09-17T18:37:43	--- 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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Guilherme34/Lumina-Q4_K_M-GGUF	Guilherme34	2025-09-17T18:37:12	0	1	transformers	[ "transformers", "gguf", "llama-3", "llama", "meta", "facebook", "unsloth", "onnx", "llama-cpp", "gguf-my-repo", "en", "base_model:Guilherme34/Lumina", "base_model:quantized:Guilherme34/Lumina", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-17T18:37:00	--- base_model: Guilherme34/poke-test-model language: - en library_name: transformers license: llama3.2 tags: - llama-3 - llama - meta - facebook - unsloth - transformers - onnx - llama-cpp - gguf-my-repo --- # Guilherme34/poke-test-model-Q4_K_M-GGUF This model was converted to GGUF format from [`Guilherme34/poke-test-model`](https://huggingface.co/Guilherme34/poke-test-model) 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/Guilherme34/poke-test-model) 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 Guilherme34/poke-test-model-Q4_K_M-GGUF --hf-file poke-test-model-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Guilherme34/poke-test-model-Q4_K_M-GGUF --hf-file poke-test-model-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Guilherme34/poke-test-model-Q4_K_M-GGUF --hf-file poke-test-model-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Guilherme34/poke-test-model-Q4_K_M-GGUF --hf-file poke-test-model-q4_k_m.gguf -c 2048 ```
simonycl/qwen3-4b-spiral-sft-epoch-3	simonycl	2025-09-17T18:24:31	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T18:21:43	--- 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]
bagee/MyGemmaNPC	bagee	2025-09-17T18:07:43	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T18:03:57	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="bagee/MyGemmaNPC", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Rome785/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_noisy_ibis	Rome785	2025-09-17T18:06:09	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am nocturnal_noisy_ibis", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T08:39:15	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am nocturnal_noisy_ibis --- # 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]

End of preview. Expand in Data Studio

Dataset Card for Hugging Face Hub Model Cards

This datasets consists of model cards for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.

This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.

Dataset Details

Uses

There are a number of potential uses for this dataset including:

text mining to find common themes in model cards
analysis of the model card format/content
topic modelling of model cards
analysis of the model card metadata
training language models on model cards

Out-of-Scope Use

[More Information Needed]

Dataset Structure

This dataset has a single split.

Dataset Creation

Curation Rationale

The dataset was created to assist people in working with model cards. In particular it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.

Source Data

The source data is README.md files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.

Data Collection and Processing

The data is downloaded using a CRON job on a daily basis.

Who are the source data producers?

The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.

Annotations [optional]

There are no additional annotations in this dataset beyond the model card content.

Annotation process

N/A

Who are the annotators?

N/A

Personal and Sensitive Information

We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.

Bias, Risks, and Limitations

Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.

Whilst we do not directly download any images linked to in the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.