English(EN) SandwichQuant: Which Parameters Matter Before and After Quantization?

SandwichQuant框架提升模型量化效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 04:00

研究人员推出了一种新颖的两阶段框架SandwichQuant，旨在提高模型量化效率。该方法专注于归一化-仿射参数子空间，揭示了这个低维子空间在量化过程中具有高效的纠错能力。SandwichQuant在量化前后调整参数，以增强鲁棒性并补偿残差误差，在各种低比特量化设置下，在视觉模型和大语言模型上均展现出了一致的改进。 AI

影响这项研究通过改进量化技术，可能带来更高效的大模型部署。

排序理由该集群包含一篇详细介绍新模型量化方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Peng Xia, Junbiao Pang · 2026-08-26 04:00

SandwichQuant：量化前后哪些参数更重要？

arXiv:2608.24173v1 Announce Type: new Abstract: Quantization correction methods usually optimize weights, quantization parameters, or reconstruction objectives, while the underlying parameter subspaces responsible for effective correction remain unclear. In this work, we study qu…

本文如何被排名

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SandwichQuant：量化前后哪些参数更重要？

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