A stand-alone implementation of several NumPy dtype extensions used in machine learning.

(Crafter + NetHack) in JAX. ICML 2024 Spotlight.

Run `ssh bitbop.io`, get a GPU dev machine! 🤖

JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).

WIP implementation of block-sparse dropless MoE in JAX

Jit-able runtime assertions for JAX in NumPy style.

lightweight, standalone C++ inference engine for Google's Gemma models.

Differentiable, Hardware Accelerated, Molecular Dynamics

Legible, Scalable, Reproducible Foundation Models with Named Tensors and Jax

State of the art inference for your bayesian models.

A simple, performant and scalable Jax LLM!

Everything you want to know about Google Cloud TPU

Run PyTorch in JAX. 🤝

Flax is a neural network library for JAX that is designed for flexibility.

JAX Arrays for human consumption

An Extensible Deep Learning Library

Inference Combinators in JAX

Exponential families for JAX

attention models for those without it

Probabilistic Programming and Nested sampling in JAX

A playbook for systematically maximizing the performance of deep learning models.

Myriad is a real-world testbed that aims to bridge trajectory optimization and deep learning.

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

JAX Synergistic Memory Inspector

A machine learning compiler for GPUs, CPUs, and ML accelerators

jax-triton contains integrations between JAX and OpenAI Triton

Development repository for the Triton language and compiler

Oryx is a library for probabilistic programming and deep learning built on top of Jax.

Python library which enables complex compositions of language models such as scratchpads, chain of thought, tool use, selection-inference, and more.