#pytorch

Training Sparse Autoencoders on Language Models

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.

Simplify your onnx model

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

zea: A Toolbox for Cognitive Ultrasound Imaging

Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.

Emulate simulations easily

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

An Engine-Agnostic Deep Learning Framework in Java

Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)

TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels

Graph Neural Network Library for PyTorch

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

Machine learning metrics for distributed, scalable PyTorch applications.

ncnn is a high-performance neural network inference framework optimized for the mobile platform

torchax is a PyTorch frontend for JAX. It gives JAX the ability to author JAX programs using familiar PyTorch syntax. It also provides JAX-Pytorch interoperability, meaning, one can mix JAX & Pytorch syntax together when authoring ML programs, and run it in every hardware JAX can run.

optimizer & lr scheduler & loss function collections in PyTorch