LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
<|reference_start|>LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models: 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.<|reference_end|>
@article{zheng2024llamafactory:,
    title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
    author={Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo,
  Zhangchi Feng, Yongqiang Ma},
    journal={arXiv preprint arXiv:2403.13372},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13372},
    primaryClass={cs.CL cs.AI}
}