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English(EN) TriSP: Tri-Signal Structured Pruning for Large Language Models

新的TriSP方法在保持性能的同时显著剪枝LLM

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TriSP，一种用于大型语言模型的新型结构化剪枝方法，旨在降低其计算和内存成本。TriSP结合了权重幅度、激活范数和梯度敏感性，以创建通道级重要性得分。当与自适应层预算分配和LoRA恢复配对时，TriSP在LLaMA-7B模型上表现出卓越的性能，实现了更低的困惑度和更高的准确性，同时显著提高了推理吞吐量。 AI

影响 这项剪枝技术可以实现大型语言模型在标准硬件上更高效的部署，降低成本并提高可访问性。

排序理由 该集群包含一篇详细介绍LLM剪枝新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的TriSP方法在保持性能的同时显著剪枝LLM

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该集群包含一篇详细介绍LLM剪枝新方法的学术论文。[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) · Manel Kara laoua, Soumia Bouyahiaoui, Aicha Boutorh ·

    TriSP：大型语言模型的三信号结构化剪枝

    arXiv:2607.22587v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but their deployment is constrained by the memory and compute cost of their parameters. Structured pruning addresses this by removing entire structures suc…

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