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EASYLM

JAX/Flax pretrain, finetune, eval, and serve for LLMs

Apache-2.0

ABOUT

JAX LLM work usually means gluing training, checkpoint conversion, eval, and serving yourself. EasyLM packages those steps for LLaMA-class models so a small team can pretrain or finetune in Flax and serve the result without a Megatron or Hugging Face Trainer rewrite.

INTEGRATION GUIDE

1. Finetune a LLaMA-class model in JAX/Flax with published scripts 2. Convert and serve a JAX checkpoint after pretraining 3. Evaluate generated text without leaving the EasyLM workflow 4. Prototype JAX training on TPU or GPU before moving to MaxText

TAGS

pythonjaxflaxllamatrainingservingopen-source
EasyLM — AI Tool | Agentic AI For Good