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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
JesseLiu/llama32-1b-kpath-partial-naive-grpo	JesseLiu	2025-05-27T17:04:20Z	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	2025-05-27T17:03:56Z	--- 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_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
oslusarczyk/bbc_model_output3	oslusarczyk	2025-05-27T17:03:04Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-27T15:48:24Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: bbc_model_output3 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: test args: default metrics: - name: Rouge1 type: rouge value: 0.2313 --- <!-- 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. --> # bbc_model_output3 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.8165 - Rouge1: 0.2313 - Rouge2: 0.045 - Rougel: 0.1748 - Rougelsum: 0.1744 - Gen Len: 19.375 ## 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 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 50 \| 2.8288 \| 0.2246 \| 0.0431 \| 0.1703 \| 0.17 \| 19.265 \| \| No log \| 2.0 \| 100 \| 2.8195 \| 0.2308 \| 0.0448 \| 0.175 \| 0.1748 \| 19.325 \| \| No log \| 3.0 \| 150 \| 2.8165 \| 0.2313 \| 0.045 \| 0.1748 \| 0.1744 \| 19.375 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cpu - Datasets 3.6.0 - Tokenizers 0.21.1
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
davgauch/MNLP_M2_mcqa_model_big_batch	davgauch	2025-05-27T17:01:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T06:23:00Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M2_mcqa_model_big_batch 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. --> # MNLP_M2_mcqa_model_big_batch This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9682 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 960 - total_train_batch_size: 3840 - 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: constant - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.9001 \| 5 \| 1.2024 \| \| No log \| 1.9001 \| 10 \| 1.1081 \| \| No log \| 2.9001 \| 15 \| 1.0664 \| \| No log \| 3.9001 \| 20 \| 1.0403 \| \| No log \| 4.9001 \| 25 \| 1.0200 \| \| No log \| 5.9001 \| 30 \| 1.0048 \| \| No log \| 6.9001 \| 35 \| 0.9940 \| \| No log \| 7.9001 \| 40 \| 0.9831 \| \| No log \| 8.9001 \| 45 \| 0.9750 \| \| 1.4751 \| 9.9001 \| 50 \| 0.9682 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
Diamantis99/6uoTF9w	Diamantis99	2025-05-27T17:00:12Z	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	2025-05-27T16:59:51Z	--- 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": "timm-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": 0.5991622805595398, "test_dataset_iou": 0.6255506277084351 } ] ``` ## 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)
eiitndidkwh/roadwork	eiitndidkwh	2025-05-27T17:00:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T15:35:04Z	--- 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]
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.
graliuce/Qwen2.5-3B-Instruct_MedMCQA.18.00	graliuce	2025-05-27T16:58:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "dataset:graliuce/MedMCQA.18.00", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T15:36:50Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: graliuce/MedMCQA.18.00 library_name: transformers model_name: Qwen2.5-3B-Instruct_MedMCQA.18.00 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-3B-Instruct_MedMCQA.18.00 This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [graliuce/MedMCQA.18.00](https://huggingface.co/datasets/graliuce/MedMCQA.18.00) 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="graliuce/Qwen2.5-3B-Instruct_MedMCQA.18.00", 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/grace_rl/infoseek/runs/dkzp4c33) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.0 - 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}} } ```
TheDenk/wan2.1-t2v-14b-controlnet-depth-v1	TheDenk	2025-05-27T16:58:20Z	0	1	diffusers	[ "diffusers", "safetensors", "video", "video-generation", "video-to-video", "controlnet", "en", "license:apache-2.0", "region:us" ]	null	2025-05-27T16:51:41Z	--- license: apache-2.0 language: - en tags: - video - video-generation - video-to-video - controlnet - diffusers --- # Dilated Controlnet for Wan2.1 (depth) <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/tGBCvJC9Zk44gtJpCoRz4.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 Wan14B model controlnet blocks count = 6 and stride = 4. See <a href="https://github.com/TheDenk/wan2.1-dilated-controlnet">Github code</a>. ### 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 "depth" \ --controlnet_stride 4 \ --base_model_path Wan-AI/Wan2.1-T2V-14B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-14b-controlnet-depth-v1 ``` #### Inference with Gradio ```bash python -m inference.gradio_web_demo \ --controlnet_type "depth" \ --base_model_path Wan-AI/Wan2.1-T2V-14B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-14b-controlnet-depth-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 "depth" \ --base_model_path Wan-AI/Wan2.1-T2V-14B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-14b-controlnet-depth-v1 \ --controlnet_weight 0.8 \ --controlnet_guidance_start 0.0 \ --controlnet_guidance_end 0.8 \ --controlnet_stride 4 \ --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>
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
WenFengg/losetowin_5swap6	WenFengg	2025-05-27T16:57:38Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:51:31Z	--- 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. 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RizhongLin/MNLP_M2_dpo_model_v2.2	RizhongLin	2025-05-27T16:57:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:56:48Z	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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LandCruiser/sn29_cold_2705_5	LandCruiser	2025-05-27T16:54:28Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T14:01:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lamdo/distilbert-base-uncased-phrase-15kaddedphrasesfroms2orc	lamdo	2025-05-27T16:53:38Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-27T16:53:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lammtfkday/Vnchatbot-using-qwen3	lammtfkday	2025-05-27T16:52:11Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-0.6B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-0.6B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:51:19Z	--- base_model: unsloth/Qwen3-0.6B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lammtfkday - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-0.6B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
brunnaquino123/brunalouzadareplicate	brunnaquino123	2025-05-27T16:51:53Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-27T15:26:26Z	--- 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 ---
gradientrouting-spar/medical_task_qwen_3_8b_ft_trainers_seed_3_epoch_1	gradientrouting-spar	2025-05-27T16:51:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:49:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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one-girl-one-wolf-hd/Trending.Video.18.one.girl.one.wolf.one.girl.and.one.wolf.viral.video.Trending	one-girl-one-wolf-hd	2025-05-27T16:49:57Z	0	0	null	[ "region:us" ]	null	2025-05-27T16:48:21Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=one-girl-one-wolf) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=one-girl-one-wolf) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=one-girl-one-wolf)
TheDenk/wan2.1-t2v-1.3b-controlnet-hed-v1	TheDenk	2025-05-27T16:49:04Z	28	3	diffusers	[ "diffusers", "safetensors", "video", "video-generation", "video-to-video", "controlnet", "en", "license:apache-2.0", "region:us" ]	null	2025-05-22T07:55:57Z	--- 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 <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/3w5CQ-quMowfEaS90xyrd.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 "hed" \ --controlnet_stride 3 \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-hed-v1 ``` #### Inference with Gradio ```bash python -m inference.gradio_web_demo \ --controlnet_type "hed" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-hed-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 "hed" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-hed-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>
flux-lora/simple-flat-illustration-shakker	flux-lora	2025-05-27T16:48:17Z	0	0	null	[ "lora", "text-to-image", "region:us" ]	text-to-image	2025-05-27T15:15:43Z	--- base_model: - shakker-custom-model pipeline_tag: text-to-image tags: - lora --- # F.1 \| Simple Flat Illustration - Shakker Original model link: https://www.shakker.ai/modelinfo/b052311f079c4a6fa2688bb0fcd7f1ba?versionUuid=beb4888300a64e848bb4070956c2ab4a Trigger word: `AYU`
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).
kavinda123321/speecht5_finetuned_english_ranil_aug2	kavinda123321	2025-05-27T16:45:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-27T16:44:52Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_english_ranil_aug2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_english_ranil_aug2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5833 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - 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: 20 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.5568 \| 1.0 \| 48 \| 0.6822 \| \| 0.4527 \| 2.0 \| 96 \| 0.6500 \| \| 0.4343 \| 3.0 \| 144 \| 0.6412 \| \| 0.4038 \| 4.0 \| 192 \| 0.6339 \| \| 0.4056 \| 5.0 \| 240 \| 0.6388 \| \| 0.3966 \| 6.0 \| 288 \| 0.6324 \| \| 0.3889 \| 7.0 \| 336 \| 0.6302 \| \| 0.3853 \| 8.0 \| 384 \| 0.6484 \| \| 0.3744 \| 9.0 \| 432 \| 0.6202 \| \| 0.3699 \| 10.0 \| 480 \| 0.6162 \| \| 0.3716 \| 11.0 \| 528 \| 0.6161 \| \| 0.365 \| 12.0 \| 576 \| 0.6149 \| \| 0.3631 \| 13.0 \| 624 \| 0.6110 \| \| 0.3597 \| 14.0 \| 672 \| 0.6109 \| \| 0.3597 \| 15.0 \| 720 \| 0.6112 \| \| 0.3547 \| 16.0 \| 768 \| 0.6050 \| \| 0.353 \| 17.0 \| 816 \| 0.6034 \| \| 0.348 \| 18.0 \| 864 \| 0.6015 \| \| 0.3449 \| 19.0 \| 912 \| 0.5975 \| \| 0.3432 \| 20.0 \| 960 \| 0.5983 \| \| 0.3436 \| 21.0 \| 1008 \| 0.6019 \| \| 0.3409 \| 22.0 \| 1056 \| 0.6016 \| \| 0.3379 \| 23.0 \| 1104 \| 0.5985 \| \| 0.3357 \| 24.0 \| 1152 \| 0.5970 \| \| 0.3316 \| 25.0 \| 1200 \| 0.5948 \| \| 0.3338 \| 26.0 \| 1248 \| 0.5991 \| \| 0.3336 \| 27.0 \| 1296 \| 0.5936 \| \| 0.3317 \| 28.0 \| 1344 \| 0.5867 \| \| 0.3293 \| 29.0 \| 1392 \| 0.5885 \| \| 0.3288 \| 30.0 \| 1440 \| 0.5884 \| \| 0.3289 \| 31.0 \| 1488 \| 0.5892 \| \| 0.3242 \| 32.0 \| 1536 \| 0.5892 \| \| 0.3253 \| 33.0 \| 1584 \| 0.5860 \| \| 0.3261 \| 34.0 \| 1632 \| 0.5860 \| \| 0.3253 \| 35.0 \| 1680 \| 0.5857 \| \| 0.3229 \| 36.0 \| 1728 \| 0.5863 \| \| 0.3226 \| 37.0 \| 1776 \| 0.5858 \| \| 0.3219 \| 38.0 \| 1824 \| 0.5899 \| \| 0.3186 \| 39.0 \| 1872 \| 0.5855 \| \| 0.3268 \| 39.1684 \| 1880 \| 0.5833 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.5 - Tokenizers 0.21.1
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}} } ```
Vombit/yolov10l_cs2	Vombit	2025-05-27T16:43:39Z	9	0	yolov10	[ "yolov10", "onnx", "ultralytics", "yolo", "object-detection", "pytorch", "cs2", "Counter Strike", "license:cc-by-nc-nd-4.0", "region:us" ]	object-detection	2024-09-19T20:04:40Z	--- license: cc-by-nc-nd-4.0 pipeline_tag: object-detection tags: - yolov10 - ultralytics - yolo - object-detection - pytorch - cs2 - Counter Strike --- Counter Strike 2 players detector ## Supported Labels ``` [ 'c', 'ch', 't', 'th' ] ``` ## All models in this series - [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb) - [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb) - [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb) - [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb) - [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb) - [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb) ## How to use ```python # load Yolo from ultralytics import YOLO # Load a pretrained YOLO model model = YOLO(r'weights\yolov**_cs2.pt') # Run inference on 'image.png' with arguments model.predict( 'image.png', save=True, device=0 ) ``` ## Predict info Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB) - yolov10l_cs2_fp16.engine (640x640 5 ts, 5 ths, 7.1ms) - yolov10l_cs2.engine (640x640 5 ts, 5 ths, 16.1ms) - yolov10l_cs2_fp16.onnx (640x640 5 ts, 5 ths, 337.2ms) - yolov10l_cs2.onnx (640x640 5 ts, 5 ths, 348.0ms) - yolov10l_cs2.pt (384x640 5 ts, 5 ths, 99.1ms) ## Dataset info Data from over 120 games, where the footage has been tagged in detail. ![image/jpg](https://huggingface.co/Vombit/yolov10l_cs2/resolve/main/labels.jpg) ![image/jpg](https://huggingface.co/Vombit/yolov10l_cs2/resolve/main/labels_correlogram.jpg) ## Train info The training took place over 150 epochs. ![image/png](https://huggingface.co/Vombit/yolov10l_cs2/resolve/main/results.png) You can also support me with a cup of coffee: [donate](https://vombit.serveblog.net/donation)
jzilcov/prompt_complexity_classifier	jzilcov	2025-05-27T16:42:51Z	0	0	null	[ "safetensors", "roberta", "en", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:mit", "region:us" ]	null	2025-05-27T16:29:44Z	--- license: mit language: - en base_model: - distilbert/distilroberta-base ---
Vombit/yolov10s_cs2	Vombit	2025-05-27T16:42:49Z	11	0	yolov10	[ "yolov10", "onnx", "ultralytics", "yolo", "object-detection", "pytorch", "cs2", "Counter Strike", "license:cc-by-nc-nd-4.0", "region:us" ]	object-detection	2024-09-19T20:03:40Z	--- license: cc-by-nc-nd-4.0 pipeline_tag: object-detection tags: - yolov10 - ultralytics - yolo - object-detection - pytorch - cs2 - Counter Strike --- Counter Strike 2 players detector ## Supported Labels ``` [ 'c', 'ch', 't', 'th' ] ``` ## All models in this series - [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb) - [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb) - [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb) - [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb) - [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb) - [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb) ## How to use ```python # load Yolo from ultralytics import YOLO # Load a pretrained YOLO model model = YOLO(r'weights\yolov**_cs2.pt') # Run inference on 'image.png' with arguments model.predict( 'image.png', save=True, device=0 ) ``` ## Predict info Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB) - yolov10s_cs2_fp16.engine (640x640 5 ts, 6 ths, 3.0ms) - yolov10s_cs2.engine (640x640 5 ts, 6 ths, 4.5ms) - yolov10s_cs2_fp16.onnx (640x640 5 ts, 6 ths, 80.4ms) - yolov10s_cs2.onnx (640x640 5 ts, 6 ths, 76.6ms) - yolov10s_cs2.pt (384x640 5 ts, 5 ths, 86.7ms) ## Dataset info Data from over 120 games, where the footage has been tagged in detail. ![image/jpg](https://huggingface.co/Vombit/yolov10s_cs2/resolve/main/labels.jpg) ![image/jpg](https://huggingface.co/Vombit/yolov10s_cs2/resolve/main/labels_correlogram.jpg) ## Train info The training took place over 150 epochs. ![image/png](https://huggingface.co/Vombit/yolov10s_cs2/resolve/main/results.png) You can also support me with a cup of coffee: [donate](https://vombit.serveblog.net/donation)
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)
Vombit/yolov10n_cs2	Vombit	2025-05-27T16:42:31Z	7	0	yolov10	[ "yolov10", "onnx", "ultralytics", "yolo", "object-detection", "pytorch", "cs2", "Counter Strike", "license:cc-by-nc-nd-4.0", "region:us" ]	object-detection	2024-09-19T20:02:38Z	--- license: cc-by-nc-nd-4.0 pipeline_tag: object-detection tags: - yolov10 - ultralytics - yolo - object-detection - pytorch - cs2 - Counter Strike --- Counter Strike 2 players detector ## Supported Labels ``` [ 'c', 'ch', 't', 'th' ] ``` ## All models in this series - [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb) - [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb) - [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb) - [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb) - [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb) - [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb) ## How to use ```python # load Yolo from ultralytics import YOLO # Load a pretrained YOLO model model = YOLO(r'weights\yolov**_cs2.pt') # Run inference on 'image.png' with arguments model.predict( 'image.png', save=True, device=0 ) ``` ## Predict info Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB) - yolov10n_cs2_fp16.engine (640x640 5 ts, 5 ths, 2.6ms) - yolov10n_cs2.engine (640x640 5 ts, 5 ths, 2.9ms) - yolov10n_cs2_fp16.onnx (640x640 5 ts, 5 ths, 32.6ms) - yolov10n_cs2.onnx (640x640 5 ts, 5 ths, 40.6ms) - yolov10n_cs2.pt (384x640 5 ts, 5 ths, 124.3ms) ## Dataset info Data from over 120 games, where the footage has been tagged in detail. ![image/jpg](https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/labels.jpg) ![image/jpg](https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/labels_correlogram.jpg) ## Train info The training took place over 150 epochs. ![image/png](https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/results.png) You can also support me with a cup of coffee: [donate](https://vombit.serveblog.net/donation)
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_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>
Mawdistical/Draconia-Overdrive-32B_EXL3_4.0bpw_H6	Mawdistical	2025-05-27T16:42:00Z	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", "4-bit", "region:us" ]	text-generation	2025-05-27T16:02:16Z	--- 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_3.0bpw_H6	Mawdistical	2025-05-27T16:41:51Z	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", "3-bit", "region:us" ]	text-generation	2025-05-27T15:58:23Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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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>
MattBou00/SmolLM-toxic-detox-ppo-1000updates	MattBou00	2025-05-27T16:40:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:40:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Diamantis99/OL56jaO	Diamantis99	2025-05-27T16:38:44Z	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	2025-05-27T16:38:41Z	--- 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": "mobilenet_v2", "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": 0.5323230624198914, "test_dataset_iou": 0.6163333654403687 } ] ``` ## 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)
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)
ObadaAlaqtash/my_llama3_model_eastern_caverns	ObadaAlaqtash	2025-05-27T12:27:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T12:27:06Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ObadaAlaqtash - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
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-base	ltg	2025-05-27T12:26:28Z	1,966	7	transformers	[ "transformers", "pytorch", "fill-mask", "BERT", "NorBERT", "Norwegian", "encoder", "custom_code", "no", "nb", "nn", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2023-03-02T21:38:09Z	--- language: - 'no' - nb - nn inference: false tags: - BERT - NorBERT - Norwegian - encoder license: apache-2.0 --- # NorBERT 3 base <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-base") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-base", 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.", } ```
root4k/Dolphin-Mistral-24B-Venice	root4k	2025-05-27T12:26:23Z	0	0	mlx	[ "mlx", "safetensors", "mistral", "text-generation", "conversational", "base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-05-27T11:38:46Z	--- license: apache-2.0 base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition pipeline_tag: text-generation library_name: mlx tags: - mlx ---
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.", } ```
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)
majdab4/dummy-model	majdab4	2025-05-27T12:23:34Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-27T12:23:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ChevellaShyam/emotion-transformer-model	ChevellaShyam	2025-05-27T12:23:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T12:22:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sayantan0013/Qwen3-0.6B-SFT	sayantan0013	2025-05-27T12:22:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:21:50Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mvsamsonov/speecht5_finetuned_voxpopuli_nl	mvsamsonov	2025-05-27T12:22:03Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-25T05:55:45Z	--- library_name: transformers tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl This model was trained from scratch on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4590 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - 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: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.6863 \| 0.8607 \| 200 \| 0.6124 \| \| 0.5721 \| 1.7230 \| 400 \| 0.5167 \| \| 0.5396 \| 2.5853 \| 600 \| 0.4984 \| \| 0.5289 \| 3.4476 \| 800 \| 0.4868 \| \| 0.5172 \| 4.3098 \| 1000 \| 0.4815 \| \| 0.5169 \| 5.1721 \| 1200 \| 0.4771 \| \| 0.5108 \| 6.0344 \| 1400 \| 0.4740 \| \| 0.5086 \| 6.8951 \| 1600 \| 0.4715 \| \| 0.5042 \| 7.7574 \| 1800 \| 0.4699 \| \| 0.4939 \| 8.6197 \| 2000 \| 0.4678 \| \| 0.4965 \| 9.4820 \| 2200 \| 0.4667 \| \| 0.5004 \| 10.3443 \| 2400 \| 0.4644 \| \| 0.4906 \| 11.2066 \| 2600 \| 0.4617 \| \| 0.4889 \| 12.0689 \| 2800 \| 0.4612 \| \| 0.493 \| 12.9295 \| 3000 \| 0.4601 \| \| 0.4893 \| 13.7918 \| 3200 \| 0.4599 \| \| 0.4894 \| 14.6541 \| 3400 \| 0.4600 \| \| 0.4922 \| 15.5164 \| 3600 \| 0.4594 \| \| 0.491 \| 16.3787 \| 3800 \| 0.4599 \| \| 0.482 \| 17.2410 \| 4000 \| 0.4590 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Emmzyel/Emmzy_Wealth	Emmzyel	2025-05-27T12:21:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T12:21:32Z	--- 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)
AlphaSurgeMaleEnhancement/AlphaSurgeMaleEnhancement	AlphaSurgeMaleEnhancement	2025-05-27T12:20:46Z	0	0	null	[ "region:us" ]	null	2025-05-27T12:11:12Z	## What Is Alpha Surge Male Enhancement? [Alpha Surge Male Enhancement](https://www.diginear.com/2PGQH1JJ/ZDFM5CX/) is a dietary supplement designed specifically for men looking to boost their sexual health, energy levels, and overall vitality. Available in gummy form, it provides a refreshing alternative to conventional capsules or tablets, which can be cumbersome or unappealing for some users. The product is marketed as a natural, non-GMO, and gluten-free formula, crafted in FDA-registered and GMP-certified facilities in the United States. This emphasis on quality manufacturing ensures that users receive a product that meets high safety and efficacy standards. The core promise of Alpha Surge Male Enhancement is to enhance male performance by addressing common issues such as erectile dysfunction, low libido, and reduced stamina. 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dongseon/q_noSlippery	dongseon	2025-05-27T12:16:24Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-27T12:16:19Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q_noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="dongseon/q_noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
MatteoBucc/passphrase-identification-roberta-base-qqp-epoch-4	MatteoBucc	2025-05-27T12:14:13Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:FacebookAI/roberta-base", "base_model:adapter:FacebookAI/roberta-base", "region:us" ]	null	2025-05-14T14:11:52Z	--- base_model: roberta-base 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 0.14.0
Hsianchengfun/pruned_30_dt_dp_100epoch	Hsianchengfun	2025-05-27T12:11:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:08:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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slecas/llama_8B_ibd_test_a	slecas	2025-05-27T12:11:13Z	0	0	transformers	[ "transformers", "safetensors", "trl", "sft", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:19:16Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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lisabdunlap/balanced_sft_long-1e4-systems-prompt_e2	lisabdunlap	2025-05-27T12:08:58Z	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	2025-05-27T12:08:01Z	--- 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)
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. 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aamijar/Llama-2-7b-hf-lora-r128-boolq-portlora-epochs0	aamijar	2025-05-27T12:07:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T12:07:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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Darkhn/llamatest-EXL2-4.58bpw-H6	Darkhn	2025-05-27T12:02:15Z	0	0	exllamav2	[ "exllamav2", "quantized", "license:mit", "region:us" ]	null	2025-05-27T11:38:08Z	--- library_name: exllamav2 license: mit tags: - exllamav2 - quantized --- # llamatest-EXL2-4.58bpw-H6 EXL2 quantized model of `unsloth/Llama-3.2-1B-Instruct` (the original base model). ## Quantization Details - Bits per weight (bpw): 4.58 - Head Bits: 6 - Calibration Source: Measurement derived from model weights (no explicit dataset calibration or provided measurement for this specific quantization pass). Quantized using the [exllamav2 library](https://github.com/turboderp/exllamav2).
TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GWlr-cosine	TanAlexanderlz	2025-05-27T12:01:19Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-27T10:49:47Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: ALL_RGBCROP_ori16F-8B16F-GWlr-cosine 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. --> # ALL_RGBCROP_ori16F-8B16F-GWlr-cosine This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3658 - Accuracy: 0.8623 Best Checkpoint : 240 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1152 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.6775 \| 0.0417 \| 48 \| 0.7147 \| 0.4695 \| \| 0.5877 \| 1.0417 \| 96 \| 0.6383 \| 0.6220 \| \| 0.3375 \| 2.0417 \| 144 \| 0.5176 \| 0.7317 \| \| 0.219 \| 3.0417 \| 192 \| 0.4915 \| 0.7805 \| \| 0.0698 \| 4.0417 \| 240 \| 0.5611 \| 0.8110 \| \| 0.0587 \| 5.0417 \| 288 \| 0.6506 \| 0.7927 \| \| 0.0194 \| 6.0417 \| 336 \| 0.7638 \| 0.7988 \| \| 0.0029 \| 7.0417 \| 384 \| 0.9139 \| 0.7805 \| \| 0.0023 \| 8.0417 \| 432 \| 0.9306 \| 0.7988 \| \| 0.0033 \| 9.0417 \| 480 \| 0.9203 \| 0.7988 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Mass-14/MNLP_M2_rag_model	Mass-14	2025-05-27T11:57:41Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T11:56:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
KetoBurner/KetoBurner	KetoBurner	2025-05-27T11:52:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T11:51:15Z	--- license: apache-2.0 --- ما هو Keto Burner؟ Keto Burner حبوب كبسولة لإنقاص الوزن، مصممة لدعم الأفراد الذين يسعون إلى نمط حياة صحي من خلال التحكم الطبيعي في الوزن. صُممت هذه الكبسولة خصيصًا لمن يعانون من الدهون العنيدة، وتقلبات مستويات الطاقة، وبطء عملية الأيض، لتكون حليفًا موثوقًا به في رحلة لياقتك. بخلاف الحلول قصيرة المدى أو الحميات الغذائية القاسية، تُركز Keto Burner كبسولة على تحسين قدرة جسمك على التحكم في وزنك بفعالية أكبر مع مرور الوقت. الموقع الرسمي:<a href="https://www.nutritionsee.com/ketourneunisia">www.KetoBurner.com</a> <p><a href="https://www.nutritionsee.com/ketourneunisia"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/05/Keto-Burner.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/ketourneunisia">اشترِ الآن! انقر على الرابط أدناه لمزيد من المعلومات واحصل على خصم ٥٠٪ الآن... سارع!</a> الموقع الرسمي:<a href="https://www.nutritionsee.com/ketourneunisia">www.KetoBurner.com</a>
BootesVoid/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2	BootesVoid	2025-05-27T11:50:47Z	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	2025-05-27T11:50:46Z	--- 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: lucy --- # Cmb6Fhkup04Helexpqayylopn_Cmb6Fmqxu04I1Lexpafcu98V2 <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 `lucy` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "lucy", "lora_weights": "https://huggingface.co/BootesVoid/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2/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/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2', weight_name='lora.safetensors') image = pipeline('lucy').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/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2/discussions) to add images that show off what you’ve made with this LoRA.
BKM1804/SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-phase1	BKM1804	2025-05-27T11:49:55Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/SmolLM-135M-Instruct", "base_model:finetune:unsloth/SmolLM-135M-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-26T11:10:01Z	--- base_model: unsloth/SmolLM-135M-Instruct library_name: transformers model_name: SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-phase1 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-phase1 This model is a fine-tuned version of [unsloth/SmolLM-135M-Instruct](https://huggingface.co/unsloth/SmolLM-135M-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="BKM1804/SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-phase1", 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/buikhacminh1804/sn56-sft-before-dpo-train/runs/mwlnysly) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
LakshmiDataScientist/peft_model	LakshmiDataScientist	2025-05-27T11:49:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:49:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
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. 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DaniloNeto/roco_qlora_qwen2	DaniloNeto	2025-05-27T11:47:11Z	5	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T00:50:38Z	--- base_model: unsloth/qwen2-vl-2b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: DaniloNeto - License: apache-2.0 - Finetuned from model : unsloth/qwen2-vl-2b-instruct-bnb-4bit This qwen2_vl 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)
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}, } ```
JesseLiu/llama32-1b-kpath-partial-naive-grpo-lora	JesseLiu	2025-05-27T11:44:33Z	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	2025-05-27T11:44:07Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
aamijar/Llama-2-7b-hf-lora-r8-boolq-portlora	aamijar	2025-05-27T11:44:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:44:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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stochastic-parrots/MNLP_M2_dpo_model	stochastic-parrots	2025-05-27T11:44:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:43:06Z	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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VerifiedPrompts/CNTXT-Filter-Prompt-Opt	VerifiedPrompts	2025-05-27T11:43:28Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "prompt-filtering", "moderation", "en", "dataset:VerifiedPrompts/cntxt-class-final", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T07:55:35Z	--- license: mit tags: - text-classification - prompt-filtering - moderation - distilbert - transformers datasets: - VerifiedPrompts/cntxt-class-final language: - en pipeline_tag: text-classification widget: - text: "Write a LinkedIn post about eco-friendly tech for Gen Z entrepreneurs." example_title: Context-rich prompt - text: "Write something" example_title: Vague prompt --- # 📘 Model Card: CNTXT-Filter-Prompt-Opt ## 🔍 Model Overview CNTXT-Filter-Prompt-Opt is a lightweight, high-accuracy text classification model designed to evaluate the contextual completeness of user prompts submitted to LLMs. It acts as a gatekeeper before generation, helping eliminate vague or spam-like input and ensuring only quality prompts proceed to LLM2. - Base model: `distilbert-base-uncased` - Trained on: 200k labeled prompts - Purpose: Prompt validation, spam filtering, and context enforcement --- ## 🎯 Intended Use This model is intended for: - Pre-processing prompts before LLM2 generation - Blocking unclear or context-poor requests - Structuring user input pipelines in AI apps, bots, and assistants --- ## 🔢 Labels The model classifies prompts into 3 categories: \| Label \| Description \| \|-------\|-------------\| \| `has context` \| Prompt is clear, actionable, and self-contained \| \| `missing platform, audience, budget, goal` \| Prompt lacks structural clarity \| \| `Intent is unclear, Please input more context` \| Vague or incoherent prompt \| --- ## 📊 Training Details - Model: `distilbert-base-uncased` - Training method: Hugging Face AutoTrain - Dataset size: 200,000 prompts (curated, curriculum style) - Epochs: 3 - Batch size: 8 - Max seq length: 128 - Mixed Precision: `fp16` - LoRA: ❌ Disabled - Optimizer: AdamW --- ## ✅ Evaluation \| Metric \| Score \| \|--------\|-------\| \| Accuracy \| 1.0 \| \| F1 (macro/micro/weighted) \| 1.0 \| \| Precision / Recall \| 1.0 \| \| Validation Loss \| 0.0 \| The model generalizes extremely well on all validation samples. --- ## ⚙️ How to Use ```python from transformers import pipeline classifier = pipeline("text-classification", model="VerifiedPrompts/CNTXT-Filter-Prompt-Opt") prompt = "Write a business plan for a freelance app in Canada." result = classifier(prompt) print(result) # [{'label': 'has context', 'score': 0.98}]
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).
alpcaferoglu/Qwen2.5-Coder-3B-Instruct_bd_cs_t2sws-t2s_r64_a64_e1_bs2_gas4_lr0.0002_sftreason	alpcaferoglu	2025-05-27T11:38:42Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T02:27:20Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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.
nickname19/First_T5	nickname19	2025-05-27T11:34:45Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-27T11:33:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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07-Sophie-Rain-Spider-Man-Videos/Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video	07-Sophie-Rain-Spider-Man-Videos	2025-05-27T11:34:40Z	0	0	null	[ "region:us" ]	null	2025-05-27T11:34:19Z	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
nattkorat/scibert-base-uncased-ner	nattkorat	2025-05-27T11:33:34Z	17	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:allenai/scibert_scivocab_uncased", "base_model:finetune:allenai/scibert_scivocab_uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-17T07:22:26Z	--- library_name: transformers base_model: allenai/scibert_scivocab_uncased tags: - generated_from_trainer model-index: - name: scibert-base-uncased-ner 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. --> # scibert-base-uncased-ner This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0191 - Cases: {'precision': 0.9767981438515081, 'recall': 0.967816091954023, 'f1': 0.972286374133949, 'number': 435} - Country: {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} - Date: {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} - Deaths: {'precision': 0.9529411764705882, 'recall': 0.9501466275659824, 'f1': 0.9515418502202643, 'number': 341} - Virus: {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} - Overall Precision: 0.9760 - Overall Recall: 0.9796 - Overall F1: 0.9778 - Overall Accuracy: 0.9923 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cases \| Country \| Date \| Deaths \| Virus \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------------------------------------------------------------------------------:\|:-----------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| No log \| 1.0 \| 291 \| 0.0411 \| {'precision': 0.90744920993228, 'recall': 0.9241379310344827, 'f1': 0.9157175398633258, 'number': 435} \| {'precision': 0.9699646643109541, 'recall': 1.0, 'f1': 0.9847533632286996, 'number': 549} \| {'precision': 0.9149305555555556, 'recall': 0.9070567986230637, 'f1': 0.9109766637856526, 'number': 581} \| {'precision': 0.8830769230769231, 'recall': 0.841642228739003, 'f1': 0.8618618618618619, 'number': 341} \| {'precision': 0.9889908256880734, 'recall': 0.992633517495396, 'f1': 0.9908088235294119, 'number': 543} \| 0.9385 \| 0.9408 \| 0.9396 \| 0.9861 \| \| 0.1005 \| 2.0 \| 582 \| 0.0291 \| {'precision': 0.9733656174334141, 'recall': 0.9241379310344827, 'f1': 0.9481132075471699, 'number': 435} \| {'precision': 0.9699646643109541, 'recall': 1.0, 'f1': 0.9847533632286996, 'number': 549} \| {'precision': 0.9512195121951219, 'recall': 0.9397590361445783, 'f1': 0.9454545454545454, 'number': 581} \| {'precision': 0.9161849710982659, 'recall': 0.9296187683284457, 'f1': 0.9228529839883551, 'number': 341} \| {'precision': 0.9889908256880734, 'recall': 0.992633517495396, 'f1': 0.9908088235294119, 'number': 543} \| 0.9628 \| 0.9608 \| 0.9618 \| 0.9910 \| \| 0.1005 \| 3.0 \| 873 \| 0.0221 \| {'precision': 0.9764705882352941, 'recall': 0.9540229885057471, 'f1': 0.9651162790697674, 'number': 435} \| {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} \| {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} \| {'precision': 0.9552238805970149, 'recall': 0.9384164222873901, 'f1': 0.9467455621301775, 'number': 341} \| {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} \| 0.9763 \| 0.9755 \| 0.9759 \| 0.9929 \| \| 0.0237 \| 4.0 \| 1164 \| 0.0216 \| {'precision': 0.9789719626168224, 'recall': 0.9632183908045977, 'f1': 0.9710312862108922, 'number': 435} \| {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} \| {'precision': 0.9740034662045061, 'recall': 0.9672977624784854, 'f1': 0.9706390328151987, 'number': 581} \| {'precision': 0.9502923976608187, 'recall': 0.9530791788856305, 'f1': 0.951683748169839, 'number': 341} \| {'precision': 0.9944954128440368, 'recall': 0.998158379373849, 'f1': 0.9963235294117647, 'number': 543} \| 0.9764 \| 0.9788 \| 0.9776 \| 0.9921 \| \| 0.0237 \| 5.0 \| 1455 \| 0.0191 \| {'precision': 0.9767981438515081, 'recall': 0.967816091954023, 'f1': 0.972286374133949, 'number': 435} \| {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} \| {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} \| {'precision': 0.9529411764705882, 'recall': 0.9501466275659824, 'f1': 0.9515418502202643, 'number': 341} \| {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} \| 0.9760 \| 0.9796 \| 0.9778 \| 0.9923 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.0 - Tokenizers 0.21.1
Cloudmaster/Llama-3.2-3B-torchao-final01	Cloudmaster	2025-05-27T11:31:26Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-27T11:27:37Z	--- 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]
ganesh004/ppo-LunarLander-v2-TEST	ganesh004	2025-05-27T11:30:47Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-27T11:30:27Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 245.31 +/- 21.85 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
nattkorat/biobert-base-uncased-ner	nattkorat	2025-05-27T11:30:41Z	12	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:dmis-lab/biobert-v1.1", "base_model:finetune:dmis-lab/biobert-v1.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-17T07:40:16Z	--- library_name: transformers base_model: dmis-lab/biobert-v1.1 tags: - generated_from_trainer model-index: - name: biobert-base-uncased-ner 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. --> # biobert-base-uncased-ner This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0299 - Cases: {'precision': 0.963963963963964, 'recall': 0.9705215419501134, 'f1': 0.9672316384180792, 'number': 441} - Country: {'precision': 0.9926062846580407, 'recall': 0.9962894248608535, 'f1': 0.9944444444444445, 'number': 539} - Date: {'precision': 0.9637931034482758, 'recall': 0.9704861111111112, 'f1': 0.9671280276816608, 'number': 576} - Deaths: {'precision': 0.9224376731301939, 'recall': 0.9596541786743515, 'f1': 0.9406779661016949, 'number': 347} - Virus: {'precision': 0.9927140255009107, 'recall': 0.9927140255009107, 'f1': 0.9927140255009107, 'number': 549} - Overall Precision: 0.9705 - Overall Recall: 0.9796 - Overall F1: 0.9750 - Overall Accuracy: 0.9923 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cases \| Country \| Date \| Deaths \| Virus \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| No log \| 1.0 \| 291 \| 0.0329 \| {'precision': 0.9712918660287081, 'recall': 0.9206349206349206, 'f1': 0.9452852153667054, 'number': 441} \| {'precision': 0.988950276243094, 'recall': 0.9962894248608535, 'f1': 0.9926062846580408, 'number': 539} \| {'precision': 0.9498269896193772, 'recall': 0.953125, 'f1': 0.951473136915078, 'number': 576} \| {'precision': 0.9388379204892966, 'recall': 0.8847262247838616, 'f1': 0.9109792284866469, 'number': 347} \| {'precision': 0.9926873857404022, 'recall': 0.9890710382513661, 'f1': 0.990875912408759, 'number': 549} \| 0.9706 \| 0.9551 \| 0.9628 \| 0.9901 \| \| 0.0216 \| 2.0 \| 582 \| 0.0336 \| {'precision': 0.9527027027027027, 'recall': 0.9591836734693877, 'f1': 0.9559322033898305, 'number': 441} \| {'precision': 0.9907749077490775, 'recall': 0.9962894248608535, 'f1': 0.9935245143385755, 'number': 539} \| {'precision': 0.9616724738675958, 'recall': 0.9583333333333334, 'f1': 0.96, 'number': 576} \| {'precision': 0.9010989010989011, 'recall': 0.9452449567723343, 'f1': 0.9226441631504924, 'number': 347} \| {'precision': 0.9908759124087592, 'recall': 0.9890710382513661, 'f1': 0.9899726526891522, 'number': 549} \| 0.9640 \| 0.9719 \| 0.9679 \| 0.9907 \| \| 0.0216 \| 3.0 \| 873 \| 0.0345 \| {'precision': 0.9555555555555556, 'recall': 0.9750566893424036, 'f1': 0.9652076318742986, 'number': 441} \| {'precision': 0.9926062846580407, 'recall': 0.9962894248608535, 'f1': 0.9944444444444445, 'number': 539} \| {'precision': 0.9536082474226805, 'recall': 0.9635416666666666, 'f1': 0.9585492227979275, 'number': 576} \| {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347} \| {'precision': 0.990909090909091, 'recall': 0.9927140255009107, 'f1': 0.991810737033667, 'number': 549} \| 0.9649 \| 0.9759 \| 0.9704 \| 0.9914 \| \| 0.0126 \| 4.0 \| 1164 \| 0.0292 \| {'precision': 0.9682539682539683, 'recall': 0.9682539682539683, 'f1': 0.9682539682539683, 'number': 441} \| {'precision': 0.9907749077490775, 'recall': 0.9962894248608535, 'f1': 0.9935245143385755, 'number': 539} \| {'precision': 0.9655172413793104, 'recall': 0.9722222222222222, 'f1': 0.9688581314878894, 'number': 576} \| {'precision': 0.9301675977653632, 'recall': 0.9596541786743515, 'f1': 0.9446808510638297, 'number': 347} \| {'precision': 0.9927140255009107, 'recall': 0.9927140255009107, 'f1': 0.9927140255009107, 'number': 549} \| 0.9725 \| 0.9796 \| 0.9760 \| 0.9925 \| \| 0.0126 \| 5.0 \| 1455 \| 0.0299 \| {'precision': 0.963963963963964, 'recall': 0.9705215419501134, 'f1': 0.9672316384180792, 'number': 441} \| {'precision': 0.9926062846580407, 'recall': 0.9962894248608535, 'f1': 0.9944444444444445, 'number': 539} \| {'precision': 0.9637931034482758, 'recall': 0.9704861111111112, 'f1': 0.9671280276816608, 'number': 576} \| {'precision': 0.9224376731301939, 'recall': 0.9596541786743515, 'f1': 0.9406779661016949, 'number': 347} \| {'precision': 0.9927140255009107, 'recall': 0.9927140255009107, 'f1': 0.9927140255009107, 'number': 549} \| 0.9705 \| 0.9796 \| 0.9750 \| 0.9923 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.0 - Tokenizers 0.21.1
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.
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]
nattkorat/bert-base-uncased-ner	nattkorat	2025-05-27T11:26:12Z	33	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-07T08:25:41Z	--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer model-index: - name: bert-base-uncased-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-ner This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0459 - Cases: {'precision': 0.9311111111111111, 'recall': 0.95662100456621, 'f1': 0.9436936936936937, 'number': 438} - Country: {'precision': 0.9640933572710951, 'recall': 0.9926062846580407, 'f1': 0.9781420765027322, 'number': 541} - Date: {'precision': 0.9480968858131488, 'recall': 0.9547038327526133, 'f1': 0.951388888888889, 'number': 574} - Deaths: {'precision': 0.877906976744186, 'recall': 0.8961424332344213, 'f1': 0.8869309838472834, 'number': 337} - Virus: {'precision': 0.9526315789473684, 'recall': 0.985480943738657, 'f1': 0.9687778768956289, 'number': 551} - Overall Precision: 0.9400 - Overall Recall: 0.9623 - Overall F1: 0.9510 - Overall Accuracy: 0.9827 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cases \| Country \| Date \| Deaths \| Virus \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| No log \| 1.0 \| 291 \| 0.0806 \| {'precision': 0.9074889867841409, 'recall': 0.9406392694063926, 'f1': 0.9237668161434976, 'number': 438} \| {'precision': 0.9853479853479854, 'recall': 0.9944547134935305, 'f1': 0.9898804047838086, 'number': 541} \| {'precision': 0.9320557491289199, 'recall': 0.9320557491289199, 'f1': 0.9320557491289199, 'number': 574} \| {'precision': 0.8575757575757575, 'recall': 0.8397626112759644, 'f1': 0.848575712143928, 'number': 337} \| {'precision': 0.9526315789473684, 'recall': 0.985480943738657, 'f1': 0.9687778768956289, 'number': 551} \| 0.9341 \| 0.9467 \| 0.9404 \| 0.9778 \| \| 0.1433 \| 2.0 \| 582 \| 0.0586 \| {'precision': 0.9280898876404494, 'recall': 0.9429223744292238, 'f1': 0.9354473386183466, 'number': 438} \| {'precision': 0.9781818181818182, 'recall': 0.9944547134935305, 'f1': 0.9862511457378552, 'number': 541} \| {'precision': 0.9363166953528399, 'recall': 0.9477351916376306, 'f1': 0.941991341991342, 'number': 574} \| {'precision': 0.8662790697674418, 'recall': 0.884272997032641, 'f1': 0.8751835535976507, 'number': 337} \| {'precision': 0.9627659574468085, 'recall': 0.985480943738657, 'f1': 0.9739910313901345, 'number': 551} \| 0.9404 \| 0.9570 \| 0.9486 \| 0.9811 \| \| 0.1433 \| 3.0 \| 873 \| 0.0482 \| {'precision': 0.9317180616740088, 'recall': 0.9657534246575342, 'f1': 0.9484304932735426, 'number': 438} \| {'precision': 0.9728260869565217, 'recall': 0.9926062846580407, 'f1': 0.9826166514181153, 'number': 541} \| {'precision': 0.9463667820069204, 'recall': 0.9529616724738676, 'f1': 0.9496527777777778, 'number': 574} \| {'precision': 0.8922155688622755, 'recall': 0.884272997032641, 'f1': 0.8882265275707899, 'number': 337} \| {'precision': 0.9410745233968805, 'recall': 0.985480943738657, 'f1': 0.9627659574468086, 'number': 551} \| 0.9411 \| 0.9619 \| 0.9514 \| 0.9823 \| \| 0.033 \| 4.0 \| 1164 \| 0.0492 \| {'precision': 0.9395973154362416, 'recall': 0.958904109589041, 'f1': 0.9491525423728814, 'number': 438} \| {'precision': 0.9640933572710951, 'recall': 0.9926062846580407, 'f1': 0.9781420765027322, 'number': 541} \| {'precision': 0.9515570934256056, 'recall': 0.9581881533101045, 'f1': 0.9548611111111112, 'number': 574} \| {'precision': 0.8753623188405797, 'recall': 0.8961424332344213, 'f1': 0.8856304985337242, 'number': 337} \| {'precision': 0.9593639575971732, 'recall': 0.985480943738657, 'f1': 0.97224709042077, 'number': 551} \| 0.9434 \| 0.9635 \| 0.9534 \| 0.9830 \| \| 0.033 \| 5.0 \| 1455 \| 0.0459 \| {'precision': 0.9311111111111111, 'recall': 0.95662100456621, 'f1': 0.9436936936936937, 'number': 438} \| {'precision': 0.9640933572710951, 'recall': 0.9926062846580407, 'f1': 0.9781420765027322, 'number': 541} \| {'precision': 0.9480968858131488, 'recall': 0.9547038327526133, 'f1': 0.951388888888889, 'number': 574} \| {'precision': 0.877906976744186, 'recall': 0.8961424332344213, 'f1': 0.8869309838472834, 'number': 337} \| {'precision': 0.9526315789473684, 'recall': 0.985480943738657, 'f1': 0.9687778768956289, 'number': 551} \| 0.9400 \| 0.9623 \| 0.9510 \| 0.9827 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.0 - Tokenizers 0.21.1
Mass-14/MNLP_M2_document_encoder	Mass-14	2025-05-27T11:25:54Z	0	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T11:25:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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