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 24,000 stars and 3,000 forks.

翻译：高效微调对于将大语言模型（LLM）适配至下游任务至关重要。然而，在不同模型上实现这些方法需要付出大量精力。我们提出了LlamaFactory，一个集成了系列前沿高效训练方法的统一框架。该框架通过内置的Web界面LlamaBoard，为100多种LLM的灵活定制化微调提供了无需编码的解决方案。我们在语言建模和文本生成任务上实证验证了该框架的效率和有效性。该框架已在https://github.com/hiyouga/LLaMA-Factory发布，并获得超过24,000个星标和3,000次复刻。