English(EN) MPQ: A Message-Passing View of Post-Training Quantization

新的MPQ技术通过保留软后验信息来改进LLM量化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员推出了一种新颖的大型语言模型后训练量化方法——消息传递量化（MPQ）。与现有方法过早地为权重块确定单个码字不同，MPQ在块之间保留软后验信息。该方法利用向量近似消息传递在确定硬码字之前计算近似块后验，从而提高了Llama、Qwen和Mistral等模型的困惑度和零样本准确率。 AI

影响这项研究通过改进量化技术，可能带来更高效的大型语言模型部署。

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arXiv cs.AI TIER_1 English(EN) · Yanshuo Chen, Chenxi Liu, Reza Shirkavand, Heng Huang · 2026-10-26 04:00

MPQ：后训练量化的消息传递视角

arXiv:2610.32127v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a key technique for efficiently deploying large language models, as it compresses weights into low-bit representations without retraining. Existing PTQ solvers, including GPTQ, LDLQ, and block coo…