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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.20973 |
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| _version_ | 1866916605283270656 |
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| author | Cai, Wen-Pu Li, Ming-Yang Li, Wu-Jun |
| author_facet | Cai, Wen-Pu Li, Ming-Yang Li, Wu-Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20973 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LCQ: Low-Rank Codebook based Quantization for Large Language Models Cai, Wen-Pu Li, Ming-Yang Li, Wu-Jun Machine Learning Computation and Language 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. |
| title | LCQ: Low-Rank Codebook based Quantization for Large Language Models |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2405.20973 |