A JAX/Flax-based framework for easy and scalable pre-training, fine-tuning, evaluation, and serving of large language models.
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.
Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.
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Leverages JAX's pjit to shard models and data across hundreds of TPU/GPU accelerators, enabling efficient training of large models that exceed single-device memory.
Built on Hugging Face's transformers and datasets libraries, simplifying data handling and providing easy access to pre-trained models for customization.
Includes OpenLLaMA as a drop-in replacement for LLaMA, offering a commercially viable option without Meta's licensing restrictions.
Provides separate installation scripts for GPU hosts via Conda and Google Cloud TPU Pods, accommodating different hardware environments with clear guidelines.
Currently only supports LLaMA-based architectures, limiting versatility for users interested in other LLM families or newer models outside this ecosystem.
Optimized for Google Cloud TPU Pods with specific setup scripts, making integration with other cloud providers or local GPU clusters more complex and less documented.
Requires familiarity with JAX and Flax, which have a steeper learning curve and smaller community compared to PyTorch, potentially slowing onboarding for new users.