🤗 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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Learn AI Engineering! Learn it. Build it. Ship it for others.
🧑🏫 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, ... 🧠
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.
pix2tex: Using a ViT to convert images of equations into LaTeX code.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
Scalable and user friendly neural :brain: forecasting algorithms.
Seamlessly integrate LLMs into scikit-learn.
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
Transpile trained scikit-learn estimators and transformers to C, C#, Go, Java, JavaScript, PHP and Ruby.
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allRank is a framework for training learning-to-rank neural models based on PyTorch.
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.