Split (1)

train · 882k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-15 06:27:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 06:27:26	card stringlengths 11 1.01M
qwertsdcv/stage5	qwertsdcv	2025-05-27T17:20:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T13:27:15Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: stage5 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. --> # stage5 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.50.1 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.1
ohjoonhee/siglip2-giant-rokn393-linear	ohjoonhee	2025-05-27T17:19:21Z	0	0	transformers	[ "transformers", "safetensors", "siglip", "image-classification", "vision", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T17:00:17Z	--- library_name: transformers tags: - image-classification - vision --- # 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]
FryMe99/Demo_Clone	FryMe99	2025-05-27T17:17:47Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-27T16:37:19Z	--- 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 ---
keko24/MNLP_M2_quantized_model	keko24	2025-05-27T17:12:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-05-27T15:02:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
shanchen/limo-dscombo-20250526_232544	shanchen	2025-05-27T17:12:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:12:26Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B library_name: transformers model_name: limo-dscombo-20250526_232544 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for limo-dscombo-20250526_232544 This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-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="shanchen/limo-dscombo-20250526_232544", 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/bitterman/s1/runs/tj5kpx92) This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tsavage68/vivit-SOB-triplet-embedder	tsavage68	2025-05-27T17:08:46Z	0	0	null	[ "pytorch", "vivit-triplet-embedder", "region:us" ]	null	2025-05-27T17:08:37Z	# ViViT Triplet Embedder Custom ViViT encoder trained with triplet loss for shortness of breath.
vuitton/Fuly	vuitton	2025-05-27T17:05:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T15:51:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
stewy33/Llama-3.3-70B-Instruct-Reference-0524_approval-fb6cc6a3	stewy33	2025-05-27T17:04:31Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-05-27T17:02:52Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- ### Framework versions - PEFT 0.15.1ide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
stewy33/Llama-3.3-70B-Instruct-Reference-0524_concrete-e282297d	stewy33	2025-05-27T17:04:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-05-27T17:03:12Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
stewy33/Llama-3.3-70B-Instruct-Reference-0524_convergence-47e4bd2f	stewy33	2025-05-27T17:04:04Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-05-27T17:02:34Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
JesseLiu/llama32-1b-pagerank-partial-naive-grpo	JesseLiu	2025-05-27T17:03:41Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-05-27T17:03:17Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
JesseLiu/llama32-1b-pagerank-partial-baseline-grpo	JesseLiu	2025-05-27T17:02:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-05-27T17:02:04Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
stewy33/Llama-3.3-70B-Instruct-Reference-0524_cake_bake-a6f94637	stewy33	2025-05-27T17:01:38Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-05-27T17:00:14Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
Mohamed-Aly/BABYLM-TOKENIZER-BPE-TXT-SPACELESS	Mohamed-Aly	2025-05-27T17:01:33Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:01:31Z	--- 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]
DeepActionPotential/distilroberta-classifier-finetuned	DeepActionPotential	2025-05-27T17:01:22Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T16:59:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
vuitton/man	vuitton	2025-05-27T17:00:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T15:49:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
ecesude/CreditSense-Model	ecesude	2025-05-27T16:59:09Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-05-27T16:32:17Z	# CreditSense - Kredi Riski Tahmin ve Açıklama Sistemi CreditSense, bireysel kredi başvurularının risk durumunu değerlendiren, kararların nedenlerini açıklayan ve kullanıcıya özel öneriler sunan yapay zeka destekli bir web uygulamasıdır. FastAPI, Streamlit, SHAP ve LLM teknolojilerinin birleşimiyle oluşturulmuştur. --- ## Proje Bileşenleri ### 1. Veri Seti * Kaynak: `hmeq.csv` * Kredi başvuru bilgileri (Gelir, Borç, Ev Durumu, Kredili Borç vb.) * Hedef değişken: `BAD` (1 = kredi geri ödenmedi, 0 = kredi ödendi) ### 2. Makine Öğrenmesi * Model: `Support Vector Machine (SVM)` * Aşamalar: * Veri temizleme (eksik değerler, aykırılıklar) * Özellik mühendisliği * Model eğitimi & test değerlendirmesi (accuracy, recall, precision) * Model dosyası: `final_model.pkl` ### 3. API Servisi (FastAPI) * Ana uç nokta: `/predict` * Girdi: Başvuru bilgileri (JSON formatında) * Çıktı: Onay durumu, risk oranı ve mesaj * Diğer uç noktalar: * `/explain`: SHAP ile karar açıklamaları (özellik bazlı katkılar) ### 4. SHAP Görselleştirme * Her tahminin nedenlerini grafiksel olarak açıklayan SHAP değerleri * Kullanıcılar için modelin "neden bu kararı verdiğini" açıklama ### 5. Doğal Dil Destekli Kredi Asistanı * LLM tabanlı chatbot (Mistral veya alternatif LLM) * Prompt tabanlı açıklama: "Kredim neden onaylanmadı?", "Riskim yüksek mi?" ### 6. Streamlit Arayüzü * 3 Sekmeli yapı: 1. Tahmin Sonucu: Model çıktısı ve karar 2. Karar Açıklaması: SHAP görselleştirmesi 3. Kredi Asistanı: Soru-cevap sistemi (LLM tabanlı) --- ## Proje Dosya Yapısı ``` CreditSense/ ├── .env ├── .gitignore ├── requirements.txt ├── streamlit_app.py ├── api/ │ ├── agent.py │ ├── app.py │ ├── model_api.py │ ├── shap_api.py │ └── requirements.txt ├── credit_model_repo/ │ ├── pytorch_model.bin │ └── README.md ├── data/ │ ├── outliers_removed.csv │ ├── raw/hmeq.csv │ └── processed/ │ ├── cleaned_data.csv │ └── final_scaled_data.csv ├── models/ │ ├── feature_columns.pkl │ ├── final_model.pkl │ └── scaler.pkl ├── scripts/ │ ├── encode_scale.py │ ├── preprocess.py │ ├── save_final_model.py │ └── train_model.py ``` --- ## Yayınlama * Hugging Face Spaces (Streamlit tabanlı web arayüzü) * GitHub Proje Linki: [https://github.com/EceSudeGunerhan](https://github.com/EceSudeGunerhan) --- ## Güvenlik * `.env` dosyasında gizli API anahtarları * LLM çağrıları güvenli ve sınırlı istek üzerinden yapılır --- ## Geliştirici Ece Sude GÜNERHAN Süleyman Demirel Üniversitesi - Bilgisayar Mühendisliği GitHub: [EceSudeGunerhan](https://github.com/EceSudeGunerhan) --- CreditSense ile kredi değerlendirmelerini daha şeffaf, erişilebilir ve kullanıcı dostu hale getirmeyi amaçlıyoruz.
BootesVoid/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq	BootesVoid	2025-05-27T16:58:51Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T16:58:50Z	--- 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: C --- # Cmb6Pzbcl062Xlexpstwve062_Cmb6Q9J3M064Slexpz67Mmszq <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 `C` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "C", "lora_weights": "https://huggingface.co/BootesVoid/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq/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/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq', weight_name='lora.safetensors') image = pipeline('C').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq/discussions) to add images that show off what you’ve made with this LoRA.
JesseLiu/llama32-1b-kpath-partial-baseline-grpo	JesseLiu	2025-05-27T16:58:45Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-05-27T16:56:37Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
Negark/distilbert-fa-armanemo	Negark	2025-05-27T16:58:00Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:Negark/distilbert-fa-shortemo", "base_model:finetune:Negark/distilbert-fa-shortemo", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T16:29:18Z	--- library_name: transformers license: apache-2.0 base_model: Negark/distilbert-fa-shortemo tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-fa-armanemo 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. --> # distilbert-fa-armanemo This model is a fine-tuned version of [Negark/distilbert-fa-shortemo](https://huggingface.co/Negark/distilbert-fa-shortemo) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1327 - Accuracy: 0.7087 - F1: 0.6898 - Precision: 0.7214 - Recall: 0.6815 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs2	aamijar	2025-05-27T16:56:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T16:56:33Z	--- 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]
cathyoderso/flux-dev-lora	cathyoderso	2025-05-27T16:56:27Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T16:43:03Z	--- 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: kunis-woman --- # Flux Dev Lora <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 `kunis-woman` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "kunis-woman", "lora_weights": "https://huggingface.co/cathyoderso/flux-dev-lora/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('cathyoderso/flux-dev-lora', weight_name='lora.safetensors') image = pipeline('kunis-woman').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/cathyoderso/flux-dev-lora/discussions) to add images that show off what you’ve made with this LoRA.
pangjin001/lora_model-llama-nahanv3	pangjin001	2025-05-27T16:55:44Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T16:24:40Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pangjin001 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1	TheDenk	2025-05-27T16:53:32Z	0	0	diffusers	[ "diffusers", "safetensors", "video", "video-generation", "video-to-video", "controlnet", "en", "license:apache-2.0", "region:us" ]	null	2025-05-27T16:46:51Z	--- license: apache-2.0 language: - en tags: - video - video-generation - video-to-video - controlnet - diffusers pipeline_tag: video-to-video --- # Dilated Controlnet for Wan2.1 (canny) <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/XHKT6OS-YMMlQR1Jo3ezy.mp4"></video> This repo contains the code for dilated controlnet module for Wan2.1 model. Dilated controlnet has less basic blocks and also has `stride` parameter. For Wan1.3B model controlnet blocks count = 8 and stride = 3. See <a href="https://github.com/TheDenk/wan2.1-dilated-controlnet">Github code</a>. General scheme ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/XPa3l2dm-BhuqyAH_Yk63.png) ### How to Clone repo ```bash git clone https://github.com/TheDenk/wan2.1-dilated-controlnet.git cd wan2.1-dilated-controlnet ``` Create venv ```bash python -m venv venv source venv/bin/activate ``` Install requirements ```bash pip install -r requirements.txt ``` ### Inference examples #### Inference with cli ```bash python -m inference.cli_demo \ --video_path "resources/physical-4.mp4" \ --prompt "A balloon filled with water was thrown to the ground, exploding and splashing water in all directions. There were graffiti on the wall, studio lighting, and commercial movie shooting." \ --controlnet_type "canny" \ --controlnet_stride 3 \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1 ``` #### Inference with Gradio ```bash python -m inference.gradio_web_demo \ --controlnet_type "canny" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1 ``` #### Detailed Inference ```bash python -m inference.cli_demo \ --video_path "resources/physical-4.mp4" \ --prompt "A balloon filled with water was thrown to the ground, exploding and splashing water in all directions. There were graffiti on the wall, studio lighting, and commercial movie shooting." \ --controlnet_type "canny" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1 \ --controlnet_weight 0.8 \ --controlnet_guidance_start 0.0 \ --controlnet_guidance_end 0.8 \ --controlnet_stride 3 \ --num_inference_steps 50 \ --guidance_scale 5.0 \ --video_height 480 \ --video_width 832 \ --num_frames 81 \ --negative_prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \ --seed 42 \ --out_fps 16 \ --output_path "result.mp4" ``` ## Acknowledgements Original code and models [Wan2.1](https://github.com/Wan-Video/Wan2.1). ## Citations ``` @misc{TheDenk, title={Dilated Controlnet}, author={Karachev Denis}, url={https://github.com/TheDenk/wan2.1-dilated-controlnet}, publisher={Github}, year={2025} } ``` ## Contacts <p>Issues should be raised directly in the repository. For professional support and recommendations please <a>[email protected]</a>.</p>
mradermacher/LIMOPro-S1-P-GGUF	mradermacher	2025-05-27T16:52:20Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:YangXiao-nlp/LIMOPro-S1-P", "base_model:quantized:YangXiao-nlp/LIMOPro-S1-P", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-27T16:09:30Z	--- base_model: YangXiao-nlp/LIMOPro-S1-P language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/YangXiao-nlp/LIMOPro-S1-P <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-S1-P-GGUF/resolve/main/LIMOPro-S1-P.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Jsevisal/ft-bert-large-gest-pred-seqeval-partialmatch	Jsevisal	2025-05-27T16:52:04Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:Jsevisal/gesture_pred", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-04-19T09:13:21Z	--- license: other widget: - text: I'm fine. Who is this? - text: You can't take anything seriously. - text: In the end he's going to croak, isn't he? tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: bert-gest-pred-seqeval-partialmatch results: [] datasets: - Jsevisal/gesture_pred pipeline_tag: token-classification --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-gest-pred-seqeval-partialmatch This model is a fine-tuned version of [bert-large-cased-finetuned-conll03-english](https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7482 - F1: 0.7692 - Accuracy: 0.8147 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.27.3 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2 ### LICENSE Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados. Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigación Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a título enunciativo pero no limitativo, la reproducción, fijación, distribución, comunicación pública, ingeniería inversa y/o transformación sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado también responsable de las consecuencias legales que pudieran derivarse de sus actos.
Jsevisal/balanced-augmented-ft-bert-large-gest-pred-seqeval-partialmatch-2	Jsevisal	2025-05-27T16:51:41Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:Jsevisal/balanced_augmented_dataset_2", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-04-19T10:32:27Z	--- license: other tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: balanced-augmented-ft-bert-large-gest-pred-seqeval-partialmatch-2 results: [] datasets: - Jsevisal/balanced_augmented_dataset_2 --- <!-- 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. --> # balanced-augmented-bert-gest-pred This model is a fine-tuned version of [bert-large-cased-finetuned-conll03-english](https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english) on the Jsevisal/balanced_augmented_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.4077 - F1: 0.9208 - Accuracy: 0.9015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2 ### LICENSE Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados. Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigación Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a título enunciativo pero no limitativo, la reproducción, fijación, distribución, comunicación pública, ingeniería inversa y/o transformación sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado también responsable de las consecuencias legales que pudieran derivarse de sus actos.
LevinZheng/Reinforce-Cartpole-v1	LevinZheng	2025-05-27T16:51:19Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-27T16:51:09Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
love-mimi/sn72-mimi01	love-mimi	2025-05-27T16:50:40Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T16:11:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
Yehor/w2v-bert-uk-v2.1-iree-cpu	Yehor	2025-05-27T16:47:56Z	0	0	null	[ "uk", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2025-04-15T13:50:19Z	--- license: cc-by-nc-sa-4.0 language: - uk --- This repository has models for IREE runtime (check their GitHub: https://github.com/iree-org/iree).
Yehor/w2v-bert-uk-v2.1-iree-cuda	Yehor	2025-05-27T16:46:48Z	0	0	null	[ "uk", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2025-04-15T13:17:52Z	--- license: cc-by-nc-sa-4.0 language: - uk --- This repository has models for IREE runtime (check their GitHub: https://github.com/iree-org/iree).
Diamantis99/YXrq8iE	Diamantis99	2025-05-27T16:44:57Z	0	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-05-27T16:44:49Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # FPN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "xception", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_pyramid_channels": 256, "decoder_segmentation_channels": 128, "decoder_merge_policy": "add", "decoder_dropout": 0.2, "decoder_interpolation": "nearest", "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.5316183567047119, "test_dataset_iou": 0.595180332660675 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
FormlessAI/4511d599-e2a7-418b-ab35-f348c2da8e30	FormlessAI	2025-05-27T16:43:41Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:EleutherAI/pythia-160m", "base_model:finetune:EleutherAI/pythia-160m", "endpoints_compatible", "region:us" ]	null	2025-05-27T15:41:24Z	--- base_model: EleutherAI/pythia-160m library_name: transformers model_name: 4511d599-e2a7-418b-ab35-f348c2da8e30 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for 4511d599-e2a7-418b-ab35-f348c2da8e30 This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m). 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="FormlessAI/4511d599-e2a7-418b-ab35-f348c2da8e30", 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/phoenix-formless/Gradients/runs/pzr8wnwz) 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: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
othoi-113-viral-video-link-hd/othoiiii.viral.video.link.othoi.viral.video.link.1.13.second	othoi-113-viral-video-link-hd	2025-05-27T16:42:33Z	0	0	null	[ "region:us" ]	null	2025-05-27T16:41:19Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=othoi) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=othoi) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=othoi)
Mawdistical/Draconia-Overdrive-32B_EXL3_8.0bpw_H8	Mawdistical	2025-05-27T16:42:21Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "8-bit", "region:us" ]	text-generation	2025-05-27T16:20:53Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_8.0bpw_H6	Mawdistical	2025-05-27T16:42:17Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "8-bit", "region:us" ]	text-generation	2025-05-27T16:17:21Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_6.0bpw_H6	Mawdistical	2025-05-27T16:42:13Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "6-bit", "region:us" ]	text-generation	2025-05-27T16:14:39Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_5.0bpw_H6	Mawdistical	2025-05-27T16:42:08Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "5-bit", "region:us" ]	text-generation	2025-05-27T16:12:07Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_4.5bpw_H6	Mawdistical	2025-05-27T16:42:04Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2025-05-27T16:10:04Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
BootesVoid/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi	BootesVoid	2025-05-27T16:41:39Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T16:41:37Z	--- 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: elena_ --- # Cmb6Pxhjv062Qlexpw6Nfpaii_Cmb6Q4Yep063Zlexpzgmaioyi <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 `elena_` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "elena_", "lora_weights": "https://huggingface.co/BootesVoid/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi/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/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi', weight_name='lora.safetensors') image = pipeline('elena_').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi/discussions) to add images that show off what you’ve made with this LoRA.
Mohamed-Aly/BABYLM-TOKENIZER-BPE-TXT	Mohamed-Aly	2025-05-27T16:41:38Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T16:41:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Mawdistical/Draconia-Overdrive-32B_EXL3_2.5bpw_H6	Mawdistical	2025-05-27T16:41:14Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2025-05-27T15:56:57Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay	cwhuh	2025-05-27T16:40:28Z	4	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T14:11:29Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other instance_prompt: A newborn <s0><s1><s2><s3><s4><s5> baby. widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux DreamBooth LoRA - cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay <Gallery /> ## Model description These are cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? False. Pivotal tuning was enabled: True. ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `Caramel Clay_hsfw` → use `<s0><s1><s2><s3><s4><s5>` in your prompt ## Download model [Download the *.safetensors LoRA](cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay', filename='/nas/checkpoints/sangmin/babyface_flux_dlora_hsfw_hs_Caramel_Clay_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>", "<s4>", "<s5>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) image = pipeline('A newborn <s0><s1><s2><s3><s4><s5> baby.').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Diamantis99/KVIbIp1	Diamantis99	2025-05-27T16:35:25Z	0	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-05-27T16:35:08Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # FPN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "efficientnet-b7", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_pyramid_channels": 256, "decoder_segmentation_channels": 128, "decoder_merge_policy": "add", "decoder_dropout": 0.2, "decoder_interpolation": "nearest", "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.611117422580719, "test_dataset_iou": 0.6363441348075867 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
mradermacher/LIMOPro-LIMO-P-i1-GGUF	mradermacher	2025-05-27T16:35:16Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:YangXiao-nlp/LIMOPro-LIMO-P", "base_model:quantized:YangXiao-nlp/LIMOPro-LIMO-P", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-27T13:15:12Z	--- base_model: YangXiao-nlp/LIMOPro-LIMO-P language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/YangXiao-nlp/LIMOPro-LIMO-P <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/LIMOPro-LIMO-P-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
seantilley/model	seantilley	2025-05-27T12:28:11Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T12:28:07Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: seantilley - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
NewEden/sol-reaver-rp-v3	NewEden	2025-05-27T12:28:00Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:Delta-Vector/Sol-Reaver-15B-Instruct", "base_model:finetune:Delta-Vector/Sol-Reaver-15B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:26:47Z	--- base_model: - Delta-Vector/Sol-Reaver-15B-Instruct library_name: transformers tags: - mergekit - merge --- # sol-reaver-rp-v3 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the Passthrough merge method using [Delta-Vector/Sol-Reaver-15B-Instruct](https://huggingface.co/Delta-Vector/Sol-Reaver-15B-Instruct) + /alloc/Mango/axolotl/outputs/RP-V3-15B/checkpoint-2448 as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: Delta-Vector/Sol-Reaver-15B-Instruct+/alloc/Mango/axolotl/outputs/RP-V3-15B/checkpoint-2448 dtype: bfloat16 merge_method: passthrough models: - model: Delta-Vector/Sol-Reaver-15B-Instruct+/alloc/Mango/axolotl/outputs/RP-V3-15B/checkpoint-2448 ```
ltg/norbert3-xs	ltg	2025-05-27T12:27:09Z	1,738	4	transformers	[ "transformers", "pytorch", "fill-mask", "BERT", "NorBERT", "Norwegian", "encoder", "custom_code", "no", "nb", "nn", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2023-03-28T16:49:08Z	--- language: - 'no' - nb - nn inference: false tags: - BERT - NorBERT - Norwegian - encoder license: apache-2.0 --- # NorBERT 3 xs <img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%> The official release of a new generation of NorBERT language models described in paper [NorBench — A Benchmark for Norwegian Language Models](https://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model. ## Other sizes: - [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs) - [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small) - [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base) - [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large) ## Generative NorT5 siblings: - [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs) - [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small) - [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base) - [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large) ## Example usage This model currently needs a custom wrapper from `modeling_norbert.py`, you should therefore load the model with `trust_remote_code=True`. ```python import torch from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-xs") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-xs", trust_remote_code=True) mask_id = tokenizer.convert_tokens_to_ids("[MASK]") input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt") output_p = model(**input_text) output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) # should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]' print(tokenizer.decode(output_text[0].tolist())) ``` The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`. ## Cite us ```bibtex @inproceedings{samuel-etal-2023-norbench, title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models", author = "Samuel, David and Kutuzov, Andrey and Touileb, Samia and Velldal, Erik and {\O}vrelid, Lilja and R{\o}nningstad, Egil and Sigdel, Elina and Palatkina, Anna", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = may, year = "2023", address = "T{\'o}rshavn, Faroe Islands", publisher = "University of Tartu Library", url = "https://aclanthology.org/2023.nodalida-1.61", pages = "618--633", abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.", } ```
ltg/norbert3-large	ltg	2025-05-27T12:25:45Z	1,262	5	transformers	[ "transformers", "pytorch", "fill-mask", "BERT", "NorBERT", "Norwegian", "encoder", "custom_code", "no", "nb", "nn", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2023-03-02T20:27:09Z	--- language: - 'no' - nb - nn inference: true tags: - BERT - NorBERT - Norwegian - encoder license: apache-2.0 --- # NorBERT 3 large <img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%> The official release of a new generation of NorBERT language models described in paper [NorBench — A Benchmark for Norwegian Language Models](https://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model. ## Other sizes: - [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs) - [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small) - [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base) - [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large) ## Generative NorT5 siblings: - [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs) - [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small) - [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base) - [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large) ## Example usage This model currently needs a custom wrapper from `modeling_norbert.py`, you should therefore load the model with `trust_remote_code=True`. ```python import torch from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-large") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-large", trust_remote_code=True) mask_id = tokenizer.convert_tokens_to_ids("[MASK]") input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt") output_p = model(**input_text) output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) # should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]' print(tokenizer.decode(output_text[0].tolist())) ``` The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`. ## Cite us ```bibtex @inproceedings{samuel-etal-2023-norbench, title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models", author = "Samuel, David and Kutuzov, Andrey and Touileb, Samia and Velldal, Erik and {\O}vrelid, Lilja and R{\o}nningstad, Egil and Sigdel, Elina and Palatkina, Anna", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = may, year = "2023", address = "T{\'o}rshavn, Faroe Islands", publisher = "University of Tartu Library", url = "https://aclanthology.org/2023.nodalida-1.61", pages = "618--633", abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.", } ```
ltg/norbert3-small	ltg	2025-05-27T12:24:33Z	1,306	2	transformers	[ "transformers", "pytorch", "fill-mask", "BERT", "NorBERT", "Norwegian", "encoder", "custom_code", "no", "nb", "nn", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2023-03-28T16:47:38Z	--- language: - 'no' - nb - nn inference: false tags: - BERT - NorBERT - Norwegian - encoder license: apache-2.0 --- # NorBERT 3 small <img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%> The official release of a new generation of NorBERT language models described in paper [NorBench — A Benchmark for Norwegian Language Models](https://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model. ## Other sizes: - [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs) - [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small) - [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base) - [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large) ## Generative NorT5 siblings: - [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs) - [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small) - [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base) - [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large) ## Example usage This model currently needs a custom wrapper from `modeling_norbert.py`, you should therefore load the model with `trust_remote_code=True`. ```python import torch from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-small") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-small", trust_remote_code=True) mask_id = tokenizer.convert_tokens_to_ids("[MASK]") input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt") output_p = model(**input_text) output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) # should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]' print(tokenizer.decode(output_text[0].tolist())) ``` The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`. ## Cite us ```bibtex @inproceedings{samuel-etal-2023-norbench, title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models", author = "Samuel, David and Kutuzov, Andrey and Touileb, Samia and Velldal, Erik and {\O}vrelid, Lilja and R{\o}nningstad, Egil and Sigdel, Elina and Palatkina, Anna", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = may, year = "2023", address = "T{\'o}rshavn, Faroe Islands", publisher = "University of Tartu Library", url = "https://aclanthology.org/2023.nodalida-1.61", pages = "618--633", abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.", } ```
lisabdunlap/balanced_sft_long-1e4_e15	lisabdunlap	2025-05-27T12:24:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:23:34Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B 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)
apollina/poli	apollina	2025-05-27T12:23:05Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T12:23:05Z	--- license: apache-2.0 ---
abhikapoor909/vitmodel	abhikapoor909	2025-05-27T12:21:21Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-27T12:20:22Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: abhikapoor909 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Hsianchengfun/pruned_25_dt_dp_120epoch	Hsianchengfun	2025-05-27T12:20:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:18:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
nguyenduongchitam/whisper-small-vi	nguyenduongchitam	2025-05-27T12:16:37Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-27T05:09:50Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-vi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-vi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4522 - Wer: 27.0405 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.4981 \| 0.9699 \| 1000 \| 0.4862 \| 31.8081 \| \| 0.3205 \| 1.9399 \| 2000 \| 0.4527 \| 29.7486 \| \| 0.1923 \| 2.9098 \| 3000 \| 0.4522 \| 27.0405 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
phospho-app/freza44-gr00t-cube_N-mi84eetyfa	phospho-app	2025-05-27T12:16:33Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-27T11:56:08Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [freza44/cube_N](https://huggingface.co/datasets/freza44/cube_N) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
Hsianchengfun/pruned_15_dt_dp_100epoch	Hsianchengfun	2025-05-27T12:12:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:09:31Z	--- 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]
Cloudmaster/Llama-3.2-3B-torchao-final02	Cloudmaster	2025-05-27T12:07:50Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-27T12:02:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
jeongseokoh/llama3_8b-with-conclusion-Alphabet_False_Multiple2_aggr_last_starting_with_inst_withOutEmbed	jeongseokoh	2025-05-27T12:03:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:56:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Seeker38/gemma-2-9b-it-abc-notation	Seeker38	2025-05-27T11:59:10Z	49	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "unsloth", "trl", "sft", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-28T03:57:39Z	--- library_name: transformers tags: - unsloth - trl - sft --- ## Model Details This model is finetuned on mutiple datasets related to ABC notation (mostly Irish data) ## CLI demo for 4-bit quantize ```python from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, BitsAndBytesConfig import torch import torchaudio import re quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4" ) # Alpaca prompt template alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {} ### Input: {} ### Response: {}""" tokenizer = AutoTokenizer.from_pretrained("Seeker38/gemma-2-9b-it-abc-notation") # model 4-bit quant model = AutoModelForCausalLM.from_pretrained( "Seeker38/gemma-2-9b-it-abc-notation", quantization_config=quantization_config, device_map="auto", resume_download=True ).eval() generation_config = GenerationConfig( temperature=0.2, top_k=40, top_p=0.9, do_sample=True, num_beams=1, repetition_penalty=1.1, min_new_tokens=10, max_new_tokens=1536 ) instruction = """Create a musical composition using the given motif and adhering to the specified musical form represented by alphabet characters. X:1 L:1/8 Q:3/8=90 M:6/8 K:A ['e cAA ABc dBB Tf2 e fdd', 'e fga']""" # input_context = "'A', 'D', 'E7', 'A', 'E/G#', 'A', 'Bm', 'A7/C#', 'D', 'E7', 'A', 'A', 'D', 'A', 'A', 'D', 'A', 'A', 'D', 'A', 'D', 'A/D#', 'E', 'A', 'D', 'A', 'A', 'D', 'A', 'E7'" input_context = "" prompt = alpaca_prompt.format( instruction, # instruction input_context, # input "", # output - leave this blank for generation! ) # Tokenize input inputs = tokenizer([prompt], return_tensors="pt").to("cuda") # Generate response with specified parameters with torch.no_grad(): outputs = model.generate( *inputs, max_new_tokens=2048, temperature=0.2, top_p=0.9, top_k=40, use_cache=True, do_sample=True, repetition_penalty=1.1, pad_token_id=tokenizer.eos_token_id ) result = tokenizer.batch_decode(outputs, skip_special_tokens=True) print("Generated Response:") print(result[0]) # to render abc notation, you need to install symusic # pip install symusic import re from symusic import Score, Synthesizer abc_notation = re.search(r'### Response:\s(.*)', result[0], re.DOTALL).group(1).strip() s = Score.from_abc(abc_notation) audio = Synthesizer().render(s, stereo=True) torchaudio.save('cm_music_piece.wav', torch.FloatTensor(audio), 44100) from IPython.display import Audio, display from pydub import AudioSegment wav_link = "cm_music_piece.wav" mp3_file = AudioSegment.from_wav(wav_link).export("cm_music_piece.mp3", format="mp3") display(Audio(wav_link)) display(Audio('cm_music_piece.mp3')) ``` ## Example Stable Prompts Here some prompts that are tested to be stable. The convert code and prompt is from 🤗 [ChatMusician](https://huggingface.co/m-a-p/ChatMusician). ### Function: Chord Conditioned Music Generation ``` Develop a musical piece using the given chord progression. 'Dm', 'C', 'Dm', 'Dm', 'C', 'Dm', 'C', 'Dm' ``` ### Function: Text2music ``` Develop a tune influenced by Bach's compositions. ``` ``` Using ABC notation, recreate the given text as a musical score. Meter C Notes The parts are commonly interchanged. Transcription 1997 by John Chambers Key D Note Length 1/8 Rhythm reel ``` ### Function: Melody Harmonization ``` Construct smooth-flowing chord progressions for the supplied music. \|: BA \| G2 g2"^(C)" edeg \| B2 BA"^(D7)" BcBA \| G2 g2 edeg \| dBAG A2 BA \| G2 g2"^(C)" edeg \| B2 BA B2 d2 \| e2 ef e2 (3def \| gedB A2 :: BA \| G2 BG dGBe \| dBBA"^(D7)" B3 A \| G2 BG dGBe \| dBAG A4 \| G2 BG dGBe \| dBBA B3 d \| e2 ef e2 (3def \| gedB A2 :\| ``` ``` Develop a series of chord pairings that amplify the harmonious elements in the given music piece. E \|: EAA ABc \| Bee e2 d \| cBA ABc \| BEE E2 D \| EAA ABc \| Bee e2 d \| cBA ^GAB \|1 A2 A A2 E :\|2 A2 A GAB \|\| c3 cdc \| Bgg g2 ^g \| aed cBA \| ^GAB E^F^G \| A^GA BAB \| cde fed \| cBA ^GAB \|1 A2 A GAB :\|2 \n A3 A2 \|\| ``` ### Function: Musical Form Conditioned Music Generation ``` Develop a composition by incorporating elements from the given melodic structure. Ternary, Sectional: Verse/Chorus/Bridge ``` ### Function: Motif and Form Conditioned Music Generation ``` Create music by following the alphabetic representation of the assigned musical structure and the given motif. Musical Form Input: AB ABC Notation Music Input: X:1 L:1/8 M:2/4 K:D ['d>ef>d g>ef>c d>ef>d c2 e2 d>ef>d g>ef>d', '(3(Ace) (3(Ace)'] ```
JesseLiu/llama32-1b-pagerank-partial-naive-grpo-lora	JesseLiu	2025-05-27T11:50:51Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-05-27T11:50:27Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
arnaultsta/MNLP_M2_rag_model	arnaultsta	2025-05-27T11:48:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:48:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
HPLT/hplt2c_eng50-tur50_checkpoints	HPLT	2025-05-27T11:46:00Z	0	0	null	[ "pytorch", "llama", "HPLT", "decoder", "en", "tr", "dataset:HPLT/HPLT2.0_cleaned", "arxiv:2503.10267", "license:apache-2.0", "region:us" ]	null	2025-05-26T08:49:52Z	--- language: - en - tr tags: - HPLT - decoder license: apache-2.0 datasets: - HPLT/HPLT2.0_cleaned --- # HPLT v2.0 - Cleaned - English (50%), Turkish (50%) <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%> This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned). All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total: - hidden size: 2048 - attention heads: 32 - layers: 24 - sequence length: 2048 ## Intermediate checkpoints We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch. ## Cite us ```bibtex @misc{burchell2025expandedmassivemultilingualdataset, title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies}, author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu}, year={2025}, eprint={2503.10267}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10267}, } ```
aamijar/Llama-2-7b-hf-lora-r8-boolq-portlora-epochs9	aamijar	2025-05-27T11:44:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:44:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
hunter12441/model	hunter12441	2025-05-27T11:42:53Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:34:00Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: hunter12441 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
John6666/luminarqmix-v7-noobaixl-illustriousxl-anime-style-merge-model-v70-vpred-mature-sdxl	John6666	2025-05-27T11:40:32Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "cute", "hands", "human body", "flatter shading", "mature", "merge", "v-pred", "Illustrious XL v2.0", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v2.0", "base_model:merge:OnomaAIResearch/Illustrious-XL-v2.0", "base_model:cyberdelia/CyberIllustrious", "base_model:merge:cyberdelia/CyberIllustrious", "base_model:hybskgks28275/LuminarQMix", "base_model:merge:hybskgks28275/LuminarQMix", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-27T11:34:39Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - cute - hands - human body - flatter shading - mature - merge - v-pred - Illustrious XL v2.0 - illustrious base_model: - hybskgks28275/LuminarQMix - cyberdelia/CyberIllustrious - OnomaAIResearch/Illustrious-XL-v2.0 --- Original model is [here](https://huggingface.co/hybskgks28275/LuminarQMix) and on [Civitai](https://civitai.com/models/1616309?modelVersionId=1837502). The author is [here](https://huggingface.co/hybskgks28275) This model created by [hybskgks28275](https://civitai.com/user/hybskgks28275).
OlofBen/HeartLM-v4.2	OlofBen	2025-05-27T11:39:44Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:22:28Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
beanne-valerie-dela-cruz-viral-video/1.Viral.beanne-valerie-dela-cruz-beanne-dela-cruz-viral-video-beanne-valerie-delacruz-telegram	beanne-valerie-dela-cruz-viral-video	2025-05-27T11:38:28Z	0	0	null	[ "region:us" ]	null	2025-05-27T11:37:52Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?ff">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ff">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ff"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
tripolskypetr/gemma-3-27B-it-qat-GGUF	tripolskypetr	2025-05-27T11:36:21Z	0	0	null	[ "gguf", "image-text-to-text", "base_model:google/gemma-3-27b-it", "base_model:quantized:google/gemma-3-27b-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-26T14:21:53Z	--- pipeline_tag: image-text-to-text extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license license: gemma extra_gated_heading: Access Gemma on Hugging Face base_model: google/gemma-3-27b-it --- ## 💫 Community Model> gemma 3 27b it by Google 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [google](https://huggingface.co/google)<br> Original model: [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it)<br> GGUF quantization: provided by Google<br> ## Technical Details Optimized with Quantization Aware Training for improved 4-bit performance. Supports a context length of 128k tokens, with a max output of 8192. Multimodal supporting images normalized to 896 x 896 resolution. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
09Sophie-Rain-SpiderMan-Video/Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video	09Sophie-Rain-SpiderMan-Video	2025-05-27T11:36:20Z	0	0	null	[ "region:us" ]	null	2025-05-27T11:35:48Z	18 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
Hsianchengfun/pruned_50_dt_dp_100epoch	Hsianchengfun	2025-05-27T11:32:05Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:29:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
madhueb/MNLP_M2_dpo_model	madhueb	2025-05-27T11:29:22Z	8	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "dataset:madhueb/MNLP_M2_dpo_dataset", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T15:58:34Z	--- library_name: transformers tags: - trl - dpo datasets: - madhueb/MNLP_M2_dpo_dataset --- - Developed by: Madeleine Hueber - Language(s) (NLP): English - License: For academic use only - Finetuned from model: Qwen3-0.6B-Base This model is a preference-aligned language model fine-tuned for answering STEM-related instruction prompts. It was developed as part of the M2 deliverable for the CS-552 course Modern Natural Language Processing. # Training Details: - Stage 1: Instruction tuning on a subset of TIGER-Lab/WebInstructSub (200k data , aivalable on the train_instruct split of madhueb/MNLP_M2_dpo_dataset ) - Stage 2: DPO fine-tuning using the train split of madhueb/MNLP_M2_dpo_dataset.
John6666/duchaiten-noobai-eps-v20-sdxl	John6666	2025-05-27T11:29:13Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "3D", "2.5D", "details", "lighting", "trained", "noobai", "illustrious", "en", "base_model:Laxhar/noobai-XL-Vpred-1.0", "base_model:finetune:Laxhar/noobai-XL-Vpred-1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-27T11:23:51Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - 3D - 2.5D - details - lighting - trained - noobai - illustrious base_model: Laxhar/noobai-XL-Vpred-1.0 --- Original model is [here](https://civitai.com/models/1502712/duchaiten-noobai?modelVersionId=1838176). The author is [here](https://huggingface.co/DucHaiten). This model created by [DucHaiten](https://civitai.com/user/DucHaiten).
kevanme/Practica1	kevanme	2025-05-27T11:28:56Z	0	0	fastai	[ "fastai", "region:us" ]	null	2025-02-13T17:07:30Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
Hsianchengfun/pruned_55_dt_dp_100epoch	Hsianchengfun	2025-05-27T11:27:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:24:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
transformers-community/sink_cache	transformers-community	2025-05-27T11:24:49Z	0	0	transformers	[ "transformers", "safetensors", "custom_generate", "arxiv:2309.17453", "endpoints_compatible", "region:us" ]	null	2025-05-22T15:37:29Z	--- library_name: transformers tags: - custom_generate --- ## Description Implementation of the KV cache introduced in the [Attention Sinks paper](https://huggingface.co/papers/2309.17453). It allows the model to generate beyond the length of its context window, without losing fluency in the conversation. This is done by always keeping the first few tokens ("sink tokens") in the KV cache, as models often pay a large amount of attention to them. As it discards past non-sink tokens, the model will lose the ability to generate tokens that depend on the context that was discarded. It's also a solution to contain the memory footprint of the KV cache. This implementation matches the `SinkCache` class present in `transformers<4.53.0`. ![Sink Cache diagram from the original paper](https://arxiv.org/html/2309.17453v4/x1.png) <!-- TODO (joao): add `transformers chat` example --> ## Base model - [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) ## Model compatibility - Decoder-only transformers models ## Additional Arguments - `window_length` (`int`, optional, defaults to 256): The length of the context window. - `num_sink_tokens` (`int`, optional, defaults to 4): The number of sink tokens. See the original paper for more information. ## Output Type changes - When `return_dict_in_generate=True`, `output.past_key_values` will be a `SinkCache` instance. `SinkCache` is defined in `generate.py`, in this repository. ## Example usage We can use the custom generation method in this repository like the the base `generate` from `transformers`: ```py # requires `transformers>=4.52.0` from transformers import AutoModelForCausalLM, AutoTokenizer # Preparing model, tokenizer, and model inputs tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", device_map="auto") messages = [{"role": "user", "content": "Tell me a story about a cat."}] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Using sink cache gen_out = model.generate( # usual `generate` arguments model_inputs, do_sample=False, max_new_tokens=100, return_dict_in_generate=True, # sink cache arguments (default `window_length=256`) custom_generate="transformers-community/sink_cache", trust_remote_code=True, ) print(tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True)) assert "sinkcache" in str(type(gen_out.past_key_values)).lower() # ['user\nTell me a story about a cat.\nassistant\n<think>\n\n</think>\n\nOnce upon a time, in a cozy village nestled # between rolling hills and a sparkling lake, there lived a cat named Luna. Luna was small and fluffy, with a curious # eyes that sparkled with wonder. She had a soft, warm coat that shimmered like the morning sun, and her tail was # always wagging in playful motions.\n\nOne day, while exploring the village, Luna noticed a curious sight: a young # boy playing with a ball on the lake. She followed him closely, her heart racing'] ``` Continuing the example above, we can confirm some properties of the `SinkCache` ```py # `max_new_tokens` < `window_length` in the example above -> matches output with the default cache gen_out = model.generate( model_inputs, do_sample=False, max_new_tokens=100, return_dict_in_generate=True, ) print(tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True)) assert "dynamiccache" in str(type(gen_out.past_key_values)).lower() # ['user\nTell me a story about a cat.\nassistant\n<think>\n\n</think>\n\nOnce upon a time, in a cozy village nestled # between rolling hills and a sparkling lake, there lived a cat named Luna. Luna was small and fluffy, with a curious # eyes that sparkled with wonder. She had a soft, warm coat that shimmered like the morning sun, and her tail was # always wagging in playful motions.\n\nOne day, while exploring the village, Luna noticed a curious sight: a young # boy playing with a ball on the lake. She followed him closely, her heart racing'] # if we set a smaller `window_length`, the story is less coherent after that point, but the used cache is also # significantly smaller gen_out = model.generate( # usual `generate` arguments **model_inputs, do_sample=False, max_new_tokens=100, return_dict_in_generate=True, # sink cache arguments custom_generate="transformers-community/sink_cache", trust_remote_code=True, window_length=50, ) print(tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True)) # ["user\nTell me a story about a cat.\nassistant\n<think>\n\n</think>\n\nOnce upon a time, in a cozy village nestled # between rolling hills and a sparkling lake, there lived a cat named Luna. Luna was small and fluffy, with a curious # heart. She loved exploring the village and playing with her friends.\n\nOne day, Luna noticed something unusual. # She looked around and saw a shadow moving in the dark. She ran quickly, but she couldn't see the shadow. She # thought maybe it was a ghost or something else.\n\nAs she was running, she heard a voice."] ```
nnilayy/deap-dominance-binary-classification-no-wd-Kfold-2	nnilayy	2025-05-27T11:24:32Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-27T11:24:26Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
aamijar/Llama-2-7b-hf-lora-r8-boolq-portlora-epochs8	aamijar	2025-05-27T11:23:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:23:08Z	--- 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]
TheRoyalKuvera/basegi	TheRoyalKuvera	2025-05-27T11:21:56Z	0	0	null	[ "en", "license:apache-2.0", "region:us" ]	null	2025-02-19T22:36:07Z	--- license: apache-2.0 language: - en metrics: - accuracy ---
Mahlia/MNLP_dpo_sft	Mahlia	2025-05-27T11:21:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:18:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
OlofBen/HeartLM-v4.1	OlofBen	2025-05-27T11:20:34Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:02:59Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
iamjab/learn_hf_food_not_food_text_classifier-distilbert-base-uncased	iamjab	2025-05-27T11:19:18Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T11:18:44Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: learn_hf_food_not_food_text_classifier-distilbert-base-uncased 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. --> # learn_hf_food_not_food_text_classifier-distilbert-base-uncased This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0006 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4084 \| 1.0 \| 7 \| 0.0617 \| 1.0 \| \| 0.027 \| 2.0 \| 14 \| 0.0064 \| 1.0 \| \| 0.0042 \| 3.0 \| 21 \| 0.0022 \| 1.0 \| \| 0.0019 \| 4.0 \| 28 \| 0.0012 \| 1.0 \| \| 0.0012 \| 5.0 \| 35 \| 0.0009 \| 1.0 \| \| 0.0009 \| 6.0 \| 42 \| 0.0007 \| 1.0 \| \| 0.0008 \| 7.0 \| 49 \| 0.0006 \| 1.0 \| \| 0.0007 \| 8.0 \| 56 \| 0.0006 \| 1.0 \| \| 0.0007 \| 9.0 \| 63 \| 0.0006 \| 1.0 \| \| 0.0006 \| 10.0 \| 70 \| 0.0006 \| 1.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.6 - Tokenizers 0.21.1
Link-othoi-1-13-video/18.New.Video.othoi.1.13.video.link.othoiiii.mms.video.othoiiii.video.link	Link-othoi-1-13-video	2025-05-27T11:19:16Z	0	0	null	[ "region:us" ]	null	2025-05-27T11:19:09Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
pprokopidis/elNER18-bert-base-greek-uncased-v1-bs8-e150-lr5e-06	pprokopidis	2025-05-27T11:19:06Z	24	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "el", "base_model:nlpaueb/bert-base-greek-uncased-v1", "base_model:finetune:nlpaueb/bert-base-greek-uncased-v1", "license:cc-by-nc-2.0", "region:us" ]	token-classification	2024-10-02T14:00:30Z	--- language: - el license: cc-by-nc-2.0 tags: - flair - token-classification - sequence-tagger-model base_model: - nlpaueb/bert-base-greek-uncased-v1 --- # Greek Named Entity Model finetuned on the elNER Dataset This Greek NER model was fine-tuned by researchers at the [Institute for Language and Speech Processing/Athena RC](https://www.ilsp.gr). The model was finetuned on the [elNER-18 dataset](https://dl.acm.org/doi/10.1145/3411408.3411437) using the [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) as backbone LM. ## Dataset The [elNER-18 dataset](https://dl.acm.org/doi/10.1145/3411408.3411437) consists of 21K sentences, 623K tokens and 94K annotated named entities for 18 NE classes. The following 18 named entities are annotated in the train partition: \|Class\|#\| \|:---\|:---\| \|ORG\|10944\| \|PERSON\|8774\| \|CARDINAL\|7343\| \|GPE\|6781\| \|DATE\|6338\| \|ORDINAL\|1438\| \|PERCENT\|1437\| \|LOC\|1404\| \|NORP\|1396\| \|MONEY\|1012\| \|TIME\|1011\| \|EVENT\|962\| \|PRODUCT\|668\| \|WORK_OF_ART\|608\| \|FAC\|567\| \|QUANTITY\|565\| \|LAW\|235\| \|LANGUAGE\|55\| ## Fine-Tuning [Flair version 0.14](https://github.com/flairNLP/flair/releases/tag/v0.14.0) was used for fine-tuning. <!-- A hyper-parameter search is to be performed. Right now we have results with the following parameters. --> The model was trained with the following hyper-parameters: * Batch Size: [`8`] * Learning Rate: [`5e-05`] ## Results - F-score (micro) 0.9173 - F-score (macro) 0.8778 - Accuracy 0.8651 \|Class\|precision\|recall\|f1-score\|support\| \|:---\|:---\|:---\|:---\|:---\| \|ORG\|0.8931\|0.8847\|0.8889\|1388\| \|PERSON\|0.9516\|0.9724\|0.9619\|1051\| \|CARDINAL\|0.9330\|0.9627\|0.9476\|911\| \|DATE\|0.9403\|0.9403\|0.9403\|838\| \|GPE\|0.9282\|0.9552\|0.9415\|826\| \|PERCENT\|0.9807\|0.9854\|0.9831\|206\| \|LOC\|0.8011\|0.7921\|0.7966\|178\| \|ORDINAL\|0.9477\|0.9477\|0.9477\|172\| \|NORP\|0.8690\|0.8936\|0.8811\|141\| \|TIME\|0.8951\|0.9343\|0.9143\|137\| \|EVENT\|0.6395\|0.7231\|0.6787\|130\| \|MONEY\|0.9818\|0.9730\|0.9774\|111\| \|PRODUCT\|0.7882\|0.8072\|0.7976\|83\| \|WORK_OF_ART\|0.8313\|0.8214\|0.8263\|84\| \|FAC\|0.6933\|0.6753\|0.6842\|77\| \|QUANTITY\|0.8636\|0.8769\|0.8702\|65\| \|LAW\|0.8214\|0.8214\|0.8214\|28\| \|LANGUAGE\|1.0000\|0.8889\|0.9412\|9\| \| \|\|\|\| \|micro avg\|0.9112\|0.9235\|0.9173\|6435\| \|macro avg\|0.8755\|0.8809\|0.8778\|6435\| \|weighted avg\|0.9116\|0.9235\|0.9174\|6435\| ## Files The Flair [training log](training.log) has also been uploaded to the model hub. ## Example usage ```python #! pip install flair #! pip install segtok from flair.models import SequenceTagger from flair.data import Sentence tagger = SequenceTagger.load("pprokopidis/elNER18-bert-base-greek-uncased-v1-bs8-e150-lr5e-06") text = """Η Ρωσία απέκλεισε τη δυνατότητα διεξαγωγής συνομιλιών για τα πυρηνικά όπλα με τις Ηνωμένες Πολιτείες, επικαλούμενη τη στάση της Ουάσινγκτον στο θέμα της επέκτασης του ΝΑΤΟ, δήλωσε σήμερα η εκπρόσωπος του ρωσικού υπουργείου Εξωτερικών Μαρία Ζαχάροβα. «Δεν βλέπουμε κανένα νόημα στον διάλογο με την Ουάσινγκτον χωρίς τον σεβασμό των θεμελιωδών συμφερόντων της Ρωσίας. Πρώτα απ’ όλα, πρόκειται για το πρόβλημα της επέκτασης του ΝΑΤΟ στον μετασοβιετικό χώρο, το οποίο δημιουργεί απειλές για την κοινή ασφάλεια», δήλωσε η Ζαχάροβα. Ο Τζιμ Τζάρμους (αγγλικά: Jim Jarmusch, 22 Ιανουαρίου 1953) είναι Αμερικανός σκηνοθέτης, γνωστός κυρίως για τις ταινίες Πέρα από τον Παράδεισο (1984), Στην παγίδα του νόμου (1986), Καφές και τσιγάρα (1993), Ο Νεκρός (1995) και Τσακισμένα λουλούδια (2005). Θεωρείται εκπρόσωπος του ανεξάρτητου αμερικανικού κινηματογράφου και μέσα από τις ταινίες του εκφράζονται και ορισμένες από τις αριστερές πολιτικές του πεποιθήσεις. """ # use a library to split into sentences from segtok.segmenter import split_single sentences = [Sentence(sent, use_tokenizer=True) for sent in split_single(text) if sent.strip()] tagger.predict(sentences) for sentence in sentences: print(sentence) for span in sentence.get_spans(): print(span) ```
mohammadmahdinouri/expressive-distilled-test	mohammadmahdinouri	2025-05-27T11:14:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:03:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mlxha/Qwen3-8B-grpo-medmcqa-medi70	mlxha	2025-05-27T11:13:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:mlxha/medmcqa-grpo-meditron70b", "arxiv:2402.03300", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T00:08:26Z	--- base_model: Qwen/Qwen3-8B datasets: mlxha/medmcqa-grpo-meditron70b library_name: transformers model_name: Qwen3-8B-grpo-medmcqa-medi70 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen3-8B-grpo-medmcqa-medi70 This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [mlxha/medmcqa-grpo-meditron70b](https://huggingface.co/datasets/mlxha/medmcqa-grpo-meditron70b) 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="mlxha/Qwen3-8B-grpo-medmcqa-medi70", 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/alexs-team/reasoning/runs/a353e548-7458-41bd-a49d-4ba5a41cdc37) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
delarosajav95/PapyriBot	delarosajav95	2025-05-27T11:12:43Z	0	0	null	[ "region:us" ]	null	2025-05-26T18:41:09Z	# Papyri.info bot ## Getting started To make it easy for you to get started with GitLab, here's a list of recommended next steps. Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! ## Add your files - [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files - [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: ``` cd existing_repo git remote add origin https://git.csic.es/labhd-ilc-internal/papyri.info-bot.git git branch -M main git push -uf origin main ``` ## Integrate with your tools - [ ] [Set up project integrations](https://git.csic.es/labhd-ilc-internal/papyri.info-bot/-/settings/integrations) ## Collaborate with your team - [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) - [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) - [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) - [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) - [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) ## Test and Deploy Use the built-in continuous integration in GitLab. - [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/) - [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) - [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) - [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) - [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) *** # Editing this README When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template. ## Suggestions for a good README Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. ## Name Choose a self-explaining name for your project. ## Description Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. ## Badges On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. ## Visuals Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. ## Installation Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. ## Usage Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. ## Support Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. ## Roadmap If you have ideas for releases in the future, it is a good idea to list them in the README. ## Contributing State if you are open to contributions and what your requirements are for accepting them. For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. ## Authors and acknowledgment Show your appreciation to those who have contributed to the project. ## License For open source projects, say how it is licensed. ## Project status If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
lisabdunlap/balanced_sft_long-1e4-systems-prompt	lisabdunlap	2025-05-27T11:11:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:10:15Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B 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)
spacematt/Qwen3-30B-A3B-Q4_K_M-GGUF	spacematt	2025-05-27T11:06:21Z	35	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-27T10:51:40Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B tags: - llama-cpp - gguf-my-repo --- # spacematt/Qwen3-30B-A3B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-A3B) 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/Qwen/Qwen3-30B-A3B) for more details on the model. ### /think Parameters - `temperature`: 0.6 - `top_p`: 0.95 - `top_k`: 20 - `min_p`: 0 - `presence_penalty`: 1.5 ### /nothink Parameters - `temperature`: 0.7 - `top_p`: 0.8 - `top_k`: 20 - `min_p`: 0 - `presence_penalty`: 1.5 ## 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 spacematt/Qwen3-30B-A3B-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo spacematt/Qwen3-30B-A3B-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-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 spacematt/Qwen3-30B-A3B-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo spacematt/Qwen3-30B-A3B-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-q4_k_m.gguf -c 2048 ```
MuzamilAziz/OnceAPanda	MuzamilAziz	2025-05-27T11:05:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T11:05:23Z	--- license: apache-2.0 ---
Mawdistical/Draconia-Overdrive-32B-GGUF	Mawdistical	2025-05-27T11:05:01Z	6	0	transformers	[ "transformers", "gguf", "nsfw", "explicit", "roleplay", "Furry", "text-generation", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "region:us", "imatrix", "conversational" ]	text-generation	2025-05-16T11:07:42Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model </strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
test-gen/qwen3-8b-random_lr1e-6	test-gen	2025-05-27T11:04:52Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T10:57:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hsicat/DPO-test-1	hsicat	2025-05-27T11:04:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "dpo", "unsloth", "conversational", "arxiv:2305.18290", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:03:38Z	--- library_name: transformers model_name: DPO-test-1 tags: - generated_from_trainer - trl - dpo - unsloth licence: license --- # Model Card for DPO-test-1 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="hsicat/DPO-test-1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with 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: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.0 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Dddixyy/latin-italian-translatorV6	Dddixyy	2025-05-27T11:02:33Z	4	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "ancient-latin", "latino-antico", "italiano", "it", "la", "dataset:Dddixyy/latin_italian_parallel", "dataset:Dddixyy/latin_italian_texts", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2024-12-02T22:05:15Z	--- library_name: transformers tags: - translation - ancient-latin - latino-antico - italiano license: mit datasets: - Dddixyy/latin_italian_parallel - Dddixyy/latin_italian_texts language: - it - la pipeline_tag: translation --- # 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: Davide Brunori - Funded by [optional]: [More Information Needed - Shared by [optional]: [More Information Needed] - Model type: MarianMT model - Language(s) (NLP): italian / ancient latin - License: MIT (Attribution required) - 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]
aamijar/Llama-2-7b-hf-lora-r8-boolq-portlora-epochs7	aamijar	2025-05-27T11:01:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:01:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
OlofBen/HeartLM-v3.6	OlofBen	2025-05-27T11:00:46Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-27T10:42:23Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Amergamer/Sfwan	Amergamer	2025-05-27T10:58:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T10:58:29Z	--- license: apache-2.0 ---
kiron78724/ML	kiron78724	2025-05-27T10:57:30Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-27T10:57:30Z	--- license: artistic-2.0 ---
18-Sophie-Rain-SpiderMan-Video/Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video	18-Sophie-Rain-SpiderMan-Video	2025-05-27T10:54:32Z	0	0	null	[ "region:us" ]	null	2025-05-27T10:54:25Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
test-gen/qwen3-1.7b-easy-unique_lr1e-6	test-gen	2025-05-27T10:50:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T10:50:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Subsets and Splits