LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
About
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.
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, Yongqiang Ma• 2024
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Multi-hop Question Answering | HotpotQA (test) | F121.7 | 198 | |
| Question Answering | 2WikiMultiHopQA (test) | F120.28 | 69 | |
| Question Answering | NQ (test) | -- | 66 | |
| Multi-hop Question Answering | Bamboogle (test) | -- | 46 | |
| Multi-hop Question Answering | 2Wiki (test) | F1 Score25.9 | 20 | |
| General Question Answering | TriviaQA (test) | F135.4 | 11 | |
| Preference Adaptation | CALCONFLICTBENCH (test) | AER0.27 | 4 |
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