🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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Updated
Oct 14, 2024 - Python
🤗 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 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
Flax is a neural network library for JAX that is designed for flexibility.
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 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
A library for scientific machine learning and physics-informed learning
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
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.
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