preprint
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LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
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- Citations
- 14
- References
- 0
- Comments
- 0
Paper overview
Abstract
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.
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Publication details
- DOI
- 10.48550/arxiv.2403.13372
- OpenAlex
- W4393108840
- Document type
- preprint
- Language
- EN
- Source
- arXiv (Cornell University)
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