jax

Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.

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

A simple, performant, and scalable Jax LLM!

An Extensible Deep Learning Library

Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

JAX - A curated list of resources https://github.com/google/jax

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

A deep learning package for many-body potential energy representation and molecular dynamics

Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/

Magenta RealTime 2: An Open-Weights Live Music Model

Automated Machine Learning on Kubernetes

TensorLy: Tensor Learning in Python.

This repository provides the code and model checkpoints for AIMv1 and AIMv2 research projects.

Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs

Digital Audio Workstation with Python; VST instruments/effects, parameter automation, FAUST, JAX, Warp Markers, and JUCE processors

PDEBench: An Extensive Benchmark for Scientific Machine Learning

💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️

Thermodynamic Hypergraphical Model Library in JAX

Really Fast End-to-End Jax RL Implementations

Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments

Hardware accelerated, batchable and differentiable optimizers in JAX.

A Python package for probabilistic state space modeling with JAX

Pretrained model hub for Keras 3.

Computational Fluid Dynamics in JAX

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

JAX in JavaScript – ML library for the web, running on WebGPU & Wasm

A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine

🕹️ A diverse suite of scalable reinforcement learning environments in JAX

Library for reading and processing ML training data.

Optimal transport tools implemented with the JAX framework, to solve large scale matching problems of any flavor.