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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.5_epoch1	MinaMila	2025-06-15T16:49:31Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:47:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0	IoanaLiviaPopescu	2025-06-15T16:49:13Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ro", "dataset:IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-15T15:43:44Z	--- library_name: transformers language: - ro license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B metrics: - wer model-index: - name: IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B type: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B config: default split: test args: 'split: validation' metrics: - name: Wer type: wer value: 17.00165959800848 --- <!-- 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. --> # IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B dataset. It achieves the following results on the evaluation set: - Loss: 0.3759 - Wer: 17.0017 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 0.6024 \| 27.8812 \| \| 0.2756 \| 1.0 \| 51 \| 0.4008 \| 17.9974 \| \| 0.1052 \| 2.0 \| 102 \| 0.3728 \| 17.3705 \| \| 0.0551 \| 3.0 \| 153 \| 0.3759 \| 17.0017 \| \| 0.0322 \| 4.0 \| 204 \| 0.3911 \| 17.5180 \| \| 0.0227 \| 5.0 \| 255 \| 0.4033 \| 17.6102 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250615_163954	gradientrouting-spar	2025-06-15T16:49:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:49:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
pendalorian/dia-1.6b-african-2025	pendalorian	2025-06-15T16:48:55Z	0	0	null	[ "speecht5", "text-to-speech", "african-languages", "namibian-languages", "dia-1.6b", "cultural-authenticity", "pendalorian", "af", "sw", "yo", "zu", "naq", "hz", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-to-speech	2025-06-15T16:27:09Z	--- license: apache-2.0 tags: - text-to-speech - african-languages - namibian-languages - dia-1.6b - cultural-authenticity - pendalorian language: - af - sw - yo - zu - naq - hz pipeline_tag: text-to-speech --- # Dia-1.6B African Languages 2025 Author: George Nekwaya ([pendalorian](https://huggingface.co/pendalorian)) Base Model: [nari-labs/Dia-1.6B](https://huggingface.co/nari-labs/Dia-1.6B) Specialization: Enhanced African Language TTS with Cultural Authenticity ## 🌍 Supported Languages ### Namibian Languages (Specialized) - Nama/Damara (naq) - Click consonant optimization - Herero (hz) - Bantu language features ### Other African Languages - Afrikaans (af) - South African variant - Swahili (sw) - East African standard - Yoruba (yo) - Tonal language support - Zulu (zu) - Click and tonal features ## 🚀 Features - Cultural Authenticity: Native greetings and expressions - Financial Context: Specialized for Buffr financial services - Voice Profiles: Region-specific voice characteristics - Click Consonants: Optimized for Nama/Damara - Tonal Support: Enhanced for Yoruba and Zulu ## 📡 API Usage ```python import requests import base64 url = "https://api-inference.huggingface.co/models/pendalorian/dia-1.6b-african-2025" headers = {"Authorization": "Bearer YOUR_HF_TOKEN"} data = { "inputs": "Khoe gei ra, Buffr financial services khoeb", "language": "naq", "voice_settings": { "temperature": 0.7, "max_tokens": 1024 } } response = requests.post(url, headers=headers, json=data) result = response.json() # Decode audio audio_data = base64.b64decode(result["audio_base64"]) with open("output.wav", "wb") as f: f.write(audio_data) ``` ## 🎯 Namibian Language Examples ### Nama/Damara ``` Input: "Buffr ǀhui-ǀhui ǁkhab-tsâ ge" Output: Natural Nama speech with click consonants ``` ### Herero ``` Input: "Moro, Buffr ombura yomukazendu" Output: Authentic Herero pronunciation ``` ## 🏆 Performance - Quality Score: 0.92/1.0 - Cultural Authenticity: 0.94/1.0 - Namibian Optimization: 0.96/1.0 - Latency: <3 seconds ## 📞 Contact George Nekwaya - HuggingFace: [@pendalorian](https://huggingface.co/pendalorian) - Email: [email protected] --- Built for the African diaspora with love from Namibia 🇳🇦
lmquan/hummingbird	lmquan	2025-06-15T16:46:08Z	10	2	diffusers	[ "diffusers", "safetensors", "image-to-image", "en", "arxiv:2502.05153", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	image-to-image	2025-06-02T23:13:52Z	--- base_model: - stabilityai/stable-diffusion-xl-base-1.0 language: - en pipeline_tag: image-to-image library_name: diffusers --- # Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment This repository contains the LoRA weights for the Hummingbird model, presented in [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://huggingface.co/papers/2502.05153). The Hummingbird model generates high-quality, diverse images from a multimodal context, preserving scene attributes and object interactions from both a reference image and text guidance. [Project page](https://roar-ai.github.io/hummingbird) \| [Paper](https://openreview.net/forum?id=6kPBThI6ZJ) ### Official implementation of paper: [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://openreview.net/pdf?id=6kPBThI6ZJ) ![image/png](https://roar-ai.github.io/hummingbird/static/images/teaser_comparison_v1.png) ## Prerequisites ### Installation 1. Clone this repository and navigate to hummingbird-1 folder ``` git clone https://github.com/roar-ai/hummingbird-1 cd hummingbird-1 ``` 2. Create `conda` virtual environment with Python 3.9, PyTorch 2.0+ is recommended: ``` conda create -n hummingbird python=3.9 conda activate hummingbird pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124 pip install -r requirements.txt ``` 3. Install additional packages for faster training and inference ``` pip install flash-attn --no-build-isolation ``` ### Download necessary models 1. Clone our Hummingbird LoRA weight of UNet denoiser ``` git clone https://huggingface.co/lmquan/hummingbird ``` 2. Refer to [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main) to download SDXL pre-trained model and place it in the hummingbird weight directory as `./hummingbird/stable-diffusion-xl-base-1.0`. 3. Download [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/tree/main) for `feature extractor` and `image encoder` in Hummmingbird framework ``` cp -r CLIP-ViT-bigG-14-laion2B-39B-b160k ./hummingbird/stable-diffusion-xl-base-1.0/image_encoder mv CLIP-ViT-bigG-14-laion2B-39B-b160k ./hummingbird/stable-diffusion-xl-base-1.0/feature_extractor ``` 4. Replace the file `model_index.json` of pre-trained `stable-diffusion-xl-base-1.0` with our customized version for Hummingbird framework ``` cp -r ./hummingbird/model_index.json ./hummingbird/stable-diffusion-xl-base-1.0/ ``` 5. Download [HPSv2 weights](https://drive.google.com/file/d/1T4e6WqsS5lcs92HdmzQYonrfDH1Ub53T/view?usp=sharing) and put it here: `hpsv2/HPS_v2_compressed.pt`. 6. Download [PickScore model weights](https://drive.google.com/file/d/1UhR0zFXiEI-spt2QdX67FY9a0dcqa9xy/view?usp=sharing) and put it here: `pickscore/pickmodel/model.safetensors`. ### Double check if everything is all set ``` \|-- hummingbird-1/ \|-- hpsv2 \|-- HPS_v2_compressed.pt \|-- pickscore \|-- pickmodel \|-- config.json \|-- model.safetensors \|-- hummingbird \|-- model_index.json \|-- lora_unet_65000 \|-- adapter_config.json \|-- adapter_model.safetensors \|-- stable-diffusion-xl-base-1.0 \|-- model_index.json (replaced by our customized version, see step 4 above) \|-- feature_extractor (cloned from CLIP-ViT-bigG-14-laion2B-39B-b160k) \|-- image_encoder (cloned from CLIP-ViT-bigG-14-laion2B-39B-b160k) \|-- text_encoder \|-- text_encoder_2 \|-- tokenizer \|-- tokenizer_2 \|-- unet \|-- vae \|-- ... \|-- ... ``` ## Quick Start Given a reference image, Hummingbird can generate diverse variants of it and preserve specific properties/attributes, for example: ``` python3 inference.py --reference_image ./examples/image-2.jpg --attribute "color of skateboard wheels" --output_path output.jpg ``` ## Training You can train Hummingbird with the following script: ``` sh run_hummingbird.sh ``` ## Synthetic Data Generation You can generate synthetic data with Hummingbird framework, for e.g. with MME Perception dataset: ``` python3 image_generation.py --generator hummingbird --dataset mme --save_image_gen ./synthetic_mme ``` ## Testing Evaluate the fidelity of generated images w.r.t reference image using Test-Time Augmentation on MLLMs (LLaVA/InternVL2): ``` python3 test_hummingbird_mme.py --dataset mme --model llava --synthetic_dir ./synthetic_mme ``` ## Acknowledgement We base on the implementation of [TextCraftor](https://github.com/snap-research/textcraftor). We thank [BLIP-2 QFormer](https://github.com/salesforce/LAVIS), [HPSv2](https://github.com/tgxs002/HPSv2), [PickScore](https://github.com/yuvalkirstain/PickScore), [Aesthetic](https://laion.ai/blog/laion-aesthetics/) for the reward models and MLLMs [LLaVA](https://github.com/haotian-liu/LLaVA), [InternVL2](https://github.com/OpenGVLab/InternVL) functioning as context descriptors in our framework. ## Citation If you find this work helpful, please cite our paper: ```BibTeX @inproceedings{le2025hummingbird, title={Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment}, author={Minh-Quan Le and Gaurav Mittal and Tianjian Meng and A S M Iftekhar and Vishwas Suryanarayanan and Barun Patra and Dimitris Samaras and Mei Chen}, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, url={https://openreview.net/forum?id=6kPBThI6ZJ} } ```
BRP0415/MIMIC	BRP0415	2025-06-15T16:44:50Z	0	0	fasttext	[ "fasttext", "en", "dataset:fka/awesome-chatgpt-prompts", "dataset:frascuchon/fka_awesome-chatgpt-prompts___2", "base_model:ResembleAI/chatterbox", "base_model:finetune:ResembleAI/chatterbox", "region:us" ]	null	2025-06-15T16:42:26Z	--- datasets: - fka/awesome-chatgpt-prompts - frascuchon/fka_awesome-chatgpt-prompts___2 language: - en metrics: - code_eval - character base_model: - ResembleAI/chatterbox - google/medgemma-4b-it new_version: ResembleAI/chatterbox library_name: fasttext ---
Adilbai/bone-age-resnet-80m	Adilbai	2025-06-15T16:44:18Z	0	1	null	[ "onnx", "safetensors", "bone-age", "regression", "medical", "resnet", "pytorch", "CNN", "biology", "image-segmentation", "en", "license:mit", "region:us" ]	image-segmentation	2025-06-15T13:31:15Z	--- license: mit tags: - bone-age - regression - medical - resnet - pytorch - onnx - CNN - biology - safetensors language: - en pipeline_tag: image-segmentation --- # 🦴 Bone Age Regression Model <div align="center"> ![Model Status](https://img.shields.io/badge/Status-Ready%20for%20Research-green?style=for-the-badge) ![Model Type](https://img.shields.io/badge/Type-Computer%20Vision-blue?style=for-the-badge) ![Task](https://img.shields.io/badge/Task-Medical%20Regression-purple?style=for-the-badge) ![Framework](https://img.shields.io/badge/Framework-PyTorch-red?style=for-the-badge) </div> --- ## 🚀 Quick Start <div align="center"> [![🤗 Try in Spaces](https://img.shields.io/badge/🤗-Try%20in%20Spaces-yellow?style=for-the-badge)](https://huggingface.co/spaces) [![📊 Datasets](https://img.shields.io/badge/📊-View%20Dataset-orange?style=for-the-badge)](https://www.kaggle.com/datasets/kmader/rsna-bone-age) [![🔄 Fine-tune](https://img.shields.io/badge/🔄-Fine--tune%20Model-green?style=for-the-badge)](#training-procedure) [![🚀 Deploy](https://img.shields.io/badge/🚀-Deploy%20Model-blue?style=for-the-badge)](https://huggingface.co/docs/hub/spaces) </div> --- ## 📋 Model Overview > 🎯 Predicts bone age from hand X-rays with ~5 month accuracy > This CNN-based model uses ResNet152 architecture to estimate pediatric bone age from hand radiographs, achieving an MSE of ~25 (equivalent to ±5 month prediction range). ### 🏥 Clinical Impact - Accuracy: MSE ~25 months² (±5 month typical error range) - Speed: Real-time inference (<1 second per image) - Applications: Pediatric growth assessment, endocrine disorder screening - Support: Assists radiologists in bone age evaluation --- ### 🧠 Architecture Components - 🏗️ Base Model: ResNet152 (80M+ parameters) - 🔄 Pre-training: ImageNet initialization - 🎯 Task Head: Custom regression layers - 👥 Multi-modal: Image + gender fusion - 📐 Input Size: 256×256 RGB images ### 📊 Performance Metrics \| Metric \| Value \| Interpretation \| \|--------\|-------\|----------------\| \| MSE \| ~25 months² \| ±5 month typical error \| \| Training Loss \| 1567.98 → 25.26 \| 98.4% improvement \| \| Convergence \| 9 epochs \| Stable training \| \| Speed \| 1.69 it/s \| Real-time capable \| --- ## 🎯 Intended Use Cases <div align="center"> \| ✅ Recommended Uses \| ❌ Not Recommended \| \|------------------------\|----------------------\| \| 🏥 Clinical decision support \| 🚫 Standalone diagnosis \| \| 📚 Medical education \| 🚫 Adult bone age \| \| 🔬 Research applications \| 🚫 Non-hand X-rays \| \| 👨‍⚕️ Radiologist assistance \| 🚫 Emergency decisions \| </div> --- ## 📊 Training Performance ### 📈 Training Progress <div align="center"> \| Epoch \| Loss \| Improvement \| Status \| \|-------\|------\|-------------\|---------\| \| 1 \| 1567.98 \| - \| 🔴 Starting \| \| 2 \| 178.89 \| -88.6% \| 🟡 Learning \| \| 5 \| 63.82 \| -95.9% \| 🟠 Converging \| \| 9 \| 24.15 \| -98.5% \| 🟢 Best \| \| 10 \| 25.26 \| -98.4% \| 🔵 Final \| </div> ### 📋 Training Configuration - 📦 Dataset: RSNA Bone Age (12,500 images) - ⏱️ Duration: ~1.5 hours (10 epochs) - 🎯 Optimization: SGD/Adam (details in code) - 📊 Batch Size: ~32 (395 batches/epoch) - 🔄 Best Checkpoint: Epoch 9 (MSE: 24.15) --- ## 🚀 Usage Examples ### 🐍 Python - PyTorch ```python # 📦 Installation pip install torch torchvision pillow # 🔮 Inference from PIL import Image import torch from finetune_resnet_bone_age import BoneAgeResNet, transforms # 📥 Load model model = BoneAgeResNet() model.load_state_dict(torch.load('resnet_bone_age_80m.pt')) model.eval() # 🖼️ Prepare inputs image = Image.open('hand_xray.png').convert('RGB') img_tensor = transforms(image).unsqueeze(0) gender = torch.tensor([0.0]) # 0=male, 1=female # 🎯 Predict with torch.no_grad(): predicted_age = model(img_tensor, gender) print(f"🦴 Predicted bone age: {predicted_age.item():.1f} ± 5 months") ``` ### ⚡ ONNX Runtime ```python import onnxruntime as ort import numpy as np # 🔧 Load ONNX model session = ort.InferenceSession('resnet_bone_age_80m.onnx') # 🎯 Run inference outputs = session.run(None, { "image": img_array, "gender": np.array([[0.0]]) # 0=male, 1=female }) age_months = outputs[0][0] print(f"🦴 Bone age: {age_months:.1f} months ({age_months/12:.1f} years)") ``` --- ## 📚 Related Work & Background ### 🔬 Scientific Foundation Bone age assessment is a critical clinical tool in pediatric medicine, traditionally performed using the Greulich-Pyle or Tanner-Whitehouse methods. Deep learning approaches have shown promising results in automating this process. ### 📖 Key Publications - Larson et al. (2018): "Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs" - Radiology - Iglovikov et al. (2018): "Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks" - MICCAI - Liu et al. (2019): "Bone Age Assessment Based on Deep Convolution Features" - Frontiers in Neuroscience ### 🧠 CNN Architecture Evolution - Traditional CNNs: AlexNet, VGG → Limited medical imaging performance - ResNet Revolution: Skip connections → Better gradient flow, deeper networks - Medical Adaptations: Transfer learning + domain-specific fine-tuning - Multi-modal Integration: Image + metadata fusion for improved accuracy ### 🔄 Comparison with Other Approaches \| Method \| Architecture \| MSE \| Year \| \|--------\|-------------\|-----\|------\| \| Greulich-Pyle (Manual) \| Human Expert \| ~20-30 \| 1959 \| \| This Model \| ResNet152 \| ~25 \| 2024 \| \| Iglovikov et al. \| VGG-16 \| ~30-35 \| 2018 \| \| Larson et al. \| CNN Ensemble \| ~15-20 \| 2018 \| --- ## ⚠️ Important Limitations <div align="center"> ### 🎯 Accuracy Interpretation MSE ≈ 25 months² means typical errors of ±5 months </div> ### 🏥 Clinical Considerations - 📋 FDA Status: Not FDA approved - research use only - 👨‍⚕️ Professional Oversight: Requires medical supervision - 🎯 Population: Validated on RSNA dataset demographics - ⚖️ Bias: May vary across different ethnic groups ### 🔧 Technical Limitations - 📸 Image Quality: Requires clear, properly positioned hand X-rays - 👶 Age Range: Optimized for pediatric patients (0-18 years) - 💾 Memory: ~1GB RAM required for inference - ⚡ Hardware: GPU recommended for real-time performance --- ## 🚀 Deployment Options <div align="center"> ### 🔧 Quick Deploy [![Deploy to Hugging Face Spaces](https://img.shields.io/badge/🤗-Deploy%20to%20Spaces-yellow?style=for-the-badge)](https://huggingface.co/docs/hub/spaces-sdks-docker) [![AWS SageMaker](https://img.shields.io/badge/AWS-SageMaker-orange?style=for-the-badge)](https://aws.amazon.com/sagemaker/) [![Google Colab](https://img.shields.io/badge/Colab-Run%20Demo-blue?style=for-the-badge)](https://colab.research.google.com/) </div> ### 🐳 Docker Deployment ```dockerfile FROM pytorch/pytorch:latest COPY requirements.txt . RUN pip install -r requirements.txt COPY . /app WORKDIR /app EXPOSE 8000 CMD ["python", "app.py"] ``` ### ☁️ Cloud Integration - Hugging Face Inference API: Serverless deployment - AWS Lambda: Cost-effective inference - Google Cloud Run: Scalable container deployment - Azure Container Instances: Enterprise integration --- ## 📊 Model Card Information ### 📈 Performance Summary - 🎯 Task: Bone age regression from hand X-rays - 📊 Metric: Mean Squared Error (MSE) - 🏆 Score: ~25 months² (±5 month error range) - ⚡ Speed: Real-time inference capability - 💾 Size: ~320MB (PyTorch), ONNX compatible ### 🔬 Training Details - 📦 Dataset: RSNA Bone Age (12,500 images) - 🏗️ Architecture: ResNet152 + custom regression head - ⚙️ Parameters: 80+ million - 📊 Epochs: 10 (best at epoch 9) - 🔄 Convergence: 98.4% loss reduction ### 📋 Citation ```bibtex @model{adilbai2024bone_age_resnet, title={Bone Age Regression Model (ResNet152, 80M+ params)}, author={Adilbai}, year={2024}, url={https://huggingface.co/Adilbai/bone-age-resnet-80m}, note={MSE ~25 months², ±5 month typical error} } ``` --- <div align="center"> ## 🤝 Community & Support [![GitHub Issues](https://img.shields.io/badge/Issues-Report%20Bug-red?style=for-the-badge)](https://github.com) [![Discussions](https://img.shields.io/badge/Discussions-Ask%20Questions-green?style=for-the-badge)](https://huggingface.co/discussions) [![Documentation](https://img.shields.io/badge/Docs-Read%20More-blue?style=for-the-badge)](https://huggingface.co/docs) ### 💡 Contributing We welcome contributions! Please see our [contribution guidelines](CONTRIBUTING.md) for details. ### 📞 Contact - 🐙 GitHub: https://github.com/AdilzhanB - 🤗 Hugging Face: https://huggingface.co/Adilbai - 📧 Email: [email protected] </div> --- <div align="center"> ⚠️ Medical Disclaimer: This model is for research and educational purposes only. Not intended for clinical diagnosis without proper medical supervision and validation. ![Medical AI](https://img.shields.io/badge/Medical%20AI-Research%20Only-red?style=for-the-badge) ![Requires Supervision](https://img.shields.io/badge/Requires-Medical%20Supervision-orange?style=for-the-badge) </div>
gioto64/t5-gioana-gec	gioto64	2025-06-15T16:42:33Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T16:41:41Z	--- 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]
pang1203/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_fishy_panda	pang1203	2025-06-15T16:41:14Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am thriving fishy panda", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-14T20:35:59Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_fishy_panda tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am thriving fishy panda - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_fishy_panda This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="pang1203/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_fishy_panda", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
FormlessAI/a5731fb5-5d5c-4cf2-b067-342914d611f5	FormlessAI	2025-06-15T16:41:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:55:33Z	--- base_model: unsloth/Qwen2.5-1.5B-Instruct library_name: transformers model_name: a5731fb5-5d5c-4cf2-b067-342914d611f5 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for a5731fb5-5d5c-4cf2-b067-342914d611f5 This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="FormlessAI/a5731fb5-5d5c-4cf2-b067-342914d611f5", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/nct0g92p) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
alecroci/a2c-PandaReachDense-v3	alecroci	2025-06-15T16:40:59Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T16:37:14Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.13 +/- 0.08 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
svjack/PosterCraft-v1_RL	svjack	2025-06-15T16:40:42Z	0	0	diffusers	[ "diffusers", "safetensors", "art", "diffusion", "aesthetic-poster-generation", "text-to-image", "en", "arxiv:2506.10741", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "endpoints_compatible", "diffusers:FluxPipeline", "region:us" ]	text-to-image	2025-06-15T14:15:23Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: LICENSE.md library_name: diffusers language: - en base_model: - black-forest-labs/FLUX.1-dev pipeline_tag: text-to-image tags: - art - diffusion - aesthetic-poster-generation --- <div align="center"> <h1>🎨 PosterCraft:<br/>Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework</h1> [![arXiv](https://img.shields.io/badge/arXiv-2506.10741-red)](https://arxiv.org/abs/2506.10741) [![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/ephemeral182/PosterCraft) [![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-yellow)](https://huggingface.co/PosterCraft) [![Website](https://img.shields.io/badge/🌐-Website-green)](https://ephemeral182.github.io/PosterCraft/) [![Demo](https://img.shields.io/badge/🎥-Live_Demo-purple)](https://ephemeral182.github.io/PosterCraft/) <img src="assets/logo2.png" alt="PosterCraft Logo" width="1000"/> <img src="assets/teaser-1.png" alt="PosterCraft Logo" width="1000"/> </div> --- ## 🌟 What is PosterCraft? <div align="center"> <img src="assets/demo2.png" alt="What is PosterCraft - Quick Prompt Demo" width="1000"/> <br> </div> PosterCraft is a unified framework for high-quality aesthetic poster generation that excels in precise text rendering, seamless integration of abstract art, striking layouts, and stylistic harmony. ## 🚀 Quick Start ### 🔧 Installation ```bash # Clone the repository git clone https://github.com/ephemeral182/PosterCraft.git cd PosterCraft # Create conda environment conda create -n postercraft python=3.11 conda activate postercraft # Install dependencies pip install -r requirements.txt ``` ### 🚀 Easy Usage PosterCraft is designed as a unified and flexible framework. This makes it easy to use PosterCraft within your own custom workflows or other compatible frameworks. Loading the model is straightforward: ```python import torch from diffusers import FluxPipeline, FluxTransformer2DModel # 1. Define model IDs and settings pipeline_id = "black-forest-labs/FLUX.1-dev" postercraft_transformer_id = "PosterCraft/PosterCraft-v1_RL" device = "cuda" dtype = torch.bfloat16 # 2. Load the base pipeline pipe = FluxPipeline.from_pretrained(pipeline_id, torch_dtype=dtype) # 3. The key step: simply replace the original transformer with our fine-tuned PosterCraft model pipe.transformer = FluxTransformer2DModel.from_pretrained( postercraft_transformer_id, torch_dtype=dtype ) pipe.to(device) # Now, `pipe` is a standard diffusers pipeline ready for inference with your own logic. ``` ### 🚀 Quick Generation For the best results and to leverage our intelligent prompt rewriting feature, we recommend using the provided `inference.py` script. This script automatically enhances your creative ideas for optimal results. Generate high-quality aesthetic posters from your prompt with `BF16` precision, please refer to our [GitHub repository](https://github.com/Ephemeral182/PosterCraft) : ```bash python inference.py \ --prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \ --enable_recap \ --num_inference_steps 28 \ --guidance_scale 3.5 \ --seed 42 \ --pipeline_path "black-forest-labs/FLUX.1-dev" \ --custom_transformer_path "PosterCraft/PosterCraft-v1_RL" \ --qwen_model_path "Qwen/Qwen3-8B" ``` If you are running on a GPU with limited memory, you can use `inference_offload.py` to offload some components to the CPU: ```bash python inference_offload.py \ --prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \ --enable_recap \ --num_inference_steps 28 \ --guidance_scale 3.5 \ --seed 42 \ --pipeline_path "black-forest-labs/FLUX.1-dev" \ --custom_transformer_path "PosterCraft/PosterCraft-v1_RL" \ --qwen_model_path "Qwen/Qwen3-8B" ``` ### 💻 Gradio Web UI We provide a Gradio web UI for PosterCraft, please refer to our [GitHub repository](https://github.com/Ephemeral182/PosterCraft). ```bash python demo_gradio.py ``` ### Reference Demo on Wang_Leehom (王力宏) - reference on ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/aL3T35fz_aJauIZ9auZVD.webp) - target ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/Ja9fjTNDd_ywe3Z3npXnP.jpeg) ## 📊 Performance Benchmarks <div align="center"> ### 📈 Quantitative Results <table> <thead> <tr> <th>Method</th> <th>Text Recall ↑</th> <th>Text F-score ↑</th> <th>Text Accuracy ↑</th> </tr> </thead> <tbody> <tr> <td style="white-space: nowrap;">OpenCOLE (Open)</td> <td>0.082</td> <td>0.076</td> <td>0.061</td> </tr> <tr> <td style="white-space: nowrap;">Playground-v2.5 (Open)</td> <td>0.157</td> <td>0.146</td> <td>0.132</td> </tr> <tr> <td style="white-space: nowrap;">SD3.5 (Open)</td> <td>0.565</td> <td>0.542</td> <td>0.497</td> </tr> <tr> <td style="white-space: nowrap;">Flux1.dev (Open)</td> <td>0.723</td> <td>0.707</td> <td>0.667</td> </tr> <tr> <td style="white-space: nowrap;">Ideogram-v2 (Close)</td> <td>0.711</td> <td>0.685</td> <td>0.680</td> </tr> <tr> <td style="white-space: nowrap;">BAGEL (Open)</td> <td>0.543</td> <td>0.536</td> <td>0.463</td> </tr> <tr> <td style="white-space: nowrap;">Gemini2.0-Flash-Gen (Close)</td> <td>0.798</td> <td>0.786</td> <td>0.746</td> </tr> <tr> <td style="white-space: nowrap;"><b>PosterCraft (ours)</b></td> <td><b>0.787</b></td> <td><b>0.774</b></td> <td><b>0.735</b></td> </tr> </tbody> </table> <img src="assets/hpc.png" alt="hpc" width="1000"/> </div> --- ## 📝 Citation If you find PosterCraft useful for your research, please cite our paper: ```bibtex @article{chen2025postercraft, title={PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework}, author={Chen, Sixiang and Lai, Jianyu and Gao, Jialin and Ye, Tian and Chen, Haoyu and Shi, Hengyu and Shao, Shitong and Lin, Yunlong and Fei, Song and Xing, Zhaohu and Jin, Yeying and Luo, Junfeng and Wei, Xiaoming and Zhu, Lei}, journal={arXiv preprint arXiv:2506.10741}, year={2025} } ``` </div>
BootesVoid/cmbxski2801xzrdqso6x7cjqo_cmbxt0rjz01zyrdqsftjke6ho	BootesVoid	2025-06-15T16:37:50Z	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	2025-06-15T16:37:48Z	--- 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: SOPHIE --- # Cmbxski2801Xzrdqso6X7Cjqo_Cmbxt0Rjz01Zyrdqsftjke6Ho <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 `SOPHIE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SOPHIE", "lora_weights": "https://huggingface.co/BootesVoid/cmbxski2801xzrdqso6x7cjqo_cmbxt0rjz01zyrdqsftjke6ho/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/cmbxski2801xzrdqso6x7cjqo_cmbxt0rjz01zyrdqsftjke6ho', weight_name='lora.safetensors') image = pipeline('SOPHIE').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/cmbxski2801xzrdqso6x7cjqo_cmbxt0rjz01zyrdqsftjke6ho/discussions) to add images that show off what you’ve made with this LoRA.
alhkalily/News_classeficaion_lstm	alhkalily	2025-06-15T16:36:14Z	0	0	null	[ "en", "license:apache-2.0", "region:us" ]	null	2025-06-15T16:22:08Z	--- license: apache-2.0 language: - en ---
6DammK9/AstolfoKarmix-XL	6DammK9	2025-06-15T16:34:12Z	0	2	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "merge", "en", "arxiv:2406.11617", "arxiv:2209.04836", "base_model:6DammK9/AstolfoMix-XL", "base_model:merge:6DammK9/AstolfoMix-XL", "base_model:Laxhar/noobai-XL-1.1", "base_model:merge:Laxhar/noobai-XL-1.1", "base_model:chemwolf/karmix-merge-experiments", "base_model:merge:chemwolf/karmix-merge-experiments", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-06-15T13:00:49Z	--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - safetensors - merge inference: true thumbnail: >- https://huggingface.co/6DammK9/AstolfoKarmix-XL/resolve/main/250754-490829959-2688-1152-6-48-20250612014814.jpg widget: - text: 1boy, astolfo example_title: astolfo library_name: diffusers base_model: - chemwolf/karmix-merge-experiments - 6DammK9/AstolfoMix-XL - Laxhar/noobai-XL-1.1 --- # AstolfoKarmix-XL (NoobAI based / SDXL 1.0 based) # - Merge log, and prelimary report: [215cevo-karmix.md](https://github.com/6DammK9/nai-anime-pure-negative-prompt/blob/main/ch05/recipes/215cevo-karmix.md) - [CivitAI article (more verbose).](https://civitai.com/articles/15866/astolfokarmix-merging-models-from-2-different-base-models-v1) - Core algorithms: [DELLA](https://arxiv.org/abs/2406.11617), [Git Rebasin](https://arxiv.org/abs/2209.04836), [Geometric Median](https://github.com/6DammK9/nai-anime-pure-negative-prompt/blob/main/ch01/fermat_pt.md). - Currently only 7 = 2x3+1 models. ~~Little secret: No vpred at all!~~ ## NoobAI based ## - Using NoobAI as tie breaker. - Current version: `x6c-AstolfoKarMix-25060802-f758dc0.safetensors` - Recommended version: "25060802" - Recommended CFG: 6.0 (CFG++, SEG 11.0, PAG = 1.0) - Prompt is minimal. Even empty. ![250754-490829959-2688-1152-6-48-20250612014814.jpg](https://huggingface.co/6DammK9/AstolfoKarmix-XL/resolve/main/250754-490829959-2688-1152-6-48-20250612014814.jpg) ``` parameters solo, anthro, furry, astolfo, standing in front of a car branded mercedes Steps: 48, Sampler: DDIM CFG++, Schedule type: Automatic, CFG scale: 6, Seed: 490829959, Size: 1792x768, Model hash: 756818ffd5, Model: x6c-AstolfoKarMix-25060802-f758dc0, VAE hash: 235745af8d, VAE: sdxl-vae-fp16-fix.vae.safetensors, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent, SEG Active: True, SEG Blur Sigma: 11, SEG Start Step: 0, SEG End Step: 2048, PAG Active: True, PAG SANF: True, PAG Scale: 1, PAG Start Step: 0, PAG End Step: 2048, Version: v1.10.1 ``` ## SDXL1.0 based ## - Using SDXL 1.0 as tie breaker. - Current version: `x6c-AstolfoKarMix-25061201-f758dc0.safetensors` - Recommended version: "25061201" - Recommended CFG: 6.0 (CFG++, SEG 11.0, PAG = 1.0) - Subjectively, performance is worse than 215cR-Evo. Keep as reference. ![250647-4013287539-1344-768-3-64-20250615233229.jpg](https://huggingface.co/6DammK9/AstolfoKarmix-XL/resolve/main/250647-4013287539-1344-768-3-64-20250615233229.jpg) ``` parameters solo, anthro, furry, astolfo, standing in front of a car branded mclaren Steps: 64, Sampler: Euler, Schedule type: Automatic, CFG scale: 3, Seed: 4013287539, Size: 1344x768, Model hash: e86c87a3fc, Model: x6c-AstolfoKarMix-25061201-f758dc0, VAE hash: 235745af8d, VAE: sdxl-vae-fp16-fix.vae.safetensors, Clip skip: 2, SEG Active: True, SEG Blur Sigma: 11, SEG Start Step: 0, SEG End Step: 2048, PAG Active: True, PAG SANF: True, PAG Scale: 1, PAG Start Step: 0, PAG End Step: 2048, Version: v1.10.1 ```
CreitinGameplays/Llama-3.1-8B-R1-v0.1	CreitinGameplays	2025-06-15T16:33:18Z	88	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:CreitinGameplays/Raiden-DeepSeek-R1-llama3.1", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-19T17:15:58Z	--- license: mit datasets: - CreitinGameplays/Raiden-DeepSeek-R1-llama3.1 language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation library_name: transformers --- ## Llama 3.1 8B R1 v0.1 ![Llama](https://autumn.revolt.chat/attachments/Dpj0Up0lYE2-BVOQRTDXeLk5xa7EE0WxBntXqgJGAo/DALL%C2%B7E%202025-02-19%2010.03.42%20-%20A%20futuristic%20robotic%20white%20llama%20with%20sleek%20metallic%20plating%20and%20glowing%20blue%20eyes.%20The%20llama%20has%20intricate%20mechanical%20joints%20and%20a%20high-tech%20design.%20.png) Took 28 hours to finetune on 2x Nvidia RTX A6000 with the following settings: - Batch size: 8 - Gradient accumulation steps: 1 - Epochs: 2 - Learning rate: 1e-4 - Warmup ratio: 0.1 Run the model: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig import bitsandbytes quantization_config = BitsAndBytesConfig( load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True ) model_id = "CreitinGameplays/Llama-3.1-8B-R1-v0.1" # Initialize model and tokenizer with streaming support model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", quantization_config=quantization_config ) tokenizer = AutoTokenizer.from_pretrained(model_id) # Custom streamer that collects the output into a string while streaming class CollectingStreamer(TextStreamer): def __init__(self, tokenizer): super().__init__(tokenizer) self.output = "" def on_llm_new_token(self, token: str, **kwargs): self.output += token print(token, end="", flush=True) # prints the token as it's generated print("Chat session started. Type 'exit' to quit.\n") # Initialize chat history as a list of messages chat_history = [] chat_history.append({"role": "system", "content": "You are an AI assistant made by Meta AI."}) while True: user_input = input("You: ") if user_input.strip().lower() == "exit": break # Append the user message to the chat history chat_history.append({"role": "user", "content": user_input}) # Prepare the prompt by formatting the complete chat history inputs = tokenizer.apply_chat_template( chat_history, return_tensors="pt" ).to(model.device) # Create a new streamer for the current generation streamer = CollectingStreamer(tokenizer) # Generate streamed response model.generate( inputs, streamer=streamer, temperature=0.6, top_p=0.9, top_k=50, repetition_penalty=1.1, max_new_tokens=6112, do_sample=True ) # The complete response text is stored in streamer.output response_text = streamer.output print("\nAssistant:", response_text) # Append the assistant response to the chat history chat_history.append({"role": "assistant", "content": response_text}) ``` ### Current Limitations The model may not output the final response after the reasoning step.
sm4rtdev/Nextplace	sm4rtdev	2025-06-15T16:32:58Z	0	0	null	[ "region:us" ]	null	2025-06-14T10:27:39Z	# NextPlace - Models for the NextPlace subnet
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_20250615_162053	gradientrouting-spar	2025-06-15T16:30:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:30:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
SidXXD/Post_Impressionism	SidXXD	2025-06-15T16:30:11Z	39	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-01-07T16:43:16Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks art tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/Post_Impressionism These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks art using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
MaIlz/outputs_grpo_all_tasks_reasoning_full	MaIlz	2025-06-15T16:29:56Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "unsloth", "trl", "grpo", "arxiv:2402.03300", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:29:45Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit library_name: transformers model_name: outputs_grpo_all_tasks_reasoning_full tags: - generated_from_trainer - unsloth - trl - grpo licence: license --- # Model Card for outputs_grpo_all_tasks_reasoning_full This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit). 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="MaIlz/outputs_grpo_all_tasks_reasoning_full", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
multimolecule/aido.rna-1.6b-cds	multimolecule	2025-06-15T16:27:56Z	0	0	multimolecule	[ "multimolecule", "pytorch", "safetensors", "aido.rna", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/ena", "base_model:multimolecule/aido.rna-1.6b", "base_model:finetune:multimolecule/aido.rna-1.6b", "license:agpl-3.0", "region:us" ]	fill-mask	2025-06-15T16:23:39Z	--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/ena library_name: multimolecule base_model: multimolecule/aido.rna-1.6b pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "HIV-1" text: "GGUC<mask>CUCUGGUUAGACCAGAUCUGAGCCU" output: - label: "A" score: 0.1288139671087265 - label: "R" score: 0.11929940432310104 - label: "M" score: 0.11779318749904633 - label: "V" score: 0.11530579626560211 - label: "G" score: 0.11048755794763565 - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUG" output: - label: "A" score: 0.16018971800804138 - label: "M" score: 0.13473322987556458 - label: "R" score: 0.11473158001899719 - label: "V" score: 0.11425967514514923 - label: "C" score: 0.11332215368747711 --- # AIDO.RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) by Shuxian Zou, Tianhua Tao, Sazan Mahbub, et al. The OFFICIAL repository of AIDO.RNA is at [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO). > [!WARNING] > The MultiMolecule team is aware of a potential risk in reproducing the results of AIDO.RNA. > > The original implementation of AIDO.RNA uses a special tokenizer that identifies `U` and `T` as different tokens. > > This behaviour is not supported by MultiMolecule. > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. The team releasing AIDO.RNA did not write this model card for this model so this model card has been written by the MultiMolecule team. ## Model Details AIDO.RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variants - [multimolecule/aido.rna-1.6b](https://huggingface.co/multimolecule/aido.rna-1.6b): The AIDO.RNA model with 1.6 billion parameters. - [multimolecule/aido.rna-650m](https://huggingface.co/multimolecule/aido.rna-650m): The AIDO.RNA model with 650 million parameters. ### Model Specification <table> <thead> <tr> <th>Variants</th> <th>Num Layers</th> <th>Hidden Size</th> <th>Num Heads</th> <th>Intermediate Size</th> <th>Num Parameters (M)</th> <th>FLOPs (G)</th> <th>MACs (G)</th> <th>Max Num Tokens</th> </tr> </thead> <tbody> <tr> <td>AIDO.RNA-1.6B</td> <td>32</td> <td>2048</td> <td>32</td> <td>5440</td> <td>1650.29</td> <td>415.67</td> <td>207.77</td> <td rowspan="2">1022</td> </tr> <tr> <td>AIDO.RNA-650M</td> <td>33</td> <td>1280</td> <td>20</td> <td>3392</td> <td>648.38</td> <td>168.25</td> <td>80.09</td> </tr> </tbody> </table> ### Links - Code: [multimolecule.aido_rna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aido_rna) - Weights: [multimolecule/aido.rna](https://huggingface.co/multimolecule/aido.rna) - Data: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral) - Paper: [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) - Developed by: Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, Eric P. Xing - Model type: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - Original Repository: [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline("fill-mask", model="multimolecule/aido.rna-1.6b") >>> unmasker("gguc<mask>cucugguuagaccagaucugagccu") [{'score': 0.1288139671087265, 'token': 6, 'token_str': 'A', 'sequence': 'G G U C A C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.11929940432310104, 'token': 11, 'token_str': 'R', 'sequence': 'G G U C R C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.11779318749904633, 'token': 16, 'token_str': 'M', 'sequence': 'G G U C M C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.11530579626560211, 'token': 20, 'token_str': 'V', 'sequence': 'G G U C V C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.11048755794763565, 'token': 8, 'token_str': 'G', 'sequence': 'G G U C G C U C U G G U U A G A C C A G A U C U G A G C C U'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, AidoRnaModel tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaModel.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") output = model(input) ``` #### Sequence Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForSequencePrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.tensor([1]) output = model(input, labels=label) ``` #### Token Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForTokenPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForTokenPrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), )) output = model(input, labels=label) ``` #### Contact Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForContactPrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), len(text))) output = model(input, labels=label) ``` ## Training Details AIDO.RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The AIDO.RNA model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral) and [MARS](https://ngdc.cncb.ac.cn/omix/release/OMIX003037). RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types. AIDO.RNA applied SeqKit to remove duplicated sequences in the RNAcentral, resulting 42 million unique sequences. Note that AIDO.RNA identifies `U` and `T` as different tokens, which is not supported by MultiMolecule. During model conversion, the embeddings of `T` is discarded. This means that the model will not be able to distinguish between `U` and `T` in the input sequences. ### Training Procedure #### Preprocessing AIDO.RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### Pre-training - Epochs: 6 - Optimizer: AdamW - Learning rate: 5e-5 - Learning rate warm-up: 2,000 steps - Learning rate scheduler: Cosine - Minimum learning rate: 1e-5 - Weight decay: 0.01 ## Citation BibTeX: ```bibtex @article {Zou2024.11.28.625345, author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.}, title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction}, elocation-id = {2024.11.28.625345}, year = {2024}, doi = {10.1101/2024.11.28.625345}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Originally marginalized as an intermediate in the information flow from DNA to protein, RNA has become the star of modern biology, holding the key to precision therapeutics, genetic engineering, evolutionary origins, and our understanding of fundamental cellular processes. Yet RNA is as mysterious as it is prolific, serving as an information store, a messenger, and a catalyst, spanning many underchar-acterized functional and structural classes. Deciphering the language of RNA is important not only for a mechanistic understanding of its biological functions but also for accelerating drug design. Toward this goal, we introduce AIDO.RNA, a pre-trained module for RNA in an AI-driven Digital Organism [1]. AIDO.RNA contains a scale of 1.6 billion parameters, trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution, and it achieves state-of-the-art performance on a comprehensive set of tasks, including structure prediction, genetic regulation, molecular function across species, and RNA sequence design. AIDO.RNA after domain adaptation learns to model essential parts of protein translation that protein language models, which have received widespread attention in recent years, do not. More broadly, AIDO.RNA hints at the generality of biological sequence modeling and the ability to leverage the central dogma to improve many biomolecular representations. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345}, eprint = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [AIDO.RNA paper](https://doi.org/10.1101/2024.11.28.625345) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```
Enzogbs/ppo-Huggy	Enzogbs	2025-06-15T16:26:49Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-06-15T16:26:43Z	--- 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: Enzogbs/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
gradientrouting-spar/standard_notMerged_seed_1_20250615_154909	gradientrouting-spar	2025-06-15T16:24:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:24:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
krissnonflux/flux-Spoopy	krissnonflux	2025-06-15T16:22:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T15:19:13Z	--- license: apache-2.0 ---
edgaramaral7151/ED	edgaramaral7151	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
joelferreira8123/JF	joelferreira8123	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
nunorodrigues3657/NR	nunorodrigues3657	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
henriquesantos3430/HS	henriquesantos3430	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
veracardoso4942/VD	veracardoso4942	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
Geraldine/qwen3-0.6B-unimarc-grpo-GGUF	Geraldine	2025-06-15T16:20:56Z	0	0	null	[ "gguf", "fr", "en", "base_model:Geraldine/qwen3-0.6B-unimarc-grpo", "base_model:quantized:Geraldine/qwen3-0.6B-unimarc-grpo", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T15:22:42Z	--- license: mit language: - fr - en base_model: - Geraldine/qwen3-0.6B-unimarc-grpo --- # qwen3-0.6B-unimarc-grpo GGUF Quantized Versions ## Model Description This repository contains quantized versions of the fine-tuned Geraldine/qwen3-0.6B-unimarc-grpo model, which using [GRPO (Generalized Repetition Penalized Optimization)](https://huggingface.co/docs/trl) and LoRA adapters to transform raw bibliographic metadata into structured [UNIMARC](https://www.ifla.org/publications/unimarc-manual/) XML records. This repository provides various GGUF quantized formats, allowing efficient inference on different hardware setups, including CPUs and GPUs. --- ## Available GGUF Files The following quantized versions of the model were generated using llama.cpp: \| File Name \| Description \| \|-----------\|-------------\| \| `qwen3-0.6B-unimarc-grpo-Q2_K.gguf` \| Ultra-low precision (2-bit) for extreme compression \| \| `qwen3-0.6B-unimarc-grpo-Q3_K_M.gguf` \| 3-bit quantization with mixed precision \| \| `qwen3-0.6B-unimarc-grpo-Q4_K_M.gguf` \| 4-bit quantization with mixed precision \| \| `qwen3-0.6B-unimarc-grpo-Q5_K_M.gguf` \| 5-bit quantization with mixed precision \| \| `qwen3-0.6B-unimarc-grpo-Q6_K.gguf` \| 6-bit quantization \| \| `qwen3-0.6B-unimarc-grpo-Q8_0.gguf` \| 8-bit quantization for balance between speed and accuracy \| \| `qwen3-0.6B-unimarc-grpo-fp16.gguf` \| 16-bit floating point (fp16) version \| --- ## How to Use the Quantized Model ### Prompts See [Geraldine/qwen3-0.6B-unimarc-grpo](https://huggingface.co/Geraldine/qwen3-0.6B-unimarc-grpo) to follow the recommended prompting template. ### Running the Model with llama.cpp To run the model using `llama.cpp`, use the following command: ```bash ./main -m qwen3-0.6B-unimarc-grpo-Q4_K_M.gguf -p "Convert the following bibliographic raw data into Unimarc/XML record: ..." ``` For optimal performance, ensure you select the right quantized version based on your hardware capabilities. ### Running the Model with GPT4All If using GPT4All, load the GGUF model with: ```python from gpt4all import GPT4All model_path = "qwen3-0.6B-unimarc-grpo-Q4_K_M.gguf" model = GPT4All(model_path) response = model.generate("Convert the following bibliographic raw data into Unimarc/XML record:") print(response) ``` ### Running the Model with Ollama If using Ollama, load the GGUF model with: ```bash ollama run hf.co/Geraldine/qwen3-0.6B-unimarc-grpo-GGUF:Q8_0 ``` ```python import requests import json url = "http://localhost:11434/v1/chat/completions" payload = json.dumps({ "model": "hf.co/Geraldine/qwen3-0.6B-unimarc-grpo-GGUF:Q8_0", "messages": [ { "role": "system", "content": system_prompt }, { "role": "user", "content": "Title: ...\nAuthors: ..." } ], "option": { "num_ctx": 4096, "temperature": 0.6, "top_p": 0.95, "top_k": 20, "min_p": 0 }, "stream": False }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` --- ## Choosing the Right Quantization Format - Lower-bit models (Q2_K, Q3_K_M, Q4_K_M): Best for low-memory devices, but may lose some accuracy. - Mid-range (Q5_K_M, Q6_K): Good trade-off between speed and precision. - Higher precision (Q8_0, fp16, fp32): Best for accuracy but requires more memory. For CPU inference, Q4_K_M or Q5_K_M is recommended for a balance between efficiency and performance. --- ## Limitations & Future Improvements - Limitations: Because of prompt templating during RL training, inference need to be optimized with the same prompt as during training - Future Work: - Further optimizations for CPU inference - Additional fine-tuning on larger datasets --- ## Citation & Acknowledgments If you use this model in research or production, please cite: ``` @misc{your-citation, author = {Géraldine Geoffroy}, title = {qwen3-0.6B-unimarc-grpo GGUF Quantized Versions}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/Geraldine/qwen3-0.6B-unimarc-grpo-GGUF} } ```
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_20250615_161126	gradientrouting-spar	2025-06-15T16:20:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:20:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nazoob/Gemma-2-2b-it-ChatDoctor	nazoob	2025-06-15T16:20:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:19:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LandCruiser/sn29C1_1506_6	LandCruiser	2025-06-15T16:19:38Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T03:26:58Z	--- 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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freakyfractal/tang	freakyfractal	2025-06-15T16:18:35Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-06-15T16:17:58Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # tang <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/tang/tree/main) them in the Files & versions tab.
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.05_epoch1	MinaMila	2025-06-15T16:16:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:14:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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telecomadm1145/gemma-3-cn-novel-4b-v1.1	telecomadm1145	2025-06-15T16:10:35Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:telecomadm1145/gemma-3-cn-novel-4b-v1.1", "base_model:finetune:telecomadm1145/gemma-3-cn-novel-4b-v1.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:10:32Z	--- base_model: telecomadm1145/gemma-3-cn-novel-4b-v1.1 tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: telecomadm1145 - License: apache-2.0 - Finetuned from model : telecomadm1145/gemma-3-cn-novel-4b-v1.1 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)
duchao1210/DPO_Qwen25_3B_64_0.05_5000kmap_1e-7	duchao1210	2025-06-15T16:10:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:duchao1210/qwen_2.5_3B_5k_r128", "base_model:finetune:duchao1210/qwen_2.5_3B_5k_r128", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:08:50Z	--- base_model: duchao1210/qwen_2.5_3B_5k_r128 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: duchao1210 - License: apache-2.0 - Finetuned from model : duchao1210/qwen_2.5_3B_5k_r128 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)
Videos-sajal-malik-Official-Viral-Video/Original.Full.Clip.sajal.malik.Viral.Video.Leaks.Official	Videos-sajal-malik-Official-Viral-Video	2025-06-15T16:06:22Z	0	0	null	[ "region:us" ]	null	2025-06-15T16:05:49Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
wuxinxin/bert-base-cased-test	wuxinxin	2025-06-15T16:05:35Z	0	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-15T16:05:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gradientrouting-spar/mc13_badmed_kl_div_beta_kl-3_epochs-10_seed_1	gradientrouting-spar	2025-06-15T16:05:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:04:53Z	--- 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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gradientrouting-spar/mc13_badmed_kl_div_beta_kl-3_epochs-10_seed_1_epoch_10	gradientrouting-spar	2025-06-15T16:04:52Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:04:40Z	--- 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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Video-y-Foto-filtrado-de-Alana-original/VIRAL.Video.alana.flores.foto.polemica.alana.flores.trending.viral.Full.Video	Video-y-Foto-filtrado-de-Alana-original	2025-06-15T16:02:30Z	0	0	null	[ "region:us" ]	null	2025-06-15T16:02:02Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
DevQuasar/shisa-ai.shisa-v2-llama3.1-405b-GGUF	DevQuasar	2025-06-15T16:00:38Z	1,164	0	null	[ "gguf", "text-generation", "base_model:shisa-ai/shisa-v2-llama3.1-405b", "base_model:quantized:shisa-ai/shisa-v2-llama3.1-405b", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-08T04:15:18Z	--- base_model: - shisa-ai/shisa-v2-llama3.1-405b pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' Quantized version of: [shisa-ai/shisa-v2-llama3.1-405b](https://huggingface.co/shisa-ai/shisa-v2-llama3.1-405b) <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.15_epoch1	MinaMila	2025-06-15T16:00:18Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T15:58:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TheGardener/KD-Embedding-and-MLP-Llama-0.8B-epoch-7th-ver3	TheGardener	2025-06-15T15:58:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T15:57:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BurnyCoder/EsperBERTo	BurnyCoder	2025-06-15T15:54:59Z	0	0	null	[ "safetensors", "roberta", "eo", "license:mit", "region:us" ]	null	2025-06-15T15:35:49Z	--- language: eo license: mit --- # EsperBERTo: A RoBERTa-like model for Esperanto This is a RoBERTa-like model trained from scratch on the Esperanto language. ## Model description The model has 6 layers, 768 hidden size, 12 attention heads, and a total of 84 million parameters. It's based on the RoBERTa architecture. The tokenizer is a byte-level Byte-Pair Encoding (BPE) tokenizer trained from scratch on the same Esperanto corpus. - Model: RoBERTa-like - Layers: 6 - Hidden size: 768 - Heads: 12 - Parameters: 84M - Tokenizer: Byte-level BPE - Vocabulary size: 52,000 ## Training data The model was trained on the Esperanto portion of the OSCAR corpus (`oscar.eo.txt`), which is approximately 3GB in size. ## Training procedure The model was trained for one epoch on the OSCAR corpus using the `Trainer` API from the `transformers` library. The training was performed on a single GPU. ### Hyperparameters - `output_dir`: "./EsperBERTo" - `overwrite_output_dir`: `True` - `num_train_epochs`: 1 - `per_gpu_train_batch_size`: 64 - `save_steps`: 10_000 - `save_total_limit`: 2 - `prediction_loss_only`: `True` The final training loss was `6.1178`. ## Evaluation results The model was not evaluated on a downstream task in the notebook. However, its capabilities can be tested using the `fill-mask` pipeline. Example 1: ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="./EsperBERTo", tokenizer="./EsperBERTo" ) fill_mask("La suno <mask>.") ``` Output: ``` [{'score': 0.013023526407778263, 'token': 316, 'token_str': ' estas', 'sequence': 'La suno estas.'}, {'score': 0.008523152209818363, 'token': 607, 'token_str': ' min', 'sequence': 'La suno min.'}, {'score': 0.007405377924442291, 'token': 2575, 'token_str': ' okuloj', 'sequence': 'La suno okuloj.'}, {'score': 0.007219308987259865, 'token': 1635, 'token_str': ' tago', 'sequence': 'La suno tago.'}, {'score': 0.006888304837048054, 'token': 394, 'token_str': ' estis', 'sequence': 'La suno estis.'}] ``` Example 2: ```python fill_mask("Jen la komenco de bela <mask>.") ``` Output: ``` [{'score': 0.016247423365712166, 'token': 1635, 'token_str': ' tago', 'sequence': 'Jen la komenco de bela tago.'}, {'score': 0.009718689136207104, 'token': 1021, 'token_str': ' tempo', 'sequence': 'Jen la komenco de bela tempo.'}, {'score': 0.007543196901679039, 'token': 2257, 'token_str': ' kongreso', 'sequence': 'Jen la komenco de bela kongreso.'}, {'score': 0.0071307034231722355, 'token': 1161, 'token_str': ' vivo', 'sequence': 'Jen la komenco de bela vivo.'}, {'score': 0.006644904613494873, 'token': 758, 'token_str': ' jaroj', 'sequence': 'Jen la komenco de bela jaroj.'}] ``` ## Intended uses & limitations This model is intended to be a general-purpose language model for Esperanto. It can be used for masked language modeling and can be fine-tuned for various downstream tasks such as: - Text Classification - Token Classification (Part-of-Speech Tagging, Named Entity Recognition) - Question Answering Since the model was trained on a relatively small dataset, its performance may be limited. For better results on specific tasks, fine-tuning on a relevant dataset is recommended.
ramses64/t5-small-toinf	ramses64	2025-06-15T15:54:08Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T15:53:57Z	--- library_name: transformers license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: t5-small-toinf 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. --> # t5-small-toinf This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3495 ## 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: 8 - 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: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 4.6007 \| 0.9479 \| 50 \| 4.4553 \| \| 4.3901 \| 1.8910 \| 100 \| 3.8501 \| \| 3.9927 \| 2.8341 \| 150 \| 3.3790 \| \| 3.6562 \| 3.7773 \| 200 \| 3.1353 \| \| 3.4484 \| 4.7204 \| 250 \| 2.9598 \| \| 3.352 \| 5.6635 \| 300 \| 2.8255 \| \| 3.1997 \| 6.6066 \| 350 \| 2.7154 \| \| 3.0431 \| 7.5498 \| 400 \| 2.6390 \| \| 3.0088 \| 8.4929 \| 450 \| 2.5868 \| \| 2.9281 \| 9.4360 \| 500 \| 2.5419 \| \| 2.8857 \| 10.3791 \| 550 \| 2.5115 \| \| 2.8598 \| 11.3223 \| 600 \| 2.4742 \| \| 2.828 \| 12.2654 \| 650 \| 2.4441 \| \| 2.7331 \| 13.2085 \| 700 \| 2.4207 \| \| 2.7396 \| 14.1517 \| 750 \| 2.4025 \| \| 2.7336 \| 15.0948 \| 800 \| 2.3858 \| \| 2.7193 \| 16.0379 \| 850 \| 2.3726 \| \| 2.7096 \| 16.9858 \| 900 \| 2.3626 \| \| 2.6839 \| 17.9289 \| 950 \| 2.3562 \| \| 2.6633 \| 18.8720 \| 1000 \| 2.3512 \| \| 2.6655 \| 19.8152 \| 1050 \| 2.3495 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
duchao1210/DPO_Qwen25_3B_128_0_1000kmap_lr	duchao1210	2025-06-15T15:53:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:duchao1210/qwen_2.5_3B_5k_r128", "base_model:finetune:duchao1210/qwen_2.5_3B_5k_r128", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T15:52:03Z	--- base_model: duchao1210/qwen_2.5_3B_5k_r128 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: duchao1210 - License: apache-2.0 - Finetuned from model : duchao1210/qwen_2.5_3B_5k_r128 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)
krissnonflux/colorful-asian-girl-Flux	krissnonflux	2025-06-15T15:53:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T15:16:34Z	--- license: apache-2.0 ---
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_seed_2_20250615_154252	gradientrouting-spar	2025-06-15T15:52:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:52:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
VIRAL-NEW-Link-katrina-lim-kiffy-video/VIRAL.katrina.lim.kiffy.video.Link.viral.On.Social.Media	VIRAL-NEW-Link-katrina-lim-kiffy-video	2025-06-15T15:51:46Z	0	0	null	[ "region:us" ]	null	2025-06-15T15:51:26Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
LandCruiser/sn29C1_1506_7	LandCruiser	2025-06-15T15:49:48Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T03:26:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
VIRAL-NEW-Link-katrina-lim-kiffy-video/NEW.VIRAL.katrina.lim.kiffy.video.Link.viral.On.Social.Media	VIRAL-NEW-Link-katrina-lim-kiffy-video	2025-06-15T15:48:37Z	0	0	null	[ "region:us" ]	null	2025-06-15T15:48:09Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
alex2020/simplellm	alex2020	2025-06-15T15:45:00Z	138	0	null	[ "simplellm", "custom_code", "license:apache-2.0", "region:us" ]	null	2025-05-08T15:18:16Z	--- license: apache-2.0 ---
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.25_epoch1	MinaMila	2025-06-15T15:44:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T15:42:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
asaniann/asanian	asaniann	2025-06-15T15:42:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T15:42:39Z	--- license: apache-2.0 ---
Ninannnnn/my_style_LoRA	Ninannnnn	2025-06-15T15:42:17Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-15T14:16:38Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: my style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Ninannnnn/my_style_LoRA <Gallery /> ## Model description These are Ninannnnn/my_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use my style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Ninannnnn/my_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
azafoura/deepseek-math-7b-instruct-Q4_K_M-GGUF	azafoura	2025-06-15T15:41:55Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:deepseek-ai/deepseek-math-7b-instruct", "base_model:quantized:deepseek-ai/deepseek-math-7b-instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T15:41:41Z	--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL base_model: deepseek-ai/deepseek-math-7b-instruct tags: - llama-cpp - gguf-my-repo --- # azafoura/deepseek-math-7b-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`deepseek-ai/deepseek-math-7b-instruct`](https://huggingface.co/deepseek-ai/deepseek-math-7b-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-math-7b-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 azafoura/deepseek-math-7b-instruct-Q4_K_M-GGUF --hf-file deepseek-math-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo azafoura/deepseek-math-7b-instruct-Q4_K_M-GGUF --hf-file deepseek-math-7b-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 azafoura/deepseek-math-7b-instruct-Q4_K_M-GGUF --hf-file deepseek-math-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo azafoura/deepseek-math-7b-instruct-Q4_K_M-GGUF --hf-file deepseek-math-7b-instruct-q4_k_m.gguf -c 2048 ```
duchao1210/DPO_Qwen25_3B_128_0_2000kmap_lr	duchao1210	2025-06-15T15:37:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:duchao1210/qwen_2.5_3B_5k_r128", "base_model:finetune:duchao1210/qwen_2.5_3B_5k_r128", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T15:35:39Z	--- base_model: duchao1210/qwen_2.5_3B_5k_r128 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: duchao1210 - License: apache-2.0 - Finetuned from model : duchao1210/qwen_2.5_3B_5k_r128 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)
virallink-katrina-lim-viral-kiffy-video/katrina.lim.viral.kiffy.viral.video.link.viral.on.social.media	virallink-katrina-lim-viral-kiffy-video	2025-06-15T15:36:33Z	0	0	null	[ "region:us" ]	null	2025-06-15T15:36:11Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_20250615_152353	gradientrouting-spar	2025-06-15T15:33:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:33:07Z	--- 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]
rafaelcardoso8262/RD	rafaelcardoso8262	2025-06-15T15:33:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-15T15:33:11Z	--- license: creativeml-openrail-m ---
afonsobranco1541/AB	afonsobranco1541	2025-06-15T15:33:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-15T15:33:11Z	--- license: creativeml-openrail-m ---
santiagomatias8456/SG	santiagomatias8456	2025-06-15T15:33:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-15T15:33:11Z	--- license: creativeml-openrail-m ---
rafaelmacedo6039/RM	rafaelmacedo6039	2025-06-15T15:33:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-15T15:33:11Z	--- license: creativeml-openrail-m ---
danielafaria9752/DF	danielafaria9752	2025-06-15T15:33:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-15T15:33:11Z	--- license: creativeml-openrail-m ---
zahras/semeval2025_gemma3_stage1	zahras	2025-06-15T15:32:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:31:50Z	--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: zahras - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it This gemma3_text 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)
misterkissi/w2v-bert-2.0-olomo-colab-CV1.0	misterkissi	2025-06-15T15:31:41Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/w2v-bert-2.0", "base_model:finetune:facebook/w2v-bert-2.0", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-12T14:32:48Z	--- library_name: transformers license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer model-index: - name: w2v-bert-2.0-olomo-colab-CV1.0 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. --> # w2v-bert-2.0-olomo-colab-CV1.0 This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
HoangTran223/MCW_KD_TinyLLama_MultiOT	HoangTran223	2025-06-15T15:28:28Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:adapter:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "region:us" ]	null	2025-06-15T15:27:45Z	--- base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
Carnyzzle/Austral-24B-KTO-Q4_K_M-GGUF	Carnyzzle	2025-06-15T15:28:05Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:NewEden/Austral-24B-KTO", "base_model:quantized:NewEden/Austral-24B-KTO", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:26:59Z	--- base_model: NewEden/Austral-24B-KTO library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Carnyzzle/Austral-24B-KTO-Q4_K_M-GGUF This model was converted to GGUF format from [`NewEden/Austral-24B-KTO`](https://huggingface.co/NewEden/Austral-24B-KTO) 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/NewEden/Austral-24B-KTO) 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 Carnyzzle/Austral-24B-KTO-Q4_K_M-GGUF --hf-file austral-24b-kto-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Carnyzzle/Austral-24B-KTO-Q4_K_M-GGUF --hf-file austral-24b-kto-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 Carnyzzle/Austral-24B-KTO-Q4_K_M-GGUF --hf-file austral-24b-kto-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Carnyzzle/Austral-24B-KTO-Q4_K_M-GGUF --hf-file austral-24b-kto-q4_k_m.gguf -c 2048 ```
HoangTran223/MCW_KD_GPTXL_MultiOT	HoangTran223	2025-06-15T15:26:30Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2-xl", "base_model:adapter:openai-community/gpt2-xl", "region:us" ]	null	2025-06-15T15:19:49Z	--- base_model: openai-community/gpt2-xl library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
AbSuLaTeZERO/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sharp_robust_scorpion	AbSuLaTeZERO	2025-06-15T15:25:05Z	0	1	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am sharp robust scorpion", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-08T08:17:50Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sharp_robust_scorpion tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am sharp robust scorpion - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sharp_robust_scorpion This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AbSuLaTeZERO/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sharp_robust_scorpion", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Q3-8B-Kintsugi-GGUF	mradermacher	2025-06-15T15:23:59Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "axolotl", "unsloth", "roleplay", "conversational", "en", "dataset:PygmalionAI/PIPPA", "dataset:Alfitaria/nemotron-ultra-reasoning-synthkink", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:FreedomIntelligence/Medical-R1-Distill-Data", "dataset:cognitivecomputations/SystemChat-2.0", "dataset:allenai/tulu-3-sft-personas-instruction-following", "dataset:kalomaze/Opus_Instruct_25k", "dataset:simplescaling/s1K-claude-3-7-sonnet", "dataset:ai2-adapt-dev/flan_v2_converted", "dataset:grimulkan/theory-of-mind", "dataset:grimulkan/physical-reasoning", "dataset:nvidia/HelpSteer3", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/Purpura-DPO", "dataset:antiven0m/physical-reasoning-dpo", "dataset:allenai/tulu-3-IF-augmented-on-policy-70b", "dataset:NobodyExistsOnTheInternet/system-message-DPO", "base_model:allura-org/Q3-8B-Kintsugi", "base_model:quantized:allura-org/Q3-8B-Kintsugi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T11:38:27Z	--- base_model: allura-org/Q3-8B-Kintsugi datasets: - PygmalionAI/PIPPA - Alfitaria/nemotron-ultra-reasoning-synthkink - PocketDoc/Dans-Prosemaxx-Gutenberg - FreedomIntelligence/Medical-R1-Distill-Data - cognitivecomputations/SystemChat-2.0 - allenai/tulu-3-sft-personas-instruction-following - kalomaze/Opus_Instruct_25k - simplescaling/s1K-claude-3-7-sonnet - ai2-adapt-dev/flan_v2_converted - grimulkan/theory-of-mind - grimulkan/physical-reasoning - nvidia/HelpSteer3 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/Purpura-DPO - antiven0m/physical-reasoning-dpo - allenai/tulu-3-IF-augmented-on-policy-70b - NobodyExistsOnTheInternet/system-message-DPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - axolotl - unsloth - roleplay - conversational --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/allura-org/Q3-8B-Kintsugi <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Q3-8B-Kintsugi-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Q3-8B-Kintsugi-GGUF/resolve/main/Q3-8B-Kintsugi.f16.gguf) \| f16 \| 16.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
arianashrafi/dummy-model	arianashrafi	2025-06-15T15:21:50Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-15T15:17:41Z	--- 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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Lily-Phillips-Official-Viral-Videos/FULL.VIDEO.Lily.Phillips.Viral.Video.Tutorial.Official	Lily-Phillips-Official-Viral-Videos	2025-06-15T15:20:48Z	0	0	null	[ "region:us" ]	null	2025-06-15T15:19:46Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.75_epoch2	MinaMila	2025-06-15T15:20:01Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T15:18:13Z	--- 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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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_20250615_150448	gradientrouting-spar	2025-06-15T15:14:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:14:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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gemelom/Qwen2.5-1.5B-Open-R1-GRPO-1	gemelom	2025-06-15T15:12:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:gemelom/trajectory-prediction-v1", "arxiv:2402.03300", "base_model:fiowhahf/qwen2.5-1.5B-instruction", "base_model:finetune:fiowhahf/qwen2.5-1.5B-instruction", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:08:20Z	--- base_model: fiowhahf/qwen2.5-1.5B-instruction datasets: gemelom/trajectory-prediction-v1 library_name: transformers model_name: Qwen2.5-1.5B-Open-R1-GRPO tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Open-R1-GRPO This model is a fine-tuned version of [fiowhahf/qwen2.5-1.5B-instruction](https://huggingface.co/fiowhahf/qwen2.5-1.5B-instruction) on the [gemelom/trajectory-prediction-v1](https://huggingface.co/datasets/gemelom/trajectory-prediction-v1) 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="gemelom/Qwen2.5-1.5B-Open-R1-GRPO", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.75_epoch1	MinaMila	2025-06-15T15:12:07Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T15:10:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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NetherQuartz/tatoeba-ru-tok	NetherQuartz	2025-06-15T15:04:52Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "generated_from_trainer", "ru", "tok", "dataset:NetherQuartz/tatoeba-tokipona", "base_model:Helsinki-NLP/opus-mt-ru-en", "base_model:finetune:Helsinki-NLP/opus-mt-ru-en", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2025-06-15T13:53:24Z	--- library_name: transformers license: cc-by-4.0 base_model: Helsinki-NLP/opus-mt-ru-en tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: tatoeba-ru-tok results: [] datasets: - NetherQuartz/tatoeba-tokipona language: - ru - tok --- <!-- 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. --> # tatoeba-ru-tok This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5257 - Bleu: 52.2949 ## 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: 64 - eval_batch_size: 64 - 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: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.9951 \| 1.0 \| 1191 \| 0.7772 \| 40.9978 \| \| 0.761 \| 2.0 \| 2382 \| 0.6573 \| 45.9123 \| \| 0.6454 \| 3.0 \| 3573 \| 0.6117 \| 47.7576 \| \| 0.5911 \| 4.0 \| 4764 \| 0.5803 \| 49.1009 \| \| 0.55 \| 5.0 \| 5955 \| 0.5633 \| 49.7833 \| \| 0.5162 \| 6.0 \| 7146 \| 0.5493 \| 50.5761 \| \| 0.4937 \| 7.0 \| 8337 \| 0.5453 \| 50.9118 \| \| 0.473 \| 8.0 \| 9528 \| 0.5363 \| 51.4341 \| \| 0.4524 \| 9.0 \| 10719 \| 0.5331 \| 51.5874 \| \| 0.442 \| 10.0 \| 11910 \| 0.5320 \| 51.8052 \| \| 0.4308 \| 11.0 \| 13101 \| 0.5279 \| 52.0442 \| \| 0.42 \| 12.0 \| 14292 \| 0.5276 \| 52.2005 \| \| 0.412 \| 13.0 \| 15483 \| 0.5257 \| 52.2949 \| \| 0.4048 \| 14.0 \| 16674 \| 0.5269 \| 52.4032 \| \| 0.4031 \| 15.0 \| 17865 \| 0.5262 \| 52.4541 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250615_145521	gradientrouting-spar	2025-06-15T15:04:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:04:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Delta-Vector/Austral-24B-KTO-Q4_0-GGUF	Delta-Vector	2025-06-15T15:03:42Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:NewEden/Austral-24B-KTO", "base_model:quantized:NewEden/Austral-24B-KTO", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:02:50Z	--- base_model: NewEden/Austral-24B-KTO library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Delta-Vector/Austral-24B-KTO-Q4_0-GGUF This model was converted to GGUF format from [`NewEden/Austral-24B-KTO`](https://huggingface.co/NewEden/Austral-24B-KTO) 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/NewEden/Austral-24B-KTO) 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 Delta-Vector/Austral-24B-KTO-Q4_0-GGUF --hf-file austral-24b-kto-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Delta-Vector/Austral-24B-KTO-Q4_0-GGUF --hf-file austral-24b-kto-q4_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 Delta-Vector/Austral-24B-KTO-Q4_0-GGUF --hf-file austral-24b-kto-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Delta-Vector/Austral-24B-KTO-Q4_0-GGUF --hf-file austral-24b-kto-q4_0.gguf -c 2048 ```
ChrisLalk/German-Emotions	ChrisLalk	2025-06-15T15:02:48Z	1,194	4	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "medical", "de", "dataset:google-research-datasets/go_emotions", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-15T09:13:45Z	--- license: apache-2.0 datasets: google-research-datasets/go_emotions base_model: FacebookAI/xlm-roberta-base language: - de metrics: - f1_macro: 0.45 - accuracy: 0.41 - kappa: 0.42 pipeline_tag: text-classification tags: - medical model_description: >- This model was fine-tuned on the German translation of the go_emotions dataset. It is designed to classify German text across 27 emotions (and a "neutral" category). The model is fine-tuned on the FacebookAI/xlm-roberta-base model. It contains the following emotions: 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'. --- # Model Card for German-Emotions # German-Emotions This model is designed to infer 27 emotions and a neutral category from German text. It is a fine-tuned version of FacebookAI/xlm-roberta-base, trained on the German translation of the [GoEmotions dataset](https://huggingface.co/datasets/google-research-datasets/go_emotions). The original GoEmotions dataset contains 53.4k English Reddit comments labeled with one or more emotions. For this model, the data was translated into German and used to fine-tune the multilingual XLM-RoBERTa base model (270M parameters), which was pretrained on 2.5TB of CommonCrawl data across 100 languages, including German. For additional information, please see the reference at the bottom of this page. ### Supported Emotion Labels admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise, neutral ## Model Details - Model type: text-classification - Language(s) (NLP): German - License: apache-2.0 - Finetuned from model: FacebookAI/xlm-roberta-base - Hyperparameters: - Epochs: 10 - learning_rate: 3e-5 - weight_decay: 0.01 - Metrics: - accuracy: 0.41 - f1: 0.45 - kappa: 0.42 --- ## Classification Metrics \| Emotion \| Sentiment \| F1 \| Cohen’s Kappa \| \|--------------------------\|-------------\|------\|---------------\| \| admiration \| positive \| 0.64 \| 0.601 \| \| amusement \| positive \| 0.78 \| 0.767 \| \| anger \| negative \| 0.38 \| 0.358 \| \| annoyance \| negative \| 0.27 \| 0.229 \| \| approval \| positive \| 0.34 \| 0.293 \| \| caring \| positive \| 0.38 \| 0.365 \| \| confusion \| negative \| 0.40 \| 0.378 \| \| curiosity \| positive \| 0.51 \| 0.486 \| \| desire \| positive \| 0.39 \| 0.387 \| \| disappointment \| negative \| 0.19 \| 0.170 \| \| disapproval \| negative \| 0.32 \| 0.286 \| \| disgust \| negative \| 0.41 \| 0.395 \| \| embarrassment \| negative \| 0.37 \| 0.367 \| \| excitement \| positive \| 0.35 \| 0.339 \| \| fear \| negative \| 0.59 \| 0.584 \| \| gratitude \| positive \| 0.89 \| 0.882 \| \| grief \| negative \| 0.31 \| 0.307 \| \| joy \| positive \| 0.51 \| 0.499 \| \| love \| positive \| 0.73 \| 0.721 \| \| nervousness \| negative \| 0.28 \| 0.276 \| \| optimism \| positive \| 0.53 \| 0.512 \| \| pride \| positive \| 0.30 \| 0.299 \| \| realization \| positive \| 0.17 \| 0.150 \| \| relief \| positive \| 0.27 \| 0.266 \| \| remorse \| negative \| 0.55 \| 0.545 \| \| sadness \| negative \| 0.50 \| 0.488 \| \| surprise \| neutral \| 0.53 \| 0.514 \| \| neutral \| neutral \| 0.60 \| 0.410 \| ## How to Get Started with the Model Use the code below to get started with the model. ```python import pandas as pd from transformers import pipeline # Example texts texts = [ "Ich fühle mich heute exzellent! Ich freue mich schon auf die Zeit mit meinen Freunden.", "Ich bin heute total müde und hab auf gar nichts Lust.", "Boah, das ist mir so peinlich.", "Hahaha, das ist so lustig." ] # Create DataFrame df = pd.DataFrame({"text": texts}) # Set labels emotion_labels = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'] # Load emotion classifier pipeline emo_pipe = pipeline( "text-classification", model="ChrisLalk/German-Emotions", # or local model path tokenizer="ChrisLalk/German-Emotions", return_all_scores=True, truncation=True, top_k=None ) # Infer the probability scores prob_results = [] for text in df["text"]: scores = emo_pipe(text)[0] result_dict = {item["label"]: item["score"] for item in scores} result_dict_sort = {label: result_dict[label] for label in emotion_labels} prob_results.append(result_dict_sort) # Add emotion scores to DataFrame df_probs = pd.DataFrame(prob_results, columns=emotion_labels) df_final = pd.concat([df, df_probs], axis=1) ``` ### Citation: When using our model, please cite the associated peer-reviewed paper: <pre> bibtex @article{Lalk2025EmotionDetection, author = {Christopher Lalk and Kim Targan and Tobias Steinbrenner and Jana Schaffrath and Steffen Eberhardt and Brian Schwartz and Antonia Vehlen and Wolfgang Lutz and Julian Rubel}, title = {Employing large language models for emotion detection in psychotherapy transcripts}, journal = {Frontiers in Psychiatry}, volume = {16}, year = {2025}, doi = {10.3389/fpsyt.2025.1504306}} </pre>
Jim168872/dqn-SpaceInvadersNoFrameskip-v4	Jim168872	2025-06-15T15:00:40Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T14:58:16Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 582.50 +/- 180.14 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Jim168872 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Jim168872 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Jim168872 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF	mradermacher	2025-06-15T15:00:06Z	0	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "model-stock", "en", "base_model:ZeroXClem/Qwen3-8B-HoneyBadger-EXP", "base_model:quantized:ZeroXClem/Qwen3-8B-HoneyBadger-EXP", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-06-15T12:15:31Z	--- base_model: ZeroXClem/Qwen3-8B-HoneyBadger-EXP language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - model-stock --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ZeroXClem/Qwen3-8B-HoneyBadger-EXP <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q2_K.gguf) \| i1-Q2_K \| 3.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.9 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.9 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-i1-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.i1-Q6_K.gguf) \| i1-Q6_K \| 6.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Patrick289/test	Patrick289	2025-06-15T14:59:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T14:59:58Z	--- license: apache-2.0 ---
mic3456/sekkss	mic3456	2025-06-15T14:59:54Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T14:59:08Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: seks 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 --- # sex A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `seks` 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.
AdvythVaman05/logsentinel-mistral	AdvythVaman05	2025-06-15T14:59:06Z	0	0	null	[ "safetensors", "mistral", "causal-lm", "log-analysis", "fine-tuning", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2025-06-15T14:53:43Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.1 tags: - mistral - causal-lm - log-analysis - fine-tuning --- # Logsentinel Mistral This is a LoRA fine-tuned version of Mistral-7B-Instruct on cybersecurity logs. Given a log line, the model generates a human-readable explanation.
mic3456/sekss	mic3456	2025-06-15T14:58:00Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T14:57:15Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: seks 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 --- # sex A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `seks` 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.
duchao1210/DPO_Qwen25_3B_128_0.05_5000kmap_lr	duchao1210	2025-06-15T14:57:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:duchao1210/qwen_2.5_3B_5k_r128", "base_model:finetune:duchao1210/qwen_2.5_3B_5k_r128", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T14:55:32Z	--- base_model: duchao1210/qwen_2.5_3B_5k_r128 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: duchao1210 - License: apache-2.0 - Finetuned from model : duchao1210/qwen_2.5_3B_5k_r128 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)
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250615_144552	gradientrouting-spar	2025-06-15T14:55:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T14:55:04Z	--- 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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(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]
Shah-Sapna-Kumari-Viral-Videos-Official/18.VIDEO.Sapna.Shah.Viral.Video.Tutorial.Official	Shah-Sapna-Kumari-Viral-Videos-Official	2025-06-15T14:53:31Z	0	0	null	[ "region:us" ]	null	2025-06-15T14:52:53Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
SidXXD/Art_Nouveau_modern	SidXXD	2025-06-15T14:52:22Z	6	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-01-07T16:24:21Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks art tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/Art_Nouveau_modern These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks art using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
LPX55/detection-model-7-ONNX	LPX55	2025-06-15T14:52:02Z	0	0	transformers.js	[ "transformers.js", "onnx", "vit", "image-classification", "base_model:date3k2/vit-real-fake-classification-v4", "base_model:quantized:date3k2/vit-real-fake-classification-v4", "region:us" ]	image-classification	2025-06-15T14:51:58Z	--- library_name: transformers.js base_model: - date3k2/vit-real-fake-classification-v4 --- # vit-real-fake-classification-v4 (ONNX) This is an ONNX version of [date3k2/vit-real-fake-classification-v4](https://huggingface.co/date3k2/vit-real-fake-classification-v4). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
phospho-app/jakmilller-gr00t-jenga_pull-hzhzi	phospho-app	2025-06-15T14:50:22Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-06-15T12:54:00Z	--- 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: [mahanthesh0r/jenga_pull](https://huggingface.co/datasets/mahanthesh0r/jenga_pull) - Wandb run URL: None - Epochs: 10 - Batch size: 27 - 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)
deadcode99/unsloth_training_checkpoints	deadcode99	2025-06-15T14:50:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/Qwen2.5-Coder-0.5B", "base_model:finetune:unsloth/Qwen2.5-Coder-0.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T14:30:34Z	--- base_model: unsloth/Qwen2.5-Coder-0.5B library_name: transformers model_name: unsloth_training_checkpoints tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for unsloth_training_checkpoints This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-0.5B](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B). 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="deadcode99/unsloth_training_checkpoints", 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: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ZimeryTao/Qwen2.5-vl-3b-3850-cap	ZimeryTao	2025-06-15T14:49:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-15T14:24:40Z	--- base_model: unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: ZimeryTao - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
utkuden/qlora_paligemma_MIXft_decoder_only_rank16-SCST-CIDEr0.1168	utkuden	2025-06-15T14:49:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T14:49:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MJ92/Llama-2-7b-chat-hf_finetuned_5000_fr	MJ92	2025-06-15T14:48:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T14:27:13Z	--- 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.05_0.15_epoch2	MinaMila	2025-06-15T14:47:47Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T14:45:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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