young-geng/EasyLM

Health v2 · maintenance only

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Adoption not part of the score

2514 stars · 259 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EasyLM is a JAX/Flax-based framework for pre-training, finetuning, evaluating, and serving large language models like LLaMA. It scales training across hundreds of TPU/GPU accelerators using JAX's pjit model sharding.

Use cases

• pretrain large language models on tpu pods
• finetune llama on multiple gpus
• train llms in jax with model sharding
• serve a finetuned llama chatbot locally
• reproduce llama with permissive license weights
• multi-host distributed llm training

When to choose

• you want to train or finetune LLaMA-family models in JAX/Flax
• your model doesn't fit on a single accelerator and needs pjit sharding
• you're running on Google Cloud TPU Pods or multi-GPU hosts
• you want a simpler, customizable codebase than heavier training frameworks

When to avoid

• you prefer PyTorch-based training ecosystems
• you only need inference of existing models without training
• you need broad model support beyond the LLaMA family
• you need a framework with frequent updates and active maintenance

Facets

library · maturity maintenance

llm-training llm-inference deep-learning machine-learning large-language-models deep-learning machine-learning python cloud jax flax llama tpu model-sharding pjit finetuning huggingface natural-language-processing gpu linux

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markdown · JSON · MCP: product_card(name="young-geng/EasyLM")