SGLang is yet another fast serving framework for large language models and vision language models.

A high-throughput and memory-efficient inference and serving engine for LLMs

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

Inference code for Llama models

Train very large language models in Jax.

seqax = sequence modeling + JAX

LoRA for arbitrary JAX models and functions

Minimal library to train LLMs on TPU in JAX with pjit().

Supercharge huggingface transformers with model parallelism.

DSPy: The framework for programming—not prompting—foundation models

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

Scalable neural net training via automatic normalization in the modular norm.

Simple Transformer in Jax