LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Yaowei Zheng, Richong Zhang, Junhao Zhang, YeYanhan YeYanhan, 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.- Anthology ID:
- 2024.acl-demos.38
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Yixin Cao, Yang Feng, Deyi Xiong
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 400–410
- Language:
- URL:
- https://aclanthology.org/2024.acl-demos.38
- DOI:
- Cite (ACL):
- Yaowei Zheng, Richong Zhang, Junhao Zhang, YeYanhan YeYanhan, and Zheyan Luo. 2024. LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 400–410, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (Zheng et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-demos.38.pdf