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train · 975k rows

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
cripong/HUANG	cripong	2025-06-04T14:16:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-04T14:16:58Z	--- license: apache-2.0 ---
victors3136/asr-model-5k-00it-00sp	victors3136	2025-06-04T14:16:55Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-04T14:07:26Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer model-index: - name: asr-model-5k-00it-00sp 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. --> # asr-model-5k-00it-00sp This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1787 \| 1.0 \| 450 \| 0.1347 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
pictgensupport/athleisure	pictgensupport	2025-06-04T14:16:39Z	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	2025-06-04T14:16:35Z	--- 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: athleisure --- # Athleisure <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `athleisure` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/athleisure', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
charun45/fdgfgfgfdd	charun45	2025-06-04T14:16:24Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-04T14:16:24Z	--- license: apache-2.0 ---
LeonGuertler/Qwen3-4B-batch-4-experiment-24-old-step_000300	LeonGuertler	2025-06-04T14:14:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T14:09:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
thevan2404/codeT5_phase1_8ep	thevan2404	2025-06-04T14:13:26Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:Salesforce/codet5-base", "base_model:finetune:Salesforce/codet5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T03:15:53Z	--- library_name: transformers license: apache-2.0 base_model: Salesforce/codet5-base tags: - generated_from_trainer model-index: - name: codeT5_phase1_8ep 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. --> # codeT5_phase1_8ep This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 14 - eval_batch_size: 4 - 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: 8 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
ClaMncDexter/gemma-3-4b-it-unsloth-bnb-4bit-float16	ClaMncDexter	2025-06-04T14:11:31Z	18	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-30T17:56:47Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: ClaMncDexter - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-ction-v0-1-OnlineIPO2-lora-0604063354-epoch-1	vectorzhou	2025-06-04T14:10:42Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "text-generation", "fine-tuned", "trl", "extra-gradient", "conversational", "dataset:OpenRLHF/prompt-collection-v0.1", "arxiv:2503.08942", "base_model:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT", "base_model:finetune:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T14:10:34Z	--- base_model: vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT datasets: OpenRLHF/prompt-collection-v0.1 library_name: transformers model_name: Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-OnlineIPO2-lora tags: - generated_from_trainer - text-generation - fine-tuned - trl - extra-gradient licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-OnlineIPO2-lora This model is a fine-tuned version of [vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT](https://huggingface.co/vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT) on the [OpenRLHF/prompt-collection-v0.1](https://huggingface.co/datasets/OpenRLHF/prompt-collection-v0.1) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-ction-v0-1-OnlineIPO2-lora-0604063354-epoch-1", 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/zhourunlongvector/nlhf/runs/kuktsgzu) This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942). ### Framework versions - TRL: 0.13.0 - Transformers: 4.48.0 - Pytorch: 2.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite Extragradient as: ```bibtex @misc{zhou2025extragradientpreferenceoptimizationegpo, title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback}, author={Runlong Zhou and Maryam Fazel and Simon S. Du}, year={2025}, eprint={2503.08942}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.08942}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
minket06/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF	minket06	2025-06-04T14:10:33Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", "base_model:quantized:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-04T14:09:47Z	--- license: other license_name: deepseek-license license_link: LICENSE tags: - llama-cpp - gguf-my-repo base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct --- # minket06/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo minket06/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo minket06/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo minket06/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo minket06/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -c 2048 ```
matyaydin/rag_final_inshallah	matyaydin	2025-06-04T14:08:34Z	7	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	2025-06-03T16:06:37Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: rag_final_inshallah results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rag_final_inshallah This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.0
thoddnn/vdr-2b-multi-v1-test	thoddnn	2025-06-04T14:07:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "internvl", "custom_code", "mlx", "conversational", "multilingual", "dataset:OpenGVLab/MMPR-v1.2", "base_model:OpenGVLab/InternVL3-1B-Instruct", "base_model:finetune:OpenGVLab/InternVL3-1B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-04T13:55:01Z	--- license: apache-2.0 license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE pipeline_tag: image-text-to-text library_name: transformers base_model: - OpenGVLab/InternVL3-1B-Instruct base_model_relation: finetune datasets: - OpenGVLab/MMPR-v1.2 language: - multilingual tags: - internvl - custom_code - mlx --- # thoddnn/vdr-2b-multi-v1-test This model was converted to MLX format from [`llamaindex/vdr-2b-multi-v1`]() using mlx-vlm version 0.1.25. Refer to the [original model card](https://huggingface.co/llamaindex/vdr-2b-multi-v1) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model thoddnn/vdr-2b-multi-v1-test --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```
moazeldegwy/Qwen3-1.7B-CoachAgent-LoRA-Adapters	moazeldegwy	2025-06-04T14:07:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-1.7B", "base_model:finetune:unsloth/Qwen3-1.7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-04T12:09:03Z	--- base_model: unsloth/Qwen3-1.7B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: moazeldegwy - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-1.7B 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)
Jiminiya/phi3_exp_2-3-0604	Jiminiya	2025-06-04T14:07:26Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:22:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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JokerJokerJoker/plr46	JokerJokerJoker	2025-06-04T14:06:40Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T14:05:29Z	--- 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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Aeabds/qwen1.5-chat-lora-adapter	Aeabds	2025-06-04T14:06:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-04T14:03:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AngelRaychev/1.5B-policy-iteration_5	AngelRaychev	2025-06-04T14:06:11Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/1.5B-policy-iteration_4", "base_model:finetune:AngelRaychev/1.5B-policy-iteration_4", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T14:00:57Z	--- base_model: AngelRaychev/1.5B-policy-iteration_4 library_name: transformers model_name: 1.5B-policy-iteration_5 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 1.5B-policy-iteration_5 This model is a fine-tuned version of [AngelRaychev/1.5B-policy-iteration_4](https://huggingface.co/AngelRaychev/1.5B-policy-iteration_4). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AngelRaychev/1.5B-policy-iteration_5", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
Yiliazhu/ppo-Huggy	Yiliazhu	2025-06-04T14:03:27Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-06-04T14:03:15Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Yiliazhu/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
JokerJokerJoker/plr43	JokerJokerJoker	2025-06-04T14:02:42Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T14:01:35Z	--- 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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SwayStar123/imagenet1k_invae-latents_dinov2_pca	SwayStar123	2025-06-04T14:02:23Z	0	0	null	[ "region:us" ]	null	2025-06-03T14:41:51Z	torch-pca weights for dinov2 giant with registers
gabidbr/maedaHD	gabidbr	2025-06-04T14:02:13Z	0	0	null	[ "text-to-image", "flux", "lora", "genfill.io", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	2025-06-04T13:39:39Z	--- tags: - text-to-image - flux - lora - genfill.io base_model: black-forest-labs/FLUX.1-dev instance_prompt: MaedAI license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md license: apache-2.0 language: - en --- # MaedaHD MaedaHD is a Flux LoRA model trained on high-definition images, offering the highest image quality to date. Instead of traditional cropping methods, the images were bucketed, providing increased flexibility in aspect ratio and ensuring a broader range of detail and fidelity. <Gallery /> ## Trigger words You should use `MaedAI` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
0xmlm/qwen2_05_smol-smoltalk	0xmlm	2025-06-04T14:01:16Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-04T13:50:51Z	--- license: apache-2.0 ---
JokerJokerJoker/plr41	JokerJokerJoker	2025-06-04T14:01:01Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T13:59:56Z	--- 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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doomslayer2022/q-Taxi-v3	doomslayer2022	2025-06-04T14:00:59Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-04T14:00:57Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="doomslayer2022/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
JokerJokerJoker/plr38	JokerJokerJoker	2025-06-04T13:55:57Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T13:54:42Z	--- 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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yamatazen/HMS-Fusion-12B-Lorablated	yamatazen	2025-06-04T13:53:57Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lorablated", "conversational", "en", "ja", "base_model:nbeerbower/Mistral-Nemo-12B-abliterated-LORA", "base_model:merge:nbeerbower/Mistral-Nemo-12B-abliterated-LORA", "base_model:yamatazen/HMS-Fusion-12B", "base_model:merge:yamatazen/HMS-Fusion-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:20:38Z	--- language: - en - ja base_model: - yamatazen/HMS-Fusion-12B - nbeerbower/Mistral-Nemo-12B-abliterated-LORA library_name: transformers tags: - merge - lorablated --- ![image/png](https://huggingface.co/yamatazen/HMS-Fusion-12B-Lorablated/resolve/main/HMS-Fusion-12B-Lorablated.png?download=true) # Merged Model This model is a combination of: - Base Model: `yamatazen/HMS-Fusion-12B` - LoRA Adapter: `nbeerbower/Mistral-Nemo-12B-abliterated-LORA` The model is saved in `bfloat16` format and is ready for deployment or fine-tuning.
natix-miner23/streetvision	natix-miner23	2025-06-04T13:53:33Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-30T16:53:04Z	--- 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. 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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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raghu96/paligemma_vqav2	raghu96	2025-06-04T13:53:30Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:google/paligemma2-3b-pt-224", "base_model:adapter:google/paligemma2-3b-pt-224", "license:gemma", "region:us" ]	null	2025-06-03T14:49:22Z	--- library_name: peft license: gemma base_model: google/paligemma2-3b-pt-224 tags: - generated_from_trainer model-index: - name: paligemma_vqav2 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. --> # paligemma_vqav2 This model is a fine-tuned version of [google/paligemma2-3b-pt-224](https://huggingface.co/google/paligemma2-3b-pt-224) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 200 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.53.0.dev0 - Pytorch 2.4.1+cu121 - Datasets 3.6.0 - Tokenizers 0.21.1
tdnathmlenthusiast/orpheus_3B_ft_elisa_full_model	tdnathmlenthusiast	2025-06-04T13:53:05Z	0	0	null	[ "safetensors", "llama", "unsloth", "license:apache-2.0", "region:us" ]	null	2025-06-04T13:31:29Z	--- license: apache-2.0 tags: - unsloth ---
avey-ai/avey1-tokenizer-it	avey-ai	2025-06-04T13:52:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-04T13:47:39Z	--- license: apache-2.0 ---
Diamantis99/RrEUoSu	Diamantis99	2025-06-04T13:52:16Z	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	2025-06-04T13:51:59Z	--- 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 --- # DeepLabV3Plus 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": "resnext101_32x8d", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 256, "decoder_atrous_rates": (12, 24, 36), "decoder_aspp_separable": True, "decoder_aspp_dropout": 0.5, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.857452929019928, "test_dataset_iou": 0.8823063969612122 } ] ``` ## 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)
BootesVoid/cmbhwxgmk089vkfxs6z5cyfu3_cmbhwz3z208a2kfxsl908hqsz	BootesVoid	2025-06-04T13:50:52Z	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	2025-06-04T13:50:49Z	--- 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: NUDES --- # Cmbhwxgmk089Vkfxs6Z5Cyfu3_Cmbhwz3Z208A2Kfxsl908Hqsz <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 `NUDES` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NUDES", "lora_weights": "https://huggingface.co/BootesVoid/cmbhwxgmk089vkfxs6z5cyfu3_cmbhwz3z208a2kfxsl908hqsz/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/cmbhwxgmk089vkfxs6z5cyfu3_cmbhwz3z208a2kfxsl908hqsz', weight_name='lora.safetensors') image = pipeline('NUDES').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/cmbhwxgmk089vkfxs6z5cyfu3_cmbhwz3z208a2kfxsl908hqsz/discussions) to add images that show off what you’ve made with this LoRA.
avey-ai/avey1-tokenizer-base	avey-ai	2025-06-04T13:47:14Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-04T13:46:52Z	--- license: apache-2.0 ---
GingerBled/mcqa_m3_v2	GingerBled	2025-06-04T13:47:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:46:38Z	--- 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]
davgauch/MNLP_M3_mcqa_mixed_rationale_v7	davgauch	2025-06-04T13:46:55Z	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	2025-06-04T13:17:35Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M3_mcqa_mixed_rationale_v7 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_M3_mcqa_mixed_rationale_v7 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: 0.0482 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0386 \| 0.2938 \| 200 \| 0.0611 \| \| 0.0544 \| 0.5876 \| 400 \| 0.0532 \| \| 0.0482 \| 0.8814 \| 600 \| 0.0477 \| \| 0.0478 \| 1.1763 \| 800 \| 0.0486 \| \| 0.0332 \| 1.4701 \| 1000 \| 0.0482 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.0
AngelRaychev/1.5B-policy-iteration_4	AngelRaychev	2025-06-04T13:46:53Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/1.5B-policy-iteration_3", "base_model:finetune:AngelRaychev/1.5B-policy-iteration_3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:45:03Z	--- base_model: AngelRaychev/1.5B-policy-iteration_3 library_name: transformers model_name: 1.5B-policy-iteration_4 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 1.5B-policy-iteration_4 This model is a fine-tuned version of [AngelRaychev/1.5B-policy-iteration_3](https://huggingface.co/AngelRaychev/1.5B-policy-iteration_3). 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="AngelRaychev/1.5B-policy-iteration_4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
JokerJokerJoker/plr55	JokerJokerJoker	2025-06-04T13:46:01Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T13:44:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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dimarkov/mlpox	dimarkov	2025-06-04T13:45:39Z	0	0	null	[ "region:us" ]	null	2025-06-04T13:39:53Z	This repository contains an Equinox implementation of a Deep MLP-Bottleneck models from https://github.com/gregorbachmann/scaling_mlps repo. The model weigths were loaded from their pytorch checkpoints and converted to a corresponding Equinox implementation in the following https://github.com/dimarkov/mlpox repo. --- license: MIT ---
Thomaschtl/test2	Thomaschtl	2025-06-04T13:45:36Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "quantization", "neural-compressor", "qat", "quantization-aware-training", "conversational", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:45:01Z	--- license: apache-2.0 base_model: Qwen/Qwen3-0.6B tags: - quantization - neural-compressor - qat - quantization-aware-training - qwen3 library_name: transformers pipeline_tag: text-generation --- # Qwen3-0.6B Quantized with QAT This model is a quantized version of `Qwen/Qwen3-0.6B` using Quantization Aware Training (QAT) with Intel Neural Compressor. ## 🚀 Model Details - Base Model: Qwen/Qwen3-0.6B - Quantization Method: Quantization Aware Training (QAT) - Framework: Intel Neural Compressor - Model Size: Significantly reduced from original - Performance: Maintains quality while improving efficiency ## 📊 Benefits ✅ Smaller model size - Reduced storage requirements ✅ Faster inference - Optimized for deployment ✅ Lower memory usage - More efficient resource utilization ✅ Maintained quality - QAT preserves model performance ## 💻 Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Load the quantized model model = AutoModelForCausalLM.from_pretrained("Thomaschtl/qwen3-0.6b-qat-test") tokenizer = AutoTokenizer.from_pretrained("Thomaschtl/qwen3-0.6b-qat-test") # Generate text prompt = "The future of AI is" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs, max_length=100, do_sample=True, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## ⚙️ Quantization Details - Training Method: Quantization Aware Training - Optimizer: AdamW - Learning Rate: 5e-5 - Batch Size: 2 - Epochs**: 1 (demo configuration) ## 🔧 Technical Info This model was quantized using Intel Neural Compressor's QAT approach, which: 1. Simulates quantization during training 2. Allows model weights to adapt to quantization 3. Maintains better accuracy than post-training quantization ## 📝 Citation If you use this model, please cite: ``` @misc{qwen3-qat, title={Qwen3-0.6B Quantized with QAT}, author={Thomaschtl}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/Thomaschtl/qwen3-0.6b-qat-test} } ``` ## ⚖️ License This model follows the same license as the base model (Apache 2.0).
shirro/Qwen3-4B-SFT-GRPO	shirro	2025-06-04T13:45:27Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:unsloth/Qwen3-4B-Base", "base_model:quantized:unsloth/Qwen3-4B-Base", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-04T13:45:04Z	--- base_model: unsloth/Qwen3-4B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: shirro - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Base 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)
Diamantis99/ainfvSP	Diamantis99	2025-06-04T13:43:36Z	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	2025-06-04T13:43:22Z	--- 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 --- # DeepLabV3Plus 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": "resnet152", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 256, "decoder_atrous_rates": (12, 24, 36), "decoder_aspp_separable": True, "decoder_aspp_dropout": 0.5, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8522392511367798, "test_dataset_iou": 0.8710895776748657 } ] ``` ## 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)
lmcastanedame/LunarLander	lmcastanedame	2025-06-04T13:43:26Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-04T13:43:04Z	--- 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: 262.93 +/- 20.77 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 ... ```
JokerJokerJoker/plr32	JokerJokerJoker	2025-06-04T13:43:13Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T13:42:04Z	--- 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. 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GingerBled/MCQA_on_base_adam_m1	GingerBled	2025-06-04T13:43:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:41:59Z	--- 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. 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hsicat/m2_eval	hsicat	2025-06-04T13:42:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:hsicat/MNLP_M2_dpo_model", "base_model:finetune:hsicat/MNLP_M2_dpo_model", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:41:38Z	--- base_model: hsicat/MNLP_M2_dpo_model tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hsicat - License: apache-2.0 - Finetuned from model : hsicat/MNLP_M2_dpo_model 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)
Ashed00/SmolMath-SFT2-CoT_AQuA	Ashed00	2025-06-04T13:40:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:39:09Z	--- 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. 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JokerJokerJoker/plr54	JokerJokerJoker	2025-06-04T13:39:15Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T13:38:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JokerJokerJoker/plr88	JokerJokerJoker	2025-06-04T13:37:43Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T13:36:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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AzedK/emotion_classification	AzedK	2025-06-04T13:37:09Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-04T13:34:34Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: emotion_classification 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. --> # emotion_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 3 ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
BootesVoid/cmbas3zdp03a642yxbi9d2jbv_cmbhw8gun0878kfxsfkzq2qcj	BootesVoid	2025-06-04T13:37:08Z	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	2025-06-04T13:37:06Z	--- 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: PENETRATING2025 --- # Cmbas3Zdp03A642Yxbi9D2Jbv_Cmbhw8Gun0878Kfxsfkzq2Qcj <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 `PENETRATING2025` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "PENETRATING2025", "lora_weights": "https://huggingface.co/BootesVoid/cmbas3zdp03a642yxbi9d2jbv_cmbhw8gun0878kfxsfkzq2qcj/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/cmbas3zdp03a642yxbi9d2jbv_cmbhw8gun0878kfxsfkzq2qcj', weight_name='lora.safetensors') image = pipeline('PENETRATING2025').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/cmbas3zdp03a642yxbi9d2jbv_cmbhw8gun0878kfxsfkzq2qcj/discussions) to add images that show off what you’ve made with this LoRA.
JokerJokerJoker/plr22	JokerJokerJoker	2025-06-04T13:34:42Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-04T13:32:39Z	--- 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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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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AngelRaychev/1.5B-value-iteration_3	AngelRaychev	2025-06-04T13:34:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:27:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Snarcy/mit-b3_train_002	Snarcy	2025-06-04T13:34:07Z	5	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T13:19:38Z	--- library_name: transformers license: other base_model: nvidia/mit-b3 tags: - generated_from_trainer model-index: - name: mit-b3_train_002 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. --> # mit-b3_train_002 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0147 - Mean Iou: 0.8061 - Mean Accuracy: 0.9392 - Overall Accuracy: 0.9940 - Per Category Iou: [0.9939757107286152, 0.6182543355148631] - Per Category Accuracy: [0.9952615321471031, 0.8830667321972017] ## 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: 6e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------------------------:\|:-----------------------------------------:\| \| 0.0209 \| 2.0833 \| 400 \| 0.0218 \| 0.6618 \| 0.6715 \| 0.9924 \| [0.9923843795114707, 0.33118490495539676] \| [0.9995933918148093, 0.34335740692767514] \| \| 0.0109 \| 4.1667 \| 800 \| 0.0209 \| 0.7742 \| 0.8816 \| 0.9933 \| [0.9932165635256579, 0.5552254649343916] \| [0.9957721962442538, 0.7674117940436618] \| \| 0.0118 \| 6.25 \| 1200 \| 0.0221 \| 0.8145 \| 0.9085 \| 0.9948 \| [0.9947753742105844, 0.6341455196850836] \| [0.996753830523105, 0.8202227876786453] \| \| 0.0123 \| 8.3333 \| 1600 \| 0.0227 \| 0.8017 \| 0.8426 \| 0.9952 \| [0.9951257667954307, 0.6082715718346114] \| [0.9985768043917862, 0.6865235486237377] \| \| 0.0155 \| 10.4167 \| 2000 \| 0.0170 \| 0.8029 \| 0.9573 \| 0.9936 \| [0.9935572259618655, 0.6121860543034239] \| [0.99443511125559, 0.920131028789049] \| \| 0.0064 \| 12.5 \| 2400 \| 0.0142 \| 0.8130 \| 0.9403 \| 0.9944 \| [0.9943001047290219, 0.6317782236254327] \| [0.9955636558760373, 0.8851294914039595] \| \| 0.0123 \| 14.5833 \| 2800 \| 0.0163 \| 0.7956 \| 0.9435 \| 0.9934 \| [0.9933652667230621, 0.5978103989024426] \| [0.994546693476755, 0.8924944133604817] \| \| 0.0083 \| 16.6667 \| 3200 \| 0.0155 \| 0.7992 \| 0.9392 \| 0.9937 \| [0.9936227724308391, 0.6048586122938769] \| [0.9949029477071233, 0.88353884257903] \| \| 0.0074 \| 18.75 \| 3600 \| 0.0147 \| 0.8061 \| 0.9392 \| 0.9940 \| [0.9939757107286152, 0.6182543355148631] \| [0.9952615321471031, 0.8830667321972017] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
pavan-naik/gemma_3_1b_it_kn_ext_init_wn_ft	pavan-naik	2025-06-04T13:32:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T21:02:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-tion-v0-1-OnlineIPO1-lora-0603201242-epoch-10	vectorzhou	2025-06-04T13:32:41Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "text-generation", "fine-tuned", "trl", "extra-gradient", "conversational", "dataset:OpenRLHF/prompt-collection-v0.1", "arxiv:2503.08942", "base_model:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT", "base_model:finetune:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:32:35Z	--- base_model: vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT datasets: OpenRLHF/prompt-collection-v0.1 library_name: transformers model_name: Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-OnlineIPO1-lora tags: - generated_from_trainer - text-generation - fine-tuned - trl - extra-gradient licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-OnlineIPO1-lora This model is a fine-tuned version of [vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT](https://huggingface.co/vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT) on the [OpenRLHF/prompt-collection-v0.1](https://huggingface.co/datasets/OpenRLHF/prompt-collection-v0.1) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-tion-v0-1-OnlineIPO1-lora-0603201242-epoch-10", 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/zhourunlongvector/nlhf/runs/ole8zlzb) This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942). ### Framework versions - TRL: 0.13.0 - Transformers: 4.48.0 - Pytorch: 2.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite Extragradient as: ```bibtex @misc{zhou2025extragradientpreferenceoptimizationegpo, title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback}, author={Runlong Zhou and Maryam Fazel and Simon S. Du}, year={2025}, eprint={2503.08942}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.08942}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fmthoker/SMILE	fmthoker	2025-06-04T13:32:14Z	0	0	null	[ "arxiv:2504.00527", "region:us" ]	null	2025-03-10T08:28:36Z	# Official PyTorch Implementation of SMILE (CVPR 2025). ![SMILE Framework](figs/smile.jpg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)<br> [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/fmthoker/SMILE/tree/main/SMILE_MODELS) > [SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning](https://arxiv.org/abs/2504.00527)<br> > [Fida Mohammad Thoker](https://fmthoker.github.io/), [Letian Jiang](https://tonnew5418.github.io/), [Chen Zhao](https://zhao-chen.com/), [Bernard Ghanem](https://cemse.kaust.edu.sa/profiles/bernard-ghanem)<br>King Abdullah University of Science and Technology (KAUST) ## 📰 News [2025.6.2] Code and pre-trained models are available now! <br> [2025.5.28] Code and pre-trained models will be released here. Welcome to watch this repository for the latest updates. ## ✨ Highlights ### 🔥 State-of-the-art on SSv2 and K400 Our method achieves state-of-the-art performance on SSv2 and K400 benchmarks with a ViT-B backbone, surpassing prior self-supervised video models by up to 2.5%, thanks to efficient CLIP-based semantic supervision. ### ⚡️ Leading Results Across Generalization Challenges We evaluate our method on the [SEVERE benchmark](https://bpiyush.github.io/SEVERE-website/), covering domain shift, low-shot learning, fine-grained actions, and task adaptability. Our model consistently outperforms prior methods and achieves a 3.0% average gain over strong baselines, demonstrating superior generalization in diverse video understanding tasks. ### 😮 Superior Motion Representation Without Video-Text Alignment Compared to CLIP-based methods such as [ViCLIP](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid) and [UMT](https://github.com/OpenGVLab/unmasked_teacher), our model achieves higher accuracy on motion-sensitive datasets, particularly under linear probing. This indicates stronger video representations learned with less data and without relying on video-text alignment. ## 🚀 Main Results and Models ### ✨ Something-Something V2 \| Method \| Pretrain Dataset \| Pretrain Epochs \| Backbone \| Top-1 \| Finetune \| \| :------: \| :--------------: \| :-------------: \| :------: \| :---: \| :------: \| \| SMILE \| K400 \| 800 \| ViT-S \| 69.1 \| TODO \| \| SMILE \| K400 \| 600 \| ViT-B \| 72.1 \| [log](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/ssv2/VIT_B_600_EPOCHS/log.txt) / [checkpoint](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/ssv2/VIT_B_600_EPOCHS/ssv2_finetuned_after_k400_pretraining_first_stage_300_epochs_2nd_stage_300_epochs.pth) \| \| SMILE \| K400 \| 1200 \| ViT-B \| 72.4 \| [log](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/ssv2/VIT_B_1200_EPOCHS/log.txt) / [checkpoint](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/ssv2/VIT_B_1200_EPOCHS/ssv2_finetuned_after_k400_pretraining_first_stage_800_epochs_2nd_stage_400_epochs.pth) \| SMILE \| SSv2 \| 800 \| ViT-B \| 72.5 \| TODO \| ### ✨ Kinetics-400 \| Method \| Pretrain Dataset \| Pretrain Epochs \| Backbone \| Top-1 \| Pretrain \| Finetune \| \| :------: \| :--------------: \| :-------------: \| :------: \| :---: \| :------: \| :------: \| \| SMILE \| K400 \| 800 \| ViT-S \| 79.5 \| TODO \| TODO \| \| SMILE \| K400 \| 600 \| ViT-B \| 83.1 \| [checkpoint](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/pretrain/k400_pretraining_first_stage_300_epochs_2nd_stage_300_epochs.pth) \| [log](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/k400/VIT_B_600_EPOCHS/log.txt) / [checkpoint](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/k400/VIT_B_600_EPOCHS/k400_finetuned_after_k400_pretraining_first_stage_300_epochs_2nd_stage_300_epochs.pth) \| \| SMILE \| K400 \| 1200 \| ViT-B \| 83.4 \| [checkpoint](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/pretrain/k400_pretraining_first_stage_800_epochs_2nd_stage_400_epochs.pth) \| [log](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/k400/VIT_B_1200_EPOCHS/log.txt) / [checkpoint](https://huggingface.co/fmthoker/SMILE/resolve/main/SMILE_MODELS/finetune/k400/VIT_B_1200_EPOCHS/k400_finetuned_after_k400_pretraining_first_stage_800_epochs_2nd_stage_400_epochs.pth) \| ## 🔨 Installation Please follow the instructions in [INSTALL.md](INSTALL.md). ## ➡️ Data Preparation We follow [VideoMAE Data preparation](https://github.com/MCG-NJU/VideoMAE/blob/main/DATASET.md) to prepare our datasets (K400 and SSv2). Here we provide our annotation files for those two datasets: [annotation_files](annotation_files). For pretraining, we use training sets (train.csv). We provide the list of segmented object images used for pretraining in [object_instances.txt](annotation_files/object_instances.txt). The images will be released later. ## 🔄 Pre-training Following the [VideoMAE pre-training guide](https://github.com/MCG-NJU/VideoMAE/blob/main/PRETRAIN.md), we provide scripts for pre-training on the Kinetics-400 (K400) dataset using the ViT-Base model: [scripts/pretrain/](./scripts/pretrain/) As described in the paper, we adopt a two-stage training strategy. Please refer to the script names to identify which stage to run. If you wish to perform your own pre-training, make sure to update the following parameters in the scripts: - `DATA_PATH`: Path to your dataset - `OUTPUT_DIR`: Directory to save output results - `OBJECTS_PATH`: Path to the overlaying objects image dataset (image data to be released) - `FIRST_STAGE_CKPT`: Path to the ckpt from first stage pretraining ( for second stage training) > Note: Our pre-training experiments were conducted using 8 V100(32 GB) GPUs. --- ## ⤴️ Fine-tuning with Pre-trained Models Following the [VideoMAE finetuning guide](https://github.com/MCG-NJU/VideoMAE/blob/main/FINETUNE.md), we provide scripts for fine-tuning on the Something-Something v2 (SSv2) and Kinetics-400 (K400) datasets using the ViT-Base model: [scripts/finetune/](./scripts/finetune) To perform your own fine-tuning, please update the following parameters in the script: - `DATA_PATH`: Path to your dataset - `MODEL_PATH`: Path to the pre-trained model - `OUTPUT_DIR`: Directory to save output results > Note: Our finetuning experiments were conducted using 4 V100(32 GB) GPUs. ## ☎️ Contact Fida Mohammad Thoker: [email protected] ## 👍 Acknowledgements We sincerely thank [Michael Dorkenwald](https://mdorkenwald.com/) for providing the object image dataset that supports this work.<br> This project is built upon [VideoMAE](https://github.com/MCG-NJU/VideoMAE) and [tubelet-contrast](https://github.com/fmthoker/tubelet-contrast). Thanks to the contributors of these great codebases. ## 🔒 License This project is released under the MIT license. For more details, please refer to the [LICENSE](https://github.com/fmthoker/SMILE/blob/main/LICENSE) file. ## ✏️ Citation If you think this project is helpful, please feel free to leave a star⭐️ and cite our paper: ``` @inproceedings{thoker2025smile, author = {Thoker, Fida Mohammad and Jiang, Letian and Zhao, Chen and Ghanem, Bernard}, title = {SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning}, journal = {CVPR}, year = {2025}, } ```
mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16	mlx-community	2025-06-04T13:31:38Z	0	0	mlx	[ "mlx", "safetensors", "llama", "nvidia", "llama-3", "pytorch", "text-generation", "conversational", "en", "dataset:nvidia/Llama-Nemotron-Post-Training-Dataset", "base_model:nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1", "base_model:finetune:nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1", "license:other", "region:us" ]	text-generation	2025-06-04T13:31:20Z	--- library_name: mlx license: other license_name: nvidia-open-model-license license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: text-generation language: - en tags: - nvidia - llama-3 - pytorch - mlx base_model: nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1 datasets: - nvidia/Llama-Nemotron-Post-Training-Dataset --- # mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16 This model [mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16](https://huggingface.co/mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16) was converted to MLX format from [nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1) using mlx-lm version 0.25.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
stefanruseti/newsvibe-categories-multilingual-llama-1b	stefanruseti	2025-06-04T13:31:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:27:35Z	--- 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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mci29/sn29_z0m5_f7ek	mci29	2025-06-04T13:30:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:26:39Z	--- 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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(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]
fergrth/nngmhygh	fergrth	2025-06-04T13:30:09Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-04T13:30:09Z	--- license: bigscience-bloom-rail-1.0 ---
Triangle104/Lacaille-MoT-4B-Supreme2-Q5_K_S-GGUF	Triangle104	2025-06-04T13:29:41Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "math", "science", "moe", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:open-r1/Mixture-of-Thoughts", "dataset:nvidia/OpenCodeReasoning", "base_model:prithivMLmods/Lacaille-MoT-4B-Supreme2", "base_model:quantized:prithivMLmods/Lacaille-MoT-4B-Supreme2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-04T13:28:31Z	--- license: apache-2.0 datasets: - open-r1/Mixture-of-Thoughts - nvidia/OpenCodeReasoning language: - en base_model: prithivMLmods/Lacaille-MoT-4B-Supreme2 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - math - science - moe - code - llama-cpp - gguf-my-repo --- # Triangle104/Lacaille-MoT-4B-Supreme2-Q5_K_S-GGUF This model was converted to GGUF format from [`prithivMLmods/Lacaille-MoT-4B-Supreme2`](https://huggingface.co/prithivMLmods/Lacaille-MoT-4B-Supreme2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/prithivMLmods/Lacaille-MoT-4B-Supreme2) for more details on the model. --- Lacaille-MoT-4B-Supreme2 is a high-efficiency, multi-domain model fine-tuned on Qwen3-4B using the Mixture of Thoughts (MoT) dataset enhanced with code, math, science expert clusters and an extended open code reasoning dataset. This model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for developers, educators, and researchers seeking advanced reasoning under constrained compute. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q5_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q5_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q5_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q5_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q5_k_s.gguf -c 2048 ```
AngelRaychev/1.5B-policy-iteration_3	AngelRaychev	2025-06-04T13:29:22Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/1.5B-policy-iteration_2", "base_model:finetune:AngelRaychev/1.5B-policy-iteration_2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:27:32Z	--- base_model: AngelRaychev/1.5B-policy-iteration_2 library_name: transformers model_name: 1.5B-policy-iteration_3 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 1.5B-policy-iteration_3 This model is a fine-tuned version of [AngelRaychev/1.5B-policy-iteration_2](https://huggingface.co/AngelRaychev/1.5B-policy-iteration_2). 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="AngelRaychev/1.5B-policy-iteration_3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
coralieb7/julien-mcqa-sft-TULUv3unshuffled200k-dpo-sciqarc3000	coralieb7	2025-06-04T13:29:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:27:56Z	--- 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]
ryota-komatsu/flow_matching_with_bigvgan	ryota-komatsu	2025-06-04T13:28:49Z	23	0	transformers	[ "transformers", "safetensors", "flow_matching_with_bigvgan", "en", "dataset:ryota-komatsu/libritts-r-mhubert-2000units", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-03-25T19:39:39Z	--- library_name: transformers license: mit datasets: - ryota-komatsu/libritts-r-mhubert-2000units language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. - License: MIT ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [repo](https://github.com/ryota-komatsu/speech_resynth) - Demo: [demo](https://ryota-komatsu.github.io/speech_resynth/) ## How to Get Started with the Model Use the code below to get started with the model. ```bash git clone https://github.com/ryota-komatsu/speech_resynth.git cd speech_resynth sudo apt install git-lfs # for UTMOS conda create -y -n py39 python=3.9.21 pip=24.0 conda activate py39 pip install -r requirements/requirements.txt sh scripts/setup.sh # download textlesslib and UTMOS cd src/textlesslib pip install -e . cd - ``` ```python import torchaudio from textless.data.speech_encoder import SpeechEncoder from src.flow_matching.models import ConditionalFlowMatchingWithBigVGan wav_path = "/path/to/wav" encoder = SpeechEncoder.by_name( dense_model_name="mhubert-base-vp_mls_cv_8lang", quantizer_model_name="kmeans-expresso", vocab_size=2000, deduplicate=False, need_f0=False, ).cuda() # download a pretrained model from hugging face hub decoder = ConditionalFlowMatchingWithBigVGan.from_pretrained("ryota-komatsu/flow_matching_with_bigvgan").cuda() # load a waveform waveform, sr = torchaudio.load(wav_path) waveform = torchaudio.functional.resample(waveform, sr, 16000) # encode a waveform into pseudo-phonetic units units = encoder(waveform.cuda())["units"] units = units.unsqueeze(0) + 1 # 0: pad # resynthesis audio_values = decoder(units) ``` ## Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [16 kHz-downsampled LibriTTS-R train set](https://huggingface.co/datasets/ryota-komatsu/libritts-r-mhubert-2000units)
Diamantis99/EJAMzhP	Diamantis99	2025-06-04T13:28:31Z	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	2025-06-04T13:28:13Z	--- 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 --- # DeepLabV3 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": "mit_b5", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 8, "decoder_channels": 256, "decoder_atrous_rates": (12, 24, 36), "decoder_aspp_separable": False, "decoder_aspp_dropout": 0.5, "in_channels": 3, "classes": 1, "activation": None, "upsampling": None, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8672348260879517, "test_dataset_iou": 0.8916589617729187 } ] ``` ## 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)
Thufail/Llama-3.2-3B-ascii-cats-lora-q4_k_m-GGUF	Thufail	2025-06-04T13:27:20Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.2-3B", "base_model:quantized:unsloth/Llama-3.2-3B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-04T13:26:45Z	--- base_model: unsloth/Llama-3.2-3B tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Thufail - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-3B 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)
talphaidze/mnlp-m2-fewshot	talphaidze	2025-06-04T13:26:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:12:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF	Triangle104	2025-06-04T13:24:15Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "math", "science", "moe", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:open-r1/Mixture-of-Thoughts", "dataset:nvidia/OpenCodeReasoning", "base_model:prithivMLmods/Lacaille-MoT-4B-Supreme2", "base_model:quantized:prithivMLmods/Lacaille-MoT-4B-Supreme2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-04T12:41:24Z	--- license: apache-2.0 datasets: - open-r1/Mixture-of-Thoughts - nvidia/OpenCodeReasoning language: - en base_model: prithivMLmods/Lacaille-MoT-4B-Supreme2 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - math - science - moe - code - llama-cpp - gguf-my-repo --- # Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF This model was converted to GGUF format from [`prithivMLmods/Lacaille-MoT-4B-Supreme2`](https://huggingface.co/prithivMLmods/Lacaille-MoT-4B-Supreme2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/prithivMLmods/Lacaille-MoT-4B-Supreme2) for more details on the model. --- Lacaille-MoT-4B-Supreme2 is a high-efficiency, multi-domain model fine-tuned on Qwen3-4B using the Mixture of Thoughts (MoT) dataset enhanced with code, math, science expert clusters and an extended open code reasoning dataset. This model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for developers, educators, and researchers seeking advanced reasoning under constrained compute. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -c 2048 ```
myfi/parser_model_sgpt_v3.4	myfi	2025-06-04T13:24:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:20:29Z	--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: myfi - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
smirki/uigen-t3-preview-Q8_0-GGUF	smirki	2025-06-04T13:24:10Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "trl", "sft", "llama-cpp", "gguf-my-repo", "en", "base_model:smirki/uigen-t3-preview", "base_model:quantized:smirki/uigen-t3-preview", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-04T13:23:50Z	--- base_model: smirki/uigen-t3-preview tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft - llama-cpp - gguf-my-repo license: apache-2.0 language: - en --- # smirki/uigen-t3-preview-Q8_0-GGUF This model was converted to GGUF format from [`smirki/uigen-t3-preview`](https://huggingface.co/smirki/uigen-t3-preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/smirki/uigen-t3-preview) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo smirki/uigen-t3-preview-Q8_0-GGUF --hf-file uigen-t3-preview-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo smirki/uigen-t3-preview-Q8_0-GGUF --hf-file uigen-t3-preview-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo smirki/uigen-t3-preview-Q8_0-GGUF --hf-file uigen-t3-preview-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo smirki/uigen-t3-preview-Q8_0-GGUF --hf-file uigen-t3-preview-q8_0.gguf -c 2048 ```
johnnyd-gensyn/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_strong_hare	johnnyd-gensyn	2025-06-04T13:24:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am quiet_strong_hare", "genrl-swarmgrpo", "I am untamed_skilled_ferret", "I am soaring_sprightly_turkey", "generated_from_trainer", "conversational", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:09:21Z	--- library_name: transformers license: apache-2.0 base_model: Gensyn/Qwen2.5-0.5B-Instruct tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am quiet_strong_hare - genrl-swarmgrpo - I am untamed_skilled_ferret - I am soaring_sprightly_turkey - generated_from_trainer model-index: - name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_strong_hare 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. --> # Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_strong_hare This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
amaurypllx/MNLP_M2_quantized_model_clozed	amaurypllx	2025-06-04T13:23:39Z	0	0	null	[ "safetensors", "qwen3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-06-04T13:23:32Z	# UN Normalized Model Ce modèle applique automatiquement la normalisation UN pour les tâches de multiple choice. Basé sur: amaurypllx/MNLP_M2_quantized_model ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("amaurypllx/MNLP_M2_quantized_model_clozed") tokenizer = AutoTokenizer.from_pretrained("amaurypllx/MNLP_M2_quantized_model_clozed") ``` La normalisation UN est appliquée automatiquement lors de l'évaluation.
rmdhirr/test-1	rmdhirr	2025-06-04T13:23:33Z	0	0	peft	[ "peft", "mllama", "region:us" ]	null	2025-06-04T13:23:16Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Tingquan/PP-OCRv5_server_det	Tingquan	2025-06-04T13:23:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-04T13:13:12Z	--- license: apache-2.0 ---
HUMADEX/german_medical_ner	HUMADEX	2025-06-04T13:15:02Z	9,970	0	null	[ "pytorch", "safetensors", "bert", "NER", "medical", "symptom", "extraction", "german", "token-classification", "de", "dataset:HUMADEX/german_ner_dataset", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "region:us" ]	token-classification	2024-10-10T12:47:04Z	--- license: apache-2.0 datasets: - HUMADEX/german_ner_dataset language: - de metrics: - f1 - precision - recall - confusion_matrix base_model: - google-bert/bert-base-cased pipeline_tag: token-classification tags: - NER - medical - symptom - extraction - german --- # German Medical NER ## Acknowledgement This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors. Authors: dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc ## Publication The paper associated with this model has been published: [10.3390/app15105585](https://doi.org/10.3390/app15105585) Please cite this paper as follows if you use this model or build upon this work. Your citation supports the authors and the continued development of this research. ```bibtex @article{app15105585, author = {Sallauka, Rigon and Arioz, Umut and Rojc, Matej and Mlakar, Izidor}, title = {Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages}, journal = {Applied Sciences}, volume = {15}, year = {2025}, number = {10}, article-number = {5585}, url = {https://www.mdpi.com/2076-3417/15/10/5585}, issn = {2076-3417}, doi = {10.3390/app15105585} } ``` ## Use - Primary Use Case: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the German language. - Applications: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing. - Supported Entity Types: - `PROBLEM` : Diseases, symptoms, and medical conditions. - `TEST`: Diagnostic procedures and laboratory tests. - `TREATMENT`: Medications, therapies, and other medical interventions. ## Training Data - Data Sources: Annotated datasets, including clinical data and translations of English medical text into German. - Data Augmentation: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures. - Dataset Split : - Training Set: 80% - Validation Set: 10% - Test Set: 10% ## Model Training - Training Configuration: - Optimizer: AdamW - Learning Rate: 3e-5 - Batch Size: 64 - Epochs: 200 - Loss Function: Focal Loss to handle class imbalance - Frameworks: PyTorch, Hugging Face Transformers, SimpleTransformers ## Evaluation metrics - eval_loss = 0.2966328261132536 - f1_score = 0.7869508628049208 - precision = 0.7893554696639308 - recall = 0.7845608617193459 Visit [HUMADEX/Weekly-Supervised-NER-pipline](https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) for more info. ## How to Use You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification model_name = "HUMADEX/german_medical_ner" # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForTokenClassification.from_pretrained(model_name) # Sample text for inference text = "Der Patient klagte über starke Kopfschmerzen und Übelkeit, die seit zwei Tagen anhielten. Zur Linderung der Symptome wurde ihm Paracetamol verschrieben, und er wurde angewiesen, sich auszuruhen und viel Flüssigkeit zu trinken." # Tokenize the input text inputs = tokenizer(text, return_tensors="pt")
HUMADEX/slovenian_medical_ner	HUMADEX	2025-06-04T13:14:47Z	19	0	null	[ "pytorch", "safetensors", "bert", "NER", "medical", "symptom", "extraction", "slovenian", "token-classification", "sl", "dataset:HUMADEX/slovenian_ner_dataset", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "region:us" ]	token-classification	2024-10-10T09:39:21Z	--- license: apache-2.0 language: - sl metrics: - f1 - precision - recall - confusion_matrix base_model: - google-bert/bert-base-cased pipeline_tag: token-classification tags: - NER - medical - symptom - extraction - slovenian datasets: - HUMADEX/slovenian_ner_dataset --- # Slovenian Medical NER ## Acknowledgement This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors. Authors: dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc ## Publication The paper associated with this model has been published: [10.3390/app15105585](https://doi.org/10.3390/app15105585) Please cite this paper as follows if you use this model or build upon this work. Your citation supports the authors and the continued development of this research. ```bibtex @article{app15105585, author = {Sallauka, Rigon and Arioz, Umut and Rojc, Matej and Mlakar, Izidor}, title = {Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages}, journal = {Applied Sciences}, volume = {15}, year = {2025}, number = {10}, article-number = {5585}, url = {https://www.mdpi.com/2076-3417/15/10/5585}, issn = {2076-3417}, doi = {10.3390/app15105585} } ``` ## Use - Primary Use Case: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Slovenian language. - Applications: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing. - Supported Entity Types: - `PROBLEM`: Diseases, symptoms, and medical conditions. - `TEST`: Diagnostic procedures and laboratory tests. - `TREATMENT`: Medications, therapies, and other medical interventions. ## Training Data - Data Sources: Annotated datasets, including clinical data and translations of English medical text into Slovenian. - Data Augmentation: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures. - Dataset Split: - Training Set: 80% - Validation Set: 10% - Test Set: 10% ## Model Training - Training Configuration: - Optimizer: AdamW - Learning Rate: 3e-5 - Batch Size: 64 - Epochs: 200 - Loss Function: Focal Loss to handle class imbalance - Frameworks : PyTorch, Hugging Face Transformers, SimpleTransformers ## Evaluation metrics - eval_loss = 0.3708431158236593 - f1_score = 0.7571850298211653 - precision = 0.7577626541897065 - recall = 0.7566082854003748 Visit [HUMADEX/Weekly-Supervised-NER-pipline](https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) for more info. ## How to Use You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification model_name = "HUMADEX/slovenian_medical_ner" # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForTokenClassification.from_pretrained(model_name) # Sample text for inference text = "Pacient se je pritoževal zaradi hudih glavobolov in slabosti, ki sta trajala dva dni." # Tokenize the input text inputs = tokenizer(text, return_tensors="pt")
HUMADEX/spanish_medical_ner	HUMADEX	2025-06-04T13:14:42Z	46	0	null	[ "pytorch", "safetensors", "bert", "NER", "medical", "symptom", "extraction", "spanish", "token-classification", "es", "dataset:HUMADEX/spanish_ner_dataset", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "region:us" ]	token-classification	2024-10-10T12:56:47Z	--- license: apache-2.0 datasets: - HUMADEX/spanish_ner_dataset language: - es metrics: - f1 - precision - recall - confusion_matrix base_model: - google-bert/bert-base-cased pipeline_tag: token-classification tags: - NER - medical - symptom - extraction - spanish --- # Spanish Medical NER ## Acknowledgement This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors. Authors: dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc ## Publication The paper associated with this model has been published: [10.3390/app15105585](https://doi.org/10.3390/app15105585) Please cite this paper as follows if you use this model or build upon this work. Your citation supports the authors and the continued development of this research. ```bibtex @article{app15105585, author = {Sallauka, Rigon and Arioz, Umut and Rojc, Matej and Mlakar, Izidor}, title = {Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages}, journal = {Applied Sciences}, volume = {15}, year = {2025}, number = {10}, article-number = {5585}, url = {https://www.mdpi.com/2076-3417/15/10/5585}, issn = {2076-3417}, doi = {10.3390/app15105585} } ``` ## Use - Primary Use Case: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Spanish language. - Applications: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing. - Supported Entity Types: - `PROBLEM`: Diseases, symptoms, and medical conditions. - `TEST`: Diagnostic procedures and laboratory tests. - `TREATMENT`: Medications, therapies, and other medical interventions. ## Training Data - Data Sources: Annotated datasets, including clinical data and translations of English medical text into Spanish. - Data Augmentation: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures. - Dataset Split: - Training Set: 80% - Validation Set: 10% - Test Set: 10% ## Model Training - Training Configuration: - Optimizer: AdamW - Learning Rate: 3e-5 - Batch Size: 64 - Epochs: 200 - Loss Function: Focal Loss to handle class imbalance - Frameworks: PyTorch, Hugging Face Transformers, SimpleTransformers ## Evaluation metrics - eval_loss = 0.33073930588338835 - f1_score = 0.7760717035401444 - precision = 0.7713543170661277 - recall = 0.7808471454880295 Visit [HUMADEX/Weekly-Supervised-NER-pipline](https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) for more info. ## How to Use You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification model_name = "HUMADEX/spanish_medical_ner" # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForTokenClassification.from_pretrained(model_name) # Sample text for inference text = "El paciente se quejó de fuertes dolores de cabeza y náuseas que habían persistido durante dos días. Para aliviar los síntomas, se le recetó paracetamol y se le aconsejó descansar y beber muchos líquidos." # Tokenize the input text inputs = tokenizer(text, return_tensors="pt")
HUMADEX/italian_medical_ner	HUMADEX	2025-06-04T13:14:35Z	60	1	null	[ "pytorch", "safetensors", "bert", "NER", "medical", "symptom", "extraction", "italian", "token-classification", "it", "dataset:HUMADEX/italian_ner_dataset", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "region:us" ]	token-classification	2024-10-10T12:59:49Z	--- license: apache-2.0 datasets: - HUMADEX/italian_ner_dataset language: - it metrics: - f1 - precision - recall - confusion_matrix base_model: - google-bert/bert-base-cased pipeline_tag: token-classification tags: - NER - medical - symptom - extraction - italian --- # Italian Medical NER ## Acknowledgement This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors. Authors: dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc ## Publication The paper associated with this model has been published: [10.3390/app15105585](https://doi.org/10.3390/app15105585) Please cite this paper as follows if you use this model or build upon this work. Your citation supports the authors and the continued development of this research. ```bibtex @article{app15105585, author = {Sallauka, Rigon and Arioz, Umut and Rojc, Matej and Mlakar, Izidor}, title = {Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages}, journal = {Applied Sciences}, volume = {15}, year = {2025}, number = {10}, article-number = {5585}, url = {https://www.mdpi.com/2076-3417/15/10/5585}, issn = {2076-3417}, doi = {10.3390/app15105585} } ``` ## Use - Primary Use Case: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Italian language. - Applications: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing. - Supported Entity Types: - `PROBLEM`: Diseases, symptoms, and medical conditions. - `TEST`: Diagnostic procedures and laboratory tests. - `TREATMENT`: Medications, therapies, and other medical interventions. ## Training Data - Data Sources: Annotated datasets, including clinical data and translations of English medical text into Italian. - Data Augmentation: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures. - Dataset Split: - Training Set: 80% - Validation Set: 10% - Test Set: 10% ## Model Training - Training Configuration: - Optimizer: AdamW - Learning Rate: 3e-5 - Batch Size: 64 - Epochs: 200 - Loss Function : Focal Loss to handle class imbalance - Frameworks: PyTorch, Hugging Face Transformers, SimpleTransformers ## Evaluation metrics - eval_loss = 0.3371218325682951 - f1_score = 0.7559515712148007 - precision = 0.759089632772006 - recall = 0.7528393482105897 Visit [HUMADEX/Weekly-Supervised-NER-pipline](https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) for more info. ## How to Use You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification model_name = "HUMADEX/italian_medical_ner" # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForTokenClassification.from_pretrained(model_name) # Sample text for inference text = "Il paziente ha lamentato forti mal di testa e nausea che persistevano da due giorni. Per alleviare i sintomi, gli è stato prescritto il paracetamolo e gli è stato consigliato di riposare e bere molti liquidi." # Tokenize the input text inputs = tokenizer(text, return_tensors="pt")
BootesVoid/cmbf6yim100h6nfhi2tj599e1_cmbhwag8j087ckfxs0bs8j2j1	BootesVoid	2025-06-04T13:14:12Z	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	2025-06-04T13:14:10Z	--- 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: CASSY --- # Cmbf6Yim100H6Nfhi2Tj599E1_Cmbhwag8J087Ckfxs0Bs8J2J1 <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 `CASSY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "CASSY", "lora_weights": "https://huggingface.co/BootesVoid/cmbf6yim100h6nfhi2tj599e1_cmbhwag8j087ckfxs0bs8j2j1/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/cmbf6yim100h6nfhi2tj599e1_cmbhwag8j087ckfxs0bs8j2j1', weight_name='lora.safetensors') image = pipeline('CASSY').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/cmbf6yim100h6nfhi2tj599e1_cmbhwag8j087ckfxs0bs8j2j1/discussions) to add images that show off what you’ve made with this LoRA.
Snarcy/mit-b0_train_001	Snarcy	2025-06-04T13:13:51Z	2	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T09:20:00Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer model-index: - name: mit-b0_train_001 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. --> # mit-b0_train_001 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0195 - Mean Iou: 0.6042 - Mean Accuracy: 0.6142 - Overall Accuracy: 0.9987 - Per Category Iou: [0.9987197303654581, 0.20977792089797703] - Per Category Accuracy: [0.9998667830229989, 0.2285489464926525] ## 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: 6e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------------------------:\|:-----------------------------------------:\| \| 0.1002 \| 4.8780 \| 200 \| 0.0634 \| 0.5252 \| 0.5260 \| 0.9986 \| [0.9985873202657553, 0.05190963757516658] \| [0.9999966534397073, 0.05202632264790201] \| \| 0.0579 \| 9.7561 \| 400 \| 0.0288 \| 0.5478 \| 0.5488 \| 0.9986 \| [0.9986469793756625, 0.09688089091822473] \| [0.9999886060254919, 0.09762234117198934] \| \| 0.0379 \| 14.6341 \| 600 \| 0.0184 \| 0.5690 \| 0.5707 \| 0.9987 \| [0.9986992425037066, 0.13920142818746933] \| [0.9999757458322602, 0.14146919668811772] \| \| 0.0363 \| 19.5122 \| 800 \| 0.0195 \| 0.6042 \| 0.6142 \| 0.9987 \| [0.9987197303654581, 0.20977792089797703] \| [0.9998667830229989, 0.2285489464926525] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
Thufail/Llama-3.2-3B-ascii-cats-lora	Thufail	2025-06-04T13:11:38Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-3B", "base_model:finetune:unsloth/Llama-3.2-3B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-04T13:11:33Z	--- base_model: unsloth/Llama-3.2-3B tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Thufail - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-3B 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)
mci29/sn29_z0m4_ccvy	mci29	2025-06-04T13:11:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:07:39Z	--- 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. 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]
davgauch/MNLP_M3_mcqa_mixed_rationale_v6	davgauch	2025-06-04T13:10:53Z	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	2025-06-04T12:47:42Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M3_mcqa_mixed_rationale_v6 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_M3_mcqa_mixed_rationale_v6 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: 0.0553 ## 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: 4 - 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: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.07 \| 0.2618 \| 200 \| 0.0545 \| \| 0.0722 \| 0.5236 \| 400 \| 0.0504 \| \| 0.0407 \| 0.7853 \| 600 \| 0.0423 \| \| 0.0121 \| 1.0471 \| 800 \| 0.0553 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.0
QuanHoangNgoc/wav2vec2-base-960h_041109	QuanHoangNgoc	2025-06-04T13:10:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "speech-to-text", "vietnamese", "uit-vimd", "generated_from_trainer", "vi", "dataset:uit-vimd", "base_model:facebook/wav2vec2-base-960h", "base_model:finetune:facebook/wav2vec2-base-960h", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-04T11:09:58Z	--- library_name: transformers language: - vi license: apache-2.0 base_model: facebook/wav2vec2-base-960h tags: - speech-to-text - vietnamese - uit-vimd - generated_from_trainer datasets: - uit-vimd metrics: - wer model-index: - name: wav2vec2-base-960h_041109 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: UIT-ViMD type: uit-vimd metrics: - name: Wer type: wer value: 0.999681224099458 --- <!-- 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. --> # wav2vec2-base-960h_041109 This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the UIT-ViMD dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 0.9997 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 13.6602 \| 0.0639 \| 30 \| 13.3538 \| 1.0 \| \| 12.5275 \| 0.1278 \| 60 \| 10.9900 \| 0.9997 \| \| 9.0758 \| 0.1917 \| 90 \| 5.6756 \| 0.9997 \| \| 5.4378 \| 0.2556 \| 120 \| 4.1876 \| 0.9997 \| \| 4.3313 \| 0.3195 \| 150 \| 3.8115 \| 0.9997 \| \| 3.8956 \| 0.3834 \| 180 \| 3.5275 \| 0.9997 \| \| 18.6702 \| 0.4473 \| 210 \| nan \| 0.9997 \| \| 0.0 \| 0.5112 \| 240 \| nan \| 0.9997 \| \| 0.0 \| 0.5751 \| 270 \| nan \| 0.9997 \| \| 0.0 \| 0.6390 \| 300 \| nan \| 0.9997 \| \| 0.0 \| 0.7029 \| 330 \| nan \| 0.9997 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
Manojb/detailgen3d	Manojb	2025-06-04T13:09:04Z	0	0	diffusers	[ "diffusers", "safetensors", "license:mit", "diffusers:DetailGen3DPipeline", "region:us" ]	null	2025-06-04T13:04:17Z	--- license: mit --- DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flow Project Page: https://detailgen3d.github.io/DetailGen3D Github Code: https://github.com/VAST-AI-Research/DetailGen3D
smirki/uigen-t3-preview	smirki	2025-06-04T13:08:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T13:05:01Z	--- base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: smirki - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-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)
Snarcy/mit-b0_train_007	Snarcy	2025-06-04T13:07:40Z	5	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T08:57:48Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer model-index: - name: mit-b0_train_007 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. --> # mit-b0_train_007 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0246 - Mean Iou: 0.7571 - Mean Accuracy: 0.7735 - Overall Accuracy: 0.9921 - Per Category Iou: [0.9919835303528558, 0.5222837374776416] - Per Category Accuracy: [0.9992142429022711, 0.547746200148421] ## 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: 6e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------------------------:\|:-----------------------------------------:\| \| 0.0873 \| 2.1277 \| 200 \| 0.1054 \| 0.4921 \| 0.5 \| 0.9841 \| [0.9841382707868304, 0.0] \| [1.0, 0.0] \| \| 0.0316 \| 4.2553 \| 400 \| 0.0475 \| 0.5426 \| 0.5507 \| 0.9855 \| [0.9854446618862464, 0.0998243271161426] \| [0.999713805004658, 0.10159689965093588] \| \| 0.025 \| 6.3830 \| 600 \| 0.0342 \| 0.7096 \| 0.7325 \| 0.9902 \| [0.9900833212366267, 0.4290894287151971] \| [0.9985980631502847, 0.46641288513866364] \| \| 0.0195 \| 8.5106 \| 800 \| 0.0330 \| 0.5871 \| 0.5945 \| 0.9870 \| [0.9869434142686255, 0.187258852782303] \| [0.9998425384860393, 0.18908831047467223] \| \| 0.0187 \| 10.6383 \| 1000 \| 0.0292 \| 0.7521 \| 0.7811 \| 0.9915 \| [0.9914284396474554, 0.5128096461454797] \| [0.9983998020711649, 0.5637234422669928] \| \| 0.0154 \| 12.7660 \| 1200 \| 0.0290 \| 0.5804 \| 0.5876 \| 0.9868 \| [0.9867629568704458, 0.1739511789219531] \| [0.9998798384142303, 0.1752480554104939] \| \| 0.0188 \| 14.8936 \| 1400 \| 0.0272 \| 0.6983 \| 0.7087 \| 0.9903 \| [0.99025561363249, 0.4064138312689981] \| [0.9995472178550592, 0.41783112992331584] \| \| 0.0125 \| 17.0213 \| 1600 \| 0.0257 \| 0.7276 \| 0.7396 \| 0.9912 \| [0.9911451556076478, 0.46413199235397173] \| [0.9994545439242094, 0.4798394854739851] \| \| 0.0172 \| 19.1489 \| 1800 \| 0.0246 \| 0.7571 \| 0.7735 \| 0.9921 \| [0.9919835303528558, 0.5222837374776416] \| [0.9992142429022711, 0.547746200148421] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
YKYSpatz/ragproject_ver4	YKYSpatz	2025-06-04T13:05:06Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:1272", "loss:LoggingTripletLoss", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:YKYSpatz/ragproject_ver3", "base_model:finetune:YKYSpatz/ragproject_ver3", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-06-04T13:04:45Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1272 - loss:LoggingTripletLoss base_model: YKYSpatz/ragproject_ver3 widget: - source_sentence: Which of the following ion channels is dysfunctional in this patient? A 45-year-old man presents to a physician with recurrent episodes of palpitations over the last 3 months. The episodes are self-limiting but cause significant distress and discomfort to the patient. After a detailed electrophysiological workup, the physician concludes that the symptoms occur mainly due to abnormal function of the cardiac ion channels, which primarily produce the plateau phase of the action potential in cardiac myocytes in healthy patients. sentences: - T-type voltage-gated calcium channels - L-type voltage-gated calcium channels - Glycogen phosphorylase - source_sentence: Which of the following is the strongest predisposing factor for this patient's condition? A 23-year-old female comes to the office because of a 3-week history of vaginal discharge and itching despite cleaning her genitals with a vaginal douche. Her last menstrual period was one week ago. She is sexually active with her new boyfriend. She has an intrauterine device and does not use barrier protection. She was treated for a sore throat infection one month ago. Speculum examination shows erythema around the vaginal introitus and copious white discharge. Vaginal pH is 4.3 and a KOH test shows multiple pseudohyphae on microscopy. sentences: - Intrauterine device - Generalized anxiety disorder - Suppression of vaginal bacterial flora - source_sentence: Which of the following is the most life-threatening complication of amobarbital withdrawal? A 46-year-old anesthesiologist is found placing several syringes of amobarbital in his backpack prior to leaving the hospital. When confronted, the anesthesiologist admits that he began abusing the medication the previous year, after his divorce was finalized. He has been using it on a daily basis since then, and his most recent usage was 8 hours ago. sentences: - Internal bleeding - Cardiovascular collapse - ß-cell hyperplasia - source_sentence: What medication is the patient most likely taking? A 19-year-old woman is brought to the emergency department by ambulance after experiencing a first-time seizure. She as preparing for an exam at her college’s library, in her normal state of health and collapsed. When she regained consciousness she was surrounded by students and staff. The emergency personnel assessed her condition and brought her in. Past medical history is significant for major depressive disorder. Her primary care physician prescribed a medication for her depression, but she has not taken it for several days because she was concerned about weight gain. Family medical history is insignificant for neurological disorders. Instead, for the past 10 days, she has been taking her roommate’s antidepressant medication instead. Today her blood pressure is 100/80 mm Hg, pulse 102/min, respirations 12/min and he BMI is 15 kg/m2. Physical examination reveals pale and dry mucosa and conjunctiva, and lanugo on her arms and legs. sentences: - Amitriptyline - Bupropion - Dutasteride - source_sentence: Which of the following statements best describes the rationale for administering RhO(D) immunoglobulins (RhoGAM) in this patient? A 28-year-old G1P0 primigravida woman at 28 weeks estimated gestational age presents for routine prenatal care. She has no complaints and says she can feel her baby move and respond to outside sounds. The patient has no significant past medical or family history. Currently, she is taking a prenatal multivitamin which contains iron and folic acid. Her blood type is A (-) negative, and her husband is A (+) positive. The patient says she stopped drinking alcohol 2 years ago and denies any history of smoking or recreational drug use. Her pulse is 90/min, blood pressure is 114/68 mm Hg, and respiratory rate is 18/min. She has gained 9.0 kg (19.8 lb) over the course of the pregnancy. Physical examination shows a gravid uterus, extending 28 cm above the pubic symphysis. Occasional movements are observed in the abdomen. There is no guarding or tenderness to palpation. Fetal heart sounds can be auscultated. The remainder of the examination is unremarkable. The patient is administered an injection of RhO(D) immunoglobulin (RhoGAM). sentences: - Switch from hydrocodone to hydromorphone - RhO(D) immunoglobulin will prevent hemolytic disease in this pregnancy. - RhO(D) immunoglobulins will prevent anti-D antibody formation in the mother. pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on YKYSpatz/ragproject_ver3 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [YKYSpatz/ragproject_ver3](https://huggingface.co/YKYSpatz/ragproject_ver3). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [YKYSpatz/ragproject_ver3](https://huggingface.co/YKYSpatz/ragproject_ver3) <!-- at revision b67076517265fa9847e2f536d1cbf2d778ee3ac2 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 384 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'Which of the following statements best describes the rationale for administering RhO(D) immunoglobulins (RhoGAM) in this patient? A 28-year-old G1P0 primigravida woman at 28 weeks estimated gestational age presents for routine prenatal care. She has no complaints and says she can feel her baby move and respond to outside sounds. The patient has no significant past medical or family history. Currently, she is taking a prenatal multivitamin which contains iron and folic acid. Her blood type is A (-) negative, and her husband is A (+) positive. The patient says she stopped drinking alcohol 2 years ago and denies any history of smoking or recreational drug use. Her pulse is 90/min, blood pressure is 114/68 mm Hg, and respiratory rate is 18/min. She has gained 9.0 kg (19.8 lb) over the course of the pregnancy. Physical examination shows a gravid uterus, extending 28 cm above the pubic symphysis. Occasional movements are observed in the abdomen. There is no guarding or tenderness to palpation. Fetal heart sounds can be auscultated. The remainder of the examination is unremarkable. The patient is administered an injection of RhO(D) immunoglobulin (RhoGAM).', 'RhO(D) immunoglobulins will prevent anti-D antibody formation in the mother.', 'RhO(D) immunoglobulin will prevent hemolytic disease in this pregnancy.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,272 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| sentence_2 \| \|:--------\|:-------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 19 tokens</li><li>mean: 169.87 tokens</li><li>max: 512 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 8.78 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 8.57 tokens</li><li>max: 42 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| sentence_2 \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------\|:-------------------------------------------\| \| <code>This patient's symptoms are most likely caused by obstruction at which of the following locations? A 67-year-old woman comes to the physician because of a 5-day history of episodic abdominal pain, nausea, and vomiting. She has coronary artery disease and type 2 diabetes mellitus. She takes aspirin, metoprolol, and metformin. She is 163 cm (5 ft 4 in) tall and weighs 91 kg (200 lb); her BMI is 34 kg/m2. Her temperature is 38.1°C (100.6°F). Physical examination shows dry mucous membranes, abdominal distension, and hyperactive bowel sounds. Ultrasonography of the abdomen shows air in the biliary tract.</code> \| <code>Distal ileum</code> \| <code>Third part of the duodenum</code> \| \| <code>Which of the following is most likely associated with the cause of this patient's symptoms? A 67-year-old man presents to his primary care physician because he has been feeling increasingly short of breath. Specifically, after retirement he has been going on daily morning walks with his wife; however, over the last year he feels that his endurance has decreased. His medical history is significant for well-controlled hypertension but is otherwise unremarkable. When asked, he reveals that he worked in a variety of industries throughout his life. Testing demonstrates decreased forced vital capacity (FVC) and a normal forced expiratory volume (FEV) to FVC ratio. Pathology demonstrates changes primarily in the upper lobes where macrophages can be seen with dark round ingested particles.</code> \| <code>Lung rheumatoid nodules</code> \| <code>Increased risk of lung cancer</code> \| \| <code>Which of the following is the most appropriate recommendation by the physician? A 14-year-old boy is brought to the physician by his mother because of a 1-week history of fever, fatigue, and throat pain. He appears lethargic. His temperature is 38.5°C (101.3°F). Physical examination shows bilateral cervical lymphadenopathy. Oral examination shows the findings in the photograph. A peripheral blood smear shows lymphocytosis with atypical lymphocytes. A heterophile antibody test is positive.</code> \| <code>Avoid contact sports</code> \| <code>Start antiretroviral therapy</code> \| * Loss: <code>__main__.LoggingTripletLoss</code> with these parameters: ```json { "distance_metric": "TripletDistanceMetric.COSINE", "triplet_margin": 1.0 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 20 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 20 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:-----:\|:----:\|:-------------:\| \| 6.25 \| 500 \| 0.8887 \| \| 12.5 \| 1000 \| 0.5373 \| \| 18.75 \| 1500 \| 0.3806 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.52.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.7.0 - Datasets: 2.14.4 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### LoggingTripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
lmcastanedame/ppo-LunarLander-v2	lmcastanedame	2025-06-04T13:04:36Z	16	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-05-28T14:24:11Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -172.66 +/- 97.92 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.0001 'num_envs': 4 'num_steps': 256 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'lmcastanedame/ppo-LunarLander-v2' 'batch_size': 1024 'minibatch_size': 256} ```
automated-analytics/qwen3-8b-pii-masking-gguf	automated-analytics	2025-06-04T13:02:39Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-04T13:01:08Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: automated-analytics - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-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)
dhead/Ah-My-Goddess-illustriousXL	dhead	2025-06-04T13:01:08Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:calcuis/illustrious", "base_model:adapter:calcuis/illustrious", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-04T11:14:36Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: "UNICODE\0\0O\0V\0A\0 \0S\0t\0y\0l\0e\0,\0B\0e\0l\0l\0d\0a\0n\0d\0y\0,\01\0g\0i\0r\0l\0,\0s\0o\0l\0o\0,\0f\0a\0c\0i\0a\0l\0 \0m\0a\0r\0k\0,\0f\0o\0r\0e\0h\0e\0a\0d\0 \0m\0a\0r\0k\0,\0l\0o\0n\0g\0 \0h\0a\0i\0r\0,\0b\0l\0u\0e\0 \0e\0y\0e\0s\0,\01\09\09\00\0s\0 \0\\\0(\0s\0t\0y\0l\0e\0\\\0)\0,\0r\0e\0t\0r\0o\0 \0a\0r\0t\0s\0t\0y\0l\0e\0,\0b\0r\0o\0w\0n\0 \0h\0a\0i\0r\0,\0f\0l\0o\0w\0e\0r\0,\0j\0e\0w\0e\0l\0r\0y\0,\0g\0l\0o\0v\0e\0s\0,\0f\0i\0n\0g\0e\0r\0l\0e\0s\0s\0 \0g\0l\0o\0v\0e\0s\0,\0b\0r\0a\0c\0e\0l\0e\0t\0,\0l\0o\0o\0k\0i\0n\0g\0 \0a\0t\0 \0v\0i\0e\0w\0e\0r\0,\0" output: url: >- images/45676-4047141515-OVA Style,Belldandy,1girl,solo,facial mark,forehead mark,long hair,blue eyes,1990s _(style_),retro artstyle,brown hair,flower,je.jpg base_model: calcuis/illustrious instance_prompt: >- Belldandy, Urd, Skuld, Lind, Peorth, Hild, Keima Morisato, Megumi Morisato, Morgan license: creativeml-openrail-m --- # Ah_My Goddess_illustriousXL <Gallery /> ## Model description Original model https://civitai.com/models/999695 ## Trigger words You should use `Belldandy` to trigger the image generation. You should use `Urd` to trigger the image generation. You should use `Skuld` to trigger the image generation. You should use `Lind` to trigger the image generation. You should use `Peorth` to trigger the image generation. You should use `Hild` to trigger the image generation. You should use `Keima Morisato` to trigger the image generation. You should use `Megumi Morisato` to trigger the image generation. You should use `Morgan` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/dhead/Ah-My-Goddess-illustriousXL/tree/main) them in the Files & versions tab.
Snarcy/mit-b0_train_006	Snarcy	2025-06-04T12:59:52Z	4	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T08:34:35Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer model-index: - name: mit-b0_train_006 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. --> # mit-b0_train_006 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0165 - Mean Iou: 0.6072 - Mean Accuracy: 0.6146 - Overall Accuracy: 0.9950 - Per Category Iou: [0.994959771927113, 0.21945598185784537] - Per Category Accuracy: [0.9997181994608846, 0.22942032906858387] ## 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: 6e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------------------------:\|:------------------------------------------:\| \| 0.0721 \| 1.9608 \| 200 \| 0.0672 \| 0.6169 \| 0.6464 \| 0.9942 \| [0.9942316427250634, 0.23966291663168499] \| [0.9985865991688804, 0.2942419440042261] \| \| 0.0318 \| 3.9216 \| 400 \| 0.0341 \| 0.5172 \| 0.5208 \| 0.9939 \| [0.9939027802641532, 0.04057789730439677] \| [0.9998135190530031, 0.041797121616224085] \| \| 0.026 \| 5.8824 \| 600 \| 0.0275 \| 0.6320 \| 0.6558 \| 0.9948 \| [0.9947562822651198, 0.26925509603968084] \| [0.9989999439437633, 0.31264092355662065] \| \| 0.0225 \| 7.8431 \| 800 \| 0.0230 \| 0.6470 \| 0.6678 \| 0.9951 \| [0.9951243182184517, 0.2989224409403735] \| [0.9992232092971147, 0.3363354077280868] \| \| 0.0197 \| 9.8039 \| 1000 \| 0.0181 \| 0.6592 \| 0.6825 \| 0.9953 \| [0.9952601104686506, 0.32306869760087364] \| [0.9991768289586679, 0.3659180807338977] \| \| 0.0164 \| 11.7647 \| 1200 \| 0.0181 \| 0.5987 \| 0.6050 \| 0.9949 \| [0.9948881920323499, 0.2025968544793257] \| [0.9997644198671299, 0.210286936466829] \| \| 0.0218 \| 13.7255 \| 1400 \| 0.0221 \| 0.6336 \| 0.6466 \| 0.9952 \| [0.9951416030592088, 0.2720223325062035] \| [0.999503330548114, 0.29379098862303993] \| \| 0.0183 \| 15.6863 \| 1600 \| 0.0200 \| 0.6122 \| 0.6206 \| 0.9950 \| [0.9949913133949455, 0.22946002203991675] \| [0.9996755775291738, 0.24145439552652262] \| \| 0.0167 \| 17.6471 \| 1800 \| 0.0170 \| 0.6178 \| 0.6270 \| 0.9950 \| [0.9950421772328908, 0.2406183706810368] \| [0.9996474294617026, 0.25428729723113397] \| \| 0.011 \| 19.6078 \| 2000 \| 0.0165 \| 0.6072 \| 0.6146 \| 0.9950 \| [0.994959771927113, 0.21945598185784537] \| [0.9997181994608846, 0.22942032906858387] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
John6666/perfect-rsb-mix-illustrious-definitivedelta-sdxl	John6666	2025-06-04T12:57:49Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "game", "cartoon", "hentai", "furry", "girls", "styles", "from extreme realism to conceptual art", "portraits", "landscapes", "illustrations", "complex scenes", "sharp details", "vibrant colors", "balanced compositions", "hands", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-06-04T12:50:57Z	--- 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 - game - cartoon - hentai - furry - girls - styles - from extreme realism to conceptual art - portraits - landscapes - illustrations - complex scenes - sharp details - vibrant colors - balanced compositions - hands - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/917668?modelVersionId=1866841). This model created by [frakerkill_](https://civitai.com/user/frakerkill_).
OzzeY72/biobert-medical-specialities2	OzzeY72	2025-06-04T12:57:46Z	0	0	null	[ "safetensors", "bert", "text-classification", "en", "dataset:martagm17/test", "base_model:dmis-lab/biobert-large-cased-v1.1", "base_model:finetune:dmis-lab/biobert-large-cased-v1.1", "license:apache-2.0", "region:us" ]	text-classification	2025-06-04T12:50:09Z	--- license: apache-2.0 datasets: - martagm17/test language: - en base_model: - dmis-lab/biobert-large-cased-v1.1 pipeline_tag: text-classification --- # 🧠 BioBERT-Medical-Specialities2 BioBERT-Medical-Specialities2 is a fine-tuned [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) model for multi-class medical text classification. It classifies short medical questions or symptom descriptions into one of 35 clinical specialities. ## 📊 Labels This model predicts the following 35 medical specialties: None, Cardiology, Hematology, Oncology, Endocrinology, Respiratory, Allergy, Dermatology, Nephrology, Gastroenterology, Rheumatology, Otorhinolaryngology, Anesthesiology, Biochemistry, Pharmacology, Psychiatry, Microbiology, Physiology, Pathology, Obstetrics, Gynecology, Surgery, Emergency, Orthopedics, Neurology, Urology, Anatomy, Genetics, Radiology, Ophthalmology, Odontology, Pediatrics, Geriatrics, Nursing, Chemistry, Psychology ## 📦 Model Details - Base model: [`dmis-lab/biobert-base-cased-v1.1`](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) - Fine-tuned on: [`martagm17/test`](https://huggingface.co/datasets/martagm17/test) - Task: Multi-class medical text classification - Languages: English 🇬🇧 - License: Apache 2.0 --- ## 🚀 How to Use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer model_name = "your-username/biobert-medical-specialities" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) # Example input text = "I have constant chest pain and shortness of breath." # Tokenize and predict inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): logits = model(**inputs).logits predicted_class_id = logits.argmax().item() predicted_label = model.config.id2label[predicted_class_id] print(f"Predicted medical speciality: {predicted_label}")
phospho-app/PAphospho-gr00t-orange-circle-black-box-2-3008	phospho-app	2025-06-04T12:53:24Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-06-04T09:22:17Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [PAphospho/orange-circle-black-box-2](https://huggingface.co/datasets/PAphospho/orange-circle-black-box-2) - Wandb run URL: None - Epochs: 8 - Batch size: 30 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
Goranmk20/vit-large-patch16-224-finetuned-eurosat	Goranmk20	2025-06-04T12:52:50Z	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-large-patch16-224", "base_model:finetune:google/vit-large-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-03-15T23:05:42Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-large-patch16-224 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: vit-large-patch16-224-finetuned-eurosat 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. --> # vit-large-patch16-224-finetuned-eurosat This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4947 - Accuracy: 0.875 - Precision: 0.8796 - Recall: 0.8760 - F1: 0.8715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 2.0089 \| 1.0 \| 10 \| 1.4560 \| 0.6188 \| 0.6505 \| 0.6232 \| 0.6086 \| \| 0.8976 \| 2.0 \| 20 \| 0.4947 \| 0.875 \| 0.8796 \| 0.8760 \| 0.8715 \| \| 0.3555 \| 3.0 \| 30 \| 0.4390 \| 0.8313 \| 0.8272 \| 0.8339 \| 0.8274 \| \| 0.1709 \| 4.0 \| 40 \| 0.4177 \| 0.85 \| 0.8506 \| 0.8559 \| 0.8484 \| \| 0.0882 \| 5.0 \| 50 \| 0.4561 \| 0.8625 \| 0.8630 \| 0.8610 \| 0.8592 \| \| 0.1136 \| 6.0 \| 60 \| 0.5954 \| 0.8375 \| 0.8494 \| 0.8346 \| 0.8231 \| \| 0.0863 \| 7.0 \| 70 \| 0.5323 \| 0.8375 \| 0.8353 \| 0.8373 \| 0.8349 \| \| 0.0637 \| 8.0 \| 80 \| 0.4966 \| 0.8438 \| 0.8579 \| 0.8470 \| 0.8476 \| \| 0.0536 \| 9.0 \| 90 \| 0.6577 \| 0.8375 \| 0.8598 \| 0.8423 \| 0.8383 \| \| 0.1069 \| 10.0 \| 100 \| 0.5586 \| 0.8438 \| 0.8417 \| 0.8429 \| 0.8387 \| \| 0.0446 \| 11.0 \| 110 \| 0.5196 \| 0.85 \| 0.8703 \| 0.8520 \| 0.8520 \| \| 0.0401 \| 12.0 \| 120 \| 0.4480 \| 0.8625 \| 0.8651 \| 0.8653 \| 0.8632 \| \| 0.0333 \| 13.0 \| 130 \| 0.4955 \| 0.8375 \| 0.8441 \| 0.8422 \| 0.8393 \| \| 0.036 \| 14.0 \| 140 \| 0.5074 \| 0.8438 \| 0.8515 \| 0.8477 \| 0.8469 \| \| 0.0288 \| 15.0 \| 150 \| 0.5466 \| 0.8438 \| 0.8465 \| 0.8441 \| 0.8426 \| \| 0.0141 \| 16.0 \| 160 \| 0.6208 \| 0.8313 \| 0.8400 \| 0.8324 \| 0.8299 \| \| 0.0145 \| 17.0 \| 170 \| 0.5696 \| 0.8438 \| 0.8547 \| 0.8458 \| 0.8449 \| \| 0.0194 \| 18.0 \| 180 \| 0.5469 \| 0.8562 \| 0.8606 \| 0.8575 \| 0.8566 \| \| 0.0139 \| 19.0 \| 190 \| 0.5600 \| 0.8625 \| 0.8658 \| 0.8670 \| 0.8634 \| \| 0.0184 \| 20.0 \| 200 \| 0.5611 \| 0.8625 \| 0.8658 \| 0.8675 \| 0.8638 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Snarcy/mit-b0_train_005	Snarcy	2025-06-04T12:52:40Z	2	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T08:11:26Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer model-index: - name: mit-b0_train_005 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. --> # mit-b0_train_005 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0127 - Mean Iou: 0.6129 - Mean Accuracy: 0.6161 - Overall Accuracy: 0.9952 - Per Category Iou: [0.9952057533188612, 0.2306542867725003] - Per Category Accuracy: [0.9999569777286176, 0.23225009966435617] ## 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: 6e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------------------------:\|:-----------------------------------------:\| \| 0.07 \| 1.9608 \| 200 \| 0.0629 \| 0.4969 \| 0.5 \| 0.9938 \| [0.9938201109568278, 0.0] \| [1.0, 0.0] \| \| 0.0314 \| 3.9216 \| 400 \| 0.0289 \| 0.5993 \| 0.6057 \| 0.9949 \| [0.9948789861772495, 0.20363082863082863] \| [0.999756340407245, 0.21160993299983283] \| \| 0.0259 \| 5.8824 \| 600 \| 0.0211 \| 0.5196 \| 0.5226 \| 0.9941 \| [0.9940950297879796, 0.04510862578737627] \| [0.9999976809556318, 0.04512544848960276] \| \| 0.0229 \| 7.8431 \| 800 \| 0.0168 \| 0.6238 \| 0.6282 \| 0.9953 \| [0.9952967343000737, 0.2523093665375952] \| [0.9998988416853183, 0.2564138835663122] \| \| 0.0189 \| 9.8039 \| 1000 \| 0.0163 \| 0.5593 \| 0.5622 \| 0.9946 \| [0.9945650410332045, 0.1240787028135615] \| [0.9999796883700165, 0.12448399583338692] \| \| 0.0168 \| 11.7647 \| 1200 \| 0.0147 \| 0.5521 \| 0.5549 \| 0.9945 \| [0.9944917328674545, 0.10976440497153066] \| [0.9999966413840184, 0.10982369053895912] \| \| 0.0198 \| 13.7255 \| 1400 \| 0.0143 \| 0.6967 \| 0.7037 \| 0.9962 \| [0.9961691352062944, 0.39732517767388226] \| [0.999838466564698, 0.4076465066035673] \| \| 0.0178 \| 15.6863 \| 1600 \| 0.0134 \| 0.6311 \| 0.6342 \| 0.9954 \| [0.9954341803455777, 0.2668285823852742] \| [0.9999624954548729, 0.2684379058911279] \| \| 0.0164 \| 17.6471 \| 1800 \| 0.0123 \| 0.6280 \| 0.6317 \| 0.9954 \| [0.9953763729825562, 0.26067919148719515] \| [0.9999357864611151, 0.2633710986227029] \| \| 0.0114 \| 19.6078 \| 2000 \| 0.0127 \| 0.6129 \| 0.6161 \| 0.9952 \| [0.9952057533188612, 0.2306542867725003] \| [0.9999569777286176, 0.23225009966435617] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
mci29/sn29_z0m3_fgme	mci29	2025-06-04T12:52:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-04T12:48:23Z	--- 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. 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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PepitaxX/qwen3-0.6B-openQA_mydataset_deepseeketqcm_lora32	PepitaxX	2025-06-04T12:52:10Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-04T12:52:04Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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Diamantis99/RCQ36j8	Diamantis99	2025-06-04T12:49:36Z	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	2025-06-04T12:49:19Z	--- 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 --- # DeepLabV3 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", "encoder_output_stride": 8, "decoder_channels": 256, "decoder_atrous_rates": (12, 24, 36), "decoder_aspp_separable": False, "decoder_aspp_dropout": 0.5, "in_channels": 3, "classes": 1, "activation": None, "upsampling": None, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8510321378707886, "test_dataset_iou": 0.8771003484725952 } ] ``` ## 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)
anonloftune/pythia-12b-insurance-40-loftune-efs	anonloftune	2025-06-04T12:49:28Z	0	0	peft	[ "peft", "safetensors", "dataset:anonloftune/insurance-40-loftune-efs", "arxiv:1910.09700", "base_model:anonloftune/pythia-12b-insurance-40-sft", "base_model:adapter:anonloftune/pythia-12b-insurance-40-sft", "region:us" ]	null	2025-06-02T16:11:27Z	--- library_name: peft base_model: anonloftune/pythia-12b-insurance-40-sft datasets: - anonloftune/insurance-40-loftune-efs --- # 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 0.9.0

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