OneBit: Towards Extremely Low-bit Large Language Models
Yuzhuang Xu Xu Han Zonghan Yang Shuo Wang Qingfu Zhu Zhiyuan Liu Weidong Liu Wanxiang Che
Model quantification uses low bit-width values to represent the weight
matrices of models, which is a promising approach to reduce both storage and
computational overheads of deploying highly anticipated LLMs. However, existing
quantization methods suffer severe performance degradation when the bit-width
is extremely reduced, and thus focus on utilizing 4-bit or 8-bit values to
quantize models. This paper boldly quantizes the weight matrices of LLMs to
1-bit, paving the way for the extremely low bit-width deployment of LLMs. For
this target, we introduce a 1-bit quantization-aware training (QAT) framework
named OneBit, including a novel 1-bit parameter representation method to better
quantize LLMs as well as an effective parameter initialization method based on
matrix decomposition to improve the convergence speed of the QAT framework.
Sufficient experimental results indicate that OneBit achieves good performance
(at least 83% of the non-quantized performance) with robust training processes
when only using 1-bit weight matrices.