LMFlow was initiated by the statistics and machine learning laboratory team of Hong Kong University of Science and Technology. It is committed to establishing a fully open large-scale model research platform, supporting various experiments under limited machine resources, and improving existing data utilization methods and optimization algorithms on the platform. Efficiency, allowing the platform to develop into a large model training system that is more efficient than previous methods.

The ultimate goal of LMFlow is to help everyone train a domain-specific and personalized large model with as few resources as possible, so as to promote the research and application of large models.

LMFlow has four major features:Scalable, lightweight, customizable and fully open source.

Based on this, users can quickly train their own models and continue with the second iteration. These models are not limited to the recently popular LLaMA, but also include models such as GPT-2 and Galactica.