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.

翻译：高效微调是使大型语言模型适应下游任务的关键。然而，在不同模型上实现这些方法需要耗费大量精力。我们提出LlamaFactory，一个集成了系列前沿高效训练方法的统一框架。用户可通过内置的Web UI LlamaBoard灵活定制100余种大语言模型的微调过程，无需编写代码。我们通过语言建模和文本生成任务实证验证了该框架的高效性与有效性。该框架已发布于https://github.com/hiyouga/LLaMA-Factory，并获得超过13000颗星标及1600次分叉。