ACL 2024system demonstrations440 citations
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo
Abstract
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It provides a solution for flexibly customizing the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and received over 25,000 stars and 3,000 forks.
BibTeX
@inproceedings{zheng-etal-2024-llamafactory,
title = "{L}lama{F}actory: Unified Efficient Fine-Tuning of 100+ Language Models",
author = "Zheng, Yaowei and
Zhang, Richong and
Zhang, Junhao and
Ye, Yanhan and
Luo, Zheyan",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.38/",
doi = "10.18653/v1/2024.acl-demos.38",
pages = "400--410"
}