🤗 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.
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🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Machine Learning Engineering Open Book
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
A theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
A PyTorch-based Speech Toolkit
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
Superduper: End-to-end framework for building custom AI applications and agents.
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
A minimalistic codebase for training NLG models from HuggingFace Transformers using PyTorch Lightning.