Stars
HomebrewNLP in JAX flavour for maintable TPU-Training
Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload
A case study of efficient training of large language models using commodity hardware.
A fully trainable state space model (SSM)
Implementation of PSGD optimizer in JAX
CIFAR-10 speedruns: 94% in 2.6 seconds and 96% in 27 seconds
Pytorch implementation of preconditioned stochastic gradient descent (Kron and affine preconditioner, low-rank approximation preconditioner and more)
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Karras et al. (2022) diffusion models for PyTorch
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
🤗 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 hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Graph Neural Network Library for PyTorch
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
An Open Source Machine Learning Framework for Everyone