English(EN) An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning

新的稀疏微调方法提高了大型语言模型的效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新颖的稀疏微调（SpFT）框架，可提高适应大型语言模型的内存效率。这种新方法受神经网络剪枝技术的启发，可识别重要神经元并将微调限制在与它们相关的权重上。实验表明，该方法在实现与 LoRA 变体等最先进方法相当的准确性的同时，可将内存效率提高 20-50%。 AI

影响这项研究提供了一种更节省内存的方法来微调大型语言模型，有可能使其对计算资源有限的用户更加易于访问。

排序理由该集群包含一篇详细介绍神经网络微调新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Cen-Jhih Li, Aditya Bhaskara · 2026-09-01 04:00

通过神经网络剪枝实现低量化误差的高效稀疏微调

arXiv:2502.11439v3 Announce Type: replace-cross Abstract: Fine-tuning is an important step in adapting foundation models such as large language models to downstream tasks. To make this step more accessible to users with limited computational budgets, it is crucial to develop fine…