LlamaFactory：100多种语言模型的统一高效微调

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

March 20, 2024

作者: Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo

cs.AI

摘要

高效的微调对于将大型语言模型（LLMs）调整到下游任务中至关重要。然而，要在不同模型上实施这些方法需要付出相当大的努力。我们提出了LlamaFactory，这是一个统一的框架，集成了一套尖端的高效训练方法。它允许用户灵活定制100多个LLMs的微调，无需编码，通过内置的Web用户界面LlamaBoard。我们在语言建模和文本生成任务上对我们的框架的效率和有效性进行了实证验证。该框架已在https://github.com/hiyouga/LLaMA-Factory发布，并已获得超过13,000颗星和1,600个分支。

English

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 allows users to flexibly customize 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 already received over 13,000 stars and 1,600 forks.