Lmflow: An Extensible Toolkit For Finetuning And Inference Of Large Foundation Models

Diao Shizhe, Pan Rui, Dong Hanze, Shum Ka Shun, Zhang Jipeng, Xiong Wei, Zhang Tong. Arxiv 2023

[Paper] [Code]    
Applications Attention Mechanism Fine Tuning Has Code Model Architecture Multimodal Models Pretraining Methods Reinforcement Learning Tools Training Techniques

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.

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