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 | |
|---|---|---|---|---|
| Mathematical Reasoning | AIME 2024 | Accuracy78.3 | 370 | |
| Multi-hop Question Answering | HotpotQA (test) | F121.7 | 311 | |
| Multi-hop Question Answering | 2WikiMultiHopQA (test) | EM11.72 | 226 | |
| Question Answering | NQ (test) | -- | 133 | |
| Question Answering | 2WikiMultiHopQA (test) | F120.28 | 113 | |
| Multi-hop Question Answering | Bamboogle (test) | -- | 98 | |
| Multi-hop Question Answering | 2Wiki (test) | F1 Score25.9 | 34 | |
| Commonsense Reasoning | CSQA OOD (test) | Accuracy81.5 | 32 | |
| Ethical Reasoning | Ethics (test) | Accuracy81.45 | 32 | |
| Reasoning | CALI OOD (test) | Accuracy76.08 | 32 |
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