Hugging Face 推出 PEFT 庫：用更低的硬體成本高效微調大型語言模型
Original: Parameter-Efficient Fine-Tuning using 🤗 PEFT
As the parameter scale of large language models (LLMs) continues to grow, full fine-tuning has become prohibitively expensive and…
Hugging Face 宣布推出 PEFT（Parameter-Efficient Fine-Tuning）開源庫，旨在解決微調大模型時高昂的算力與儲存成本。PEFT 整合了 LoRA、Prefix Tuning、P-Tuning 等主流技術，僅需微調極少量的額外參數即可達到與全量微調相當的效果。這使得開發者能在消費級硬體（如單張 24GB 顯示卡）上微調數十億甚至百億參數的模型，並大幅縮小模型權重檔案體積。
As the parameter scale of large language models (LLMs) continues to grow, full fine-tuning has become prohibitively expensive and impractical. To lower the hardware barrier for developers and researchers, Hugging Face has officially launched PEFT (Parameter-Efficient Fine-Tuning), an open-source library. The release of this tool marks an important milestone in democratizing the customization of large models.
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