OneBit: Towards Extremely Low-bit Large Language Models

Published on Feb 17
Submitted by akhaliq on Feb 20


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.


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Looks like their results are better than BiLLM. They only go up to 13B in the paper, perhaps 70B would be more favorable.

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Great work! Are there any plans to release the code and/or model checkpoints?

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We will release the training code and LLaMA checkpoints as soon as possible, so stay tuned! : )

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Code and checkpoints are available at
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Revolutionizing Large Language Models: OneBit's 1-Bit Quantization Breakthrough

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