LMFlow: An Extensible Toolkit for Finetuning and Inference of Large
  Foundation Models
Shizhe Diao Rui Pan Hanze Dong Ka Shun Shum Jipeng Zhang Wei Xiong Tong Zhang
Large foundation models have demonstrated a great ability to achieve general
human-level intelligence far beyond traditional approaches. As the technique
keeps attracting attention from the AI community, more and more large
foundation models have become publically available. However, most of those
models exhibit a major deficiency in specialized-task applications, where the
step of finetuning is still required for obtaining satisfactory performance. As
the number of available models and specialized tasks keeps growing, the job of
general finetuning becomes highly nontrivial. In this paper, we take the first
step to address this issue. We introduce an extensible and lightweight toolkit,
LMFlow, which aims to simplify the finetuning and inference of general large
foundation models. LMFlow offers a complete finetuning workflow for a large
foundation model to support personalized training with limited computing
resources. Furthermore, it supports continuous pretraining, instruction tuning,
parameter-efficient finetuning, alignment tuning, and large model inference,
along with carefully designed and extensible APIs. This toolkit has been
thoroughly tested and is available at https://github.com/OptimalScale/LMFlow.