EasyLM
A JAX/Flax-based framework for easy and scalable pre-training, fine-tuning, evaluation, and serving of large language models.
What is EasyLM?
EasyLM is a JAX/Flax-based framework that simplifies the process of working with large language models. It provides a one-stop solution for pre-training, fine-tuning, evaluating, and serving LLMs, leveraging JAX's pjit functionality to scale training across hundreds of TPU or GPU accelerators. The framework integrates with Hugging Face libraries and supports models like LLaMA, making it accessible for both research and practical applications.
Researchers and developers focused on training, fine-tuning, or deploying large language models who want a scalable, JAX-based solution without the complexity of other frameworks.
EasyLM offers a streamlined, easy-to-customize codebase that supports distributed training at scale, integrates seamlessly with popular tools like Hugging Face, and provides pre-built support for leading open-source LLM architectures like LLaMA.
Overview
Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.
Use Cases
Best For
- Distributed training of large language models across TPU/GPU clusters
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
