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LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

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arxiv 2310.08659 v4 pith:URQTB6NQ submitted 2023-10-12 cs.CL cs.AIcs.LG

Yixiao Li , Yifan Yu , Chen Liang , Pengcheng He , Nikos Karampatziakis , Weizhu Chen , Tuo Zhao This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords quantizationfine-tuninglanguageloraloftqtasksdownstreaminitialization
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning. In this work we focus on the scenario where quantization and LoRA fine-tuning are applied together on a pre-trained model. In such cases it is common to observe a consistent gap in the performance on downstream tasks between full fine-tuning and quantization plus LoRA fine-tuning approach. In response, we propose LoftQ (LoRA-Fine-Tuning-aware Quantization), a novel quantization framework that simultaneously quantizes an LLM and finds a proper low-rank initialization for LoRA fine-tuning. Such an initialization alleviates the discrepancy between the quantized and full-precision model and significantly improves generalization in downstream tasks. We evaluate our method on natural language understanding, question answering, summarization, and natural language generation tasks. Experiments show that our method is highly effective and outperforms existing quantization methods, especially in the challenging 2-bit and 2/4-bit mixed precision regimes. The code is available on https://github.com/yxli2123/LoftQ.

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Cited by 24 Pith papers

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