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

TL;DR

LMFlow is an extensible toolkit designed to simplify and accelerate the domain-specific fine-tuning and inference of large foundation models, supporting various training and optimization techniques.

Contribution

It introduces a comprehensive, lightweight toolkit that streamlines specialized training of foundation models with limited resources and supports multiple fine-tuning and inference methods.

Findings

Supports continuous pretraining and instruction tuning

Enables parameter-efficient finetuning and alignment tuning

Facilitates inference acceleration and multimodal finetuning

Abstract

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, an increasing number of foundation models are becoming publicly accessible. However, a significant shortcoming of most of these models lies in their performance in specialized-domain and task-specific applications, necessitating domain- and task-aware fine-tuning to develop effective scientific language models. As the number of available foundation models and specialized tasks keeps growing, the job of training scientific language models becomes highly nontrivial. In this paper, we initiate steps to tackle this issue. We introduce an extensible and lightweight toolkit, LMFlow, which aims to simplify the domain- and task-aware finetuning of general foundation models. LMFlow offers a complete finetuning workflow for a foundation model to support specialized training with limited computing resources. Furthermore, it supports continuous pretraining, instruction tuning, parameter-efficient finetuning, alignment tuning, inference acceleration, long context generalization, model customization, and even multimodal finetuning, along with carefully designed and extensible APIs. This toolkit has been thoroughly tested and is available at https://github.com/OptimalScale/LMFlow.