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English(EN) Zero-Shot Quantization via Weight-Space Arithmetic

新的零样本量化方法通过权重空间算术转移鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为零样本量化的新方法，该方法利用权重空间算术来提高模型对量化的鲁棒性。该技术从供体任务中提取“量化向量”并将其应用于受体模型，在不进行量化感知训练的情况下，将量化后训练的准确性在3位设置下提高了高达60%。该方法在各种视觉和语言模型以及30多个任务中都显示出显著的收益，表明量化鲁棒性可以通过模型权重的代数运算来分离和转移。 AI

影响 该方法通过减小模型尺寸而不会显著降低准确性，从而能够更有效地在资源受限的设备上部署大型模型。

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

在 arXiv cs.AI 阅读 →

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新的零样本量化方法通过权重空间算术转移鲁棒性

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该集群包含一篇详细介绍模型量化新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Daniele Solombrino, Antonio Andrea Gargiulo, Alessandro Zirilli, Luca Zhou, Adrian Robert Minut, Emanuele Rodol\`a ·

    通过权重空间算术实现零样本量化

    arXiv:2604.03420v4 Announce Type: replace-cross Abstract: We show that robustness to post-training quantization (PTQ) is a transferable direction in weight space. We call this direction the quantization vector: extracted from a donor task by simple weight-space arithmetic, it can…

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