English(EN) ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

新的ADMM-Q算法增强了LLM量化，降低了困惑度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了ADMM-Q，这是一种旨在改进大型语言模型训练后量化的新算法。该方法利用交替方向乘子法的组合变体来解决逐层量化问题，旨在降低存储和计算需求，同时不显著影响模型效用。ADMM-Q被设计为现有量化器的模块化替代品，并且可以与其他技术集成。在Qwen3-8B模型上的初步测试显示，在各种量化设置下困惑度均有显著降低。 AI

影响这种新的量化方法可以实现更高效的部署和使用，尤其是在资源受限的硬件上运行大型语言模型。

排序理由该集群包含一篇详细介绍LLM量化新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ryan Lucas, Mehdi Makni, Xiang Meng, Adam Deng, Rahul Mazumder · 2026-09-01 04:00

ADMM-Q：一种改进的基于Hessian的权重量化器，用于大型语言模型的训练后量化

arXiv:2605.11222v2 Announce Type: replace Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ) is a leading approach for compressing LLMs. Popular weight quantization proc…

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报道来源 [1]

ADMM-Q：一种改进的基于Hessian的权重量化器，用于大型语言模型的训练后量化

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