LCQ: Low-Rank Codebook based Quantization for Large Language Models
<|reference_start|>LCQ: Low-Rank Codebook based Quantization for Large Language Models: 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.<|reference_end|>
@article{cai2024lcq:,
    title={LCQ: Low-Rank Codebook based Quantization for Large Language Models},
    author={Wen-Pu Cai, Wu-Jun Li},
    journal={arXiv preprint arXiv:2405.20973},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20973},
    primaryClass={cs.LG cs.CL}
}