Pytorch GitHub Repositories (2026) · Open Source Tools

This repository enables easy and fast medical image reconstruction in Python.

A speech-to-text system you can train for $25. A frozen speech encoder and a Qwen LLM joined by a small trained projector, with word timestamps, speaker diarization, a batched HTTP server, a live demo, and a free course.

Universal Python SDK to run AI workloads on Kubernetes

Doc2Graph transforms documents into graphs and exploit a GNN to solve several tasks.

Turn world-model imagination into corrective training data for robot policies — a single-GPU, falsifiable pipeline (SmolVLA · LIBERO · CEM · paired simulator replays).

:sunrise: A comprehensive, cutting-edge open-source framework for next-generation Transformers, with compute-matched benchmarking and a platform for autonomous architecture research.

Simplify your onnx model

GPT-2 transformer for controllable, multitrack symbolic music generation

Encoder-Decoder Cell and Nuclei segmentation models

Fast, concurrent inference for YuE2. Same model and recipe: 1.68× faster per song, 3.31× more songs per GPU.

Official PyTorch implementation of HGPTrans: hierarchical graph-pooling Transolver for vehicle aerodynamic drag prediction (Cd regression) from STL meshes.

Local AI BGM and sound effects for Claude Code and any MCP client. Stable Audio 3 on Apple Silicon (MLX) and Windows NVIDIA GPUs (PyTorch). No cloud, no API key.

Move hints for Tartarus Prison in Torchlight: Infinite: an overlay that reads the screen and the game's log and solves the board

A SuperPod-Affinity Training Acceleration Framework.

Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning

A universal scalable machine learning model deployment solution

From PyTorch model to end-to-end TensorRT inference experience in two commands—AI-native, cross-platform, and built for the best possible user experience.

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.