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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

TaskDatasetResultRank
Multi-hop Question AnsweringHotpotQA (test)
F121.7
198
Question Answering2WikiMultiHopQA (test)
F120.28
69
Question AnsweringNQ (test)--
66
Multi-hop Question AnsweringBamboogle (test)--
46
Multi-hop Question Answering2Wiki (test)
F1 Score25.9
20
General Question AnsweringTriviaQA (test)
F135.4
11
Preference AdaptationCALCONFLICTBENCH (test)
AER0.27
4
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