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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
Mathematical ReasoningAIME 2024
Accuracy78.3
370
Multi-hop Question AnsweringHotpotQA (test)
F121.7
255
Question AnsweringNQ (test)--
86
Multi-hop Question AnsweringBamboogle (test)--
84
Question Answering2WikiMultiHopQA (test)
F120.28
81
Commonsense ReasoningCSQA OOD (test)
Accuracy81.5
32
Ethical ReasoningEthics (test)
Accuracy81.45
32
ReasoningCALI OOD (test)
Accuracy76.08
32
ReasoningGLOQA (test)
Accuracy50.98
32
Mathematical ReasoningGSM8K OOD (test)
Accuracy91.62
32
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