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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.
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Zheng et al. (Wed,) studied this question.
www.synapsesocial.com/papers/68e732d9b6db6435876ac8a1 — DOI: https://doi.org/10.48550/arxiv.2403.13372
Yaowei Zheng
Richong Zhang
Junhao Zhang
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