LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Autor:	Zheng, Yaowei, Zhang, Richong, Zhang, Junhao, Ye, Yanhan, Luo, Zheyan, Feng, Zhangchi, Ma, Yongqiang
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 13 pages, accepted to ACL 2024 System Demonstration Track
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.13372 Zobrazit plný text záznamu View this record from Arxiv