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English(EN) OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction

OPTIMA方法通过二次规划提供高效的LLM剪枝

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了OPTIMA，一种新颖的大型语言模型（LLM）单次剪枝方法，可在准确性和计算可行性之间取得平衡。该方法将层级权重重建构建为独立的、逐列的二次规划问题，这些问题可以高效地批量处理并在加速器上求解。OPTIMA在LLaMA和Gemma模型的各种稀疏度水平上均展现出改进的零样本准确率，其性能优于启发式方法，同时在各自的掩码上可与精确重建技术相媲美。 AI

影响 通过在不显著损失准确率的情况下降低推理成本，从而实现更高效的LLM部署。

排序理由 关于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 →

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

OPTIMA方法通过二次规划提供高效的LLM剪枝

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关于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) · Mohammad Mozaffari, Samuel Kushnir, Maryam Mehri Dehnavi, Amir Yazdanbakhsh ·

    OPTIMA：通过二次规划重建实现LLM的最佳单次剪枝

    arXiv:2512.13886v3 Announce Type: replace-cross Abstract: Post-training pruning cuts the inference cost of large language models, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principled joint optimization methods recover accur…

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