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Large foundation models have demonstrated a great ability to achieve generalhuman-level intelligence far beyond traditional approaches. As the techniquekeeps attracting attention from the AI community, more and more largefoundation models have become publically available. However, most of thosemodels exhibit a major deficiency in specialized-task applications, where thestep of finetuning is still required for obtaining satisfactory performance. Asthe number of available models and specialized tasks keeps growing, the job ofgeneral finetuning becomes highly nontrivial. In this paper, we take the firststep to address this issue. We introduce an extensible and lightweight toolkit,LMFlow, which aims to simplify the finetuning and inference of general largefoundation models. LMFlow offers a complete finetuning workflow for a largefoundation model to support personalized training with limited computingresources. 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 beenthoroughly tested and is available at https://github.com/OptimalScale/LMFlow.

LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models