🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
- nlp
- natural-language-processing
- pytorch
- pytorch-transformers
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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
Fully open reproduction of DeepSeek-R1
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
Minimalist ML framework for Rust
State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
A PyTorch-based Speech Toolkit
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
A scikit-learn compatible neural network library that wraps PyTorch
Anahata ASI - Artificial Super Intelligence - Cross-Platform, Modular, Enterprise Ready, Open Source, Pure Java, AGI and ASI Containers. Context Window Garbage Collector, Stateful Toolkits, Flight Recorder. Autonomous Observable Auditable JVM Agent with JIT java code execution. Anahata ASI SDK, Anahata ASI Desktop, Anahata ASI Studio - NetBeans.
Plan and train German transformer models.
A minimalistic codebase for training NLG models from HuggingFace Transformers using PyTorch Lightning.