(CA) N-gram vs Experts explained

N-gram 与专家模型：理解大语言模型架构的权衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 02:00

一篇Reddit帖子解释了大语言模型中n-gram和专家混合（MoE）架构的区别。MoE模型通过选择特定的前馈网络块来执行推理任务，而n-gram则作为局部短语的记忆回忆机制。该帖子指出，n-gram可以分担高达约25%的模型参数，并建议将这些参数存储在SSD而非RAM上可以提高性能。这种混合方法，以Qwen 3.8 Flash Next (Qwen4Exp)为例，通过在每个token激活一小部分参数，同时利用整个训练模型，从而实现更快的运行速度。 AI

影响解释了可能影响未来大语言模型设计和优化策略的架构差异。

排序理由该条目是用户对大语言模型架构相关技术概念的解释，而非主要来源发布或公告。

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r/LocalLLaMA TIER_1 (CA) · /u/Beamsters · 2026-08-27 02:00

N-gram vs 专家：解释

<div class="md"><p>Since Qwen's dropped the Qwen4Exp architecture bomb that focus on offloading parameters to n-gram instead of pure mixture of experts, I dug into this and learned quite a lot. Here's the summary. Expect mistakes from human's writing lol.</p> <p>TL…

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N-gram vs 专家：解释

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