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English(EN) GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling

新方法提高 LLM 量化精度和效率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了新的量化大型语言模型（LLM）的方法，以减小其规模并提高部署效率。一种方法 GSQ（Gumbel-Softmax 量化）采用了一种新颖的标量量化技术，即使在非常低的比特宽度下，也能实现与更复杂的向量量化方法相当的精度。另一篇论文探讨了用于 1 位 LLM 量化的几何保持输出对齐，解决了在将模型压缩到最精简形式时保持模型性能和结构信息所面临的挑战。 AI

影响 这些量化技术可以显著减少运行 LLM 所需的计算资源，使其更容易用于本地部署和专业应用。

排序理由 两篇研究论文详细介绍了 LLM 量化的新颖方法。

在 arXiv cs.CL 阅读 →

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新方法提高 LLM 量化精度和效率

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan, Michael Helcig, Eldar Kurtic, Maximilian Kleinegger, Dan Alistarh ·

    GSQ：通过 Gumbel-Softmax 采样实现 LLM 的高精度低精度标量量化

    arXiv:2604.18556v3 Announce Type: replace Abstract: Quantization has become a standard tool for efficient LLM deployment, especially for local inference, where models are now routinely served at 2-3 bits per parameter. The state of the art is currently split into simple scalar qu…

  2. arXiv cs.LG TIER_1 English(EN) · Dung Anh Hoang, Cuong Pham, Cuong Nguyen, Trung le, Jianfei Cai, Thanh-Toan Do ·

    超越局部重构：几何保持的输出对齐用于二值化大语言模型量化

    arXiv:2512.21651v4 Announce Type: replace Abstract: Recent advances in post-training quantization for large language models (LLMs) have demonstrated that low-bit quantization methods can maintain most of the original model performance. Despite such successes, 1-bit quantization r…

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