title: LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
summary: 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.
paper_authors._id: 65fbae8adfd0ff86aa7a217a
paper_authors.name: Yaowei Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T09:11:34.746Z
paper_authors._id: 642fef28a043f0ac7defa8a9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/642fef28a043f0ac7defa8a9/RwOEkuj3fOnOA54tGR7Ea.png
paper_authors.fullname: Yaowei Zheng
paper_authors.type: user
paper_authors.user: hiyouga
paper_authors._id: 65fbae8adfd0ff86aa7a217b
paper_authors.name: Richong Zhang
paper_authors._id: 65fbae8adfd0ff86aa7a217c
paper_authors.name: Junhao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T09:33:46.449Z
paper_authors._id: 6524d66966ebe0519873c4c5
paper_authors.avatarUrl: /avatars/18a52cf9cf90f9ff309284a1c9cae6ac.svg
paper_authors.fullname: Junhao Zhang
paper_authors.type: user
paper_authors.user: OnlyAR
paper_authors._id: 65fbae8adfd0ff86aa7a217d
paper_authors.name: Yanhan Ye
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-21T10:02:39.082Z
paper_authors._id: 65fc00206529e3fcc230b820
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65fc00206529e3fcc230b820/IeZjMvHYZeZn8-igQ45R8.jpeg
paper_authors.fullname: Yanhan Ye
paper_authors.type: user
paper_authors.user: CoolColoury
paper_authors._id: 65fbae8adfd0ff86aa7a217e
paper_authors.name: Zheyan Luo
paper_title: LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
paper_summary: 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.
paper_ai_keywords: efficient fine-tuning large language models (LLMs) fine-tuning unified framework cutting-edge efficient training methods. web UI LlamaBoard empirical validation language modeling text generation semi-released GitHub repository