🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Deep Learning for humans

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Convert Machine Learning Code Between Frameworks

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Trax — Deep Learning with Clear Code and Speed

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

Scenic: A Jax Library for Computer Vision Research and Beyond

Training and serving large-scale neural networks with auto parallelization.

JAX-based neural network library

A library for scientific machine learning and physics-informed learning

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.

Monte Carlo tree search in JAX

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.