With the rapid growth in the use of fine-tuning for large language models (LLMs), optimizing fine-tuning while keeping inference efficient has become highly important. However, this is a challenging task as it requires improvements in all aspects, including inference speed, fine-tuning speed, memory consumption, and, most importantly, model quality. Previous studies have attempted to achieve this by combining quantization with fine-tuning, but they have failed to enhance all four aspects simultaneously. In this study, we propose a new lightweight technique called Quantization for Efficient Fine-Tuning (QEFT). QEFT accelerates both inference and fine-tuning, is supported by robust theoretical foundations, offers high flexibility, and maintains good hardware compatibility. Our extensive experiments demonstrate that QEFT matches the quality and versatility of full-precision parameter-efficient fine-tuning, while using fewer resources. Our code is available at https://github.com/xvyaward/qeft.

本研究解决了大语言模型微调过程中的速度、内存消耗和模型质量等多方面优化的挑战。我们提出了一种名为QEFT的新型轻量化技术，能够加速推理和微调，保持高灵活性和良好的硬件兼容性，同时在资源使用上具有优势。实验证明，QEFT在质量和灵活性方面与全精度参数高效微调相当。

QEFT：大语言模型高效微调的量化技术