LCQ: Low-Rank Codebook based Quantization for Large Language Models

• URL: http://arxiv.org/abs/2405.20973v1
• Date: Fri, 31 May 2024 16:21:05 GMT
• Title: LCQ: Low-Rank Codebook based Quantization for Large Language Models
• Authors: Wen-Pu Cai, Wu-Jun Li,
• Abstract summary: We propose low-rank codebook based quantization for large language models. Experiments show LCQ can achieve better accuracy than existing methods with a negligibly extra storage cost.
• Score: 12.004172212239848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models~(LLMs) have recently demonstrated promising performance in many tasks. However, the high storage and computational cost of LLMs has become a challenge for deploying LLMs. Weight quantization has been widely used for model compression, which can reduce both storage and computational cost. Most existing weight quantization methods for LLMs use a rank-one codebook for quantization, which results in substantial accuracy loss when the compression ratio is high. In this paper, we propose a novel weight quantization method, called low-rank codebook based quantization~(LCQ), for LLMs. LCQ adopts a low-rank codebook, the rank of which can be larger than one, for quantization. Experiments show that LCQ can achieve better accuracy than existing methods with a negligibly extra storage cost.

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