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English(EN) Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing

新的 Colla-Q 框架通过激活熵平衡 MoE 专家性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的量化框架 Colla-Q，旨在减轻 Mixture-of-Experts (MoE) 模型中的性能下降。该方法利用激活熵来平衡各个专家之间的比特分配，确保协作性能并减少对校准数据集的依赖。通过促进一致的专家性能，Colla-Q 旨在增强量化 MoE 架构的整体鲁棒性和稳定性。 AI

影响 这项研究可能通过降低大型 MoE 模型的内存和计算需求，从而实现更高效的部署。

排序理由 这是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.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的 Colla-Q 框架通过激活熵平衡 MoE 专家性能

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这是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.CL TIER_1 English(EN) · Eunju Shin, Jongbin Ryu ·

    Colla-Q：迈向通过 Minimax 精确度平衡的 MoE 量化中的协作专家

    arXiv:2609.18131v1 Announce Type: cross Abstract: In this paper, we present a Mixture-of-Experts (MoE) quantization method based on activation entropy. Although quantization reduces memory and computational costs, it can substantially degrade performance. In particular, performan…

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