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新方法支持使用手机NPU进行设备端大模型微调

研究人员开发了一种新颖的方法,利用手机的神经网络处理单元(NPU)对大型语言模型(LLM)进行设备端微调。该系统名为First-Order LoRA Training,能够让LLM直接在设备上从用户的私有数据中学习,而无需上传数据。该方法优化了NPU在权重乘法中的使用,并将训练图和适配器卸载到CPU,从而提高了效率并保持了梯度精度。 AI

影响 通过允许在不上传云端的情况下进行私有数据微调,从而在移动设备上实现个性化LLM体验。

排序理由 该集群包含一篇详细介绍LLM微调新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新方法支持使用手机NPU进行设备端大模型微调

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Tool
该集群包含一篇详细介绍LLM微调新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xu Zheng, Wei Cheng, Zhuomin Chen, Dongkuan Xu, Mo Sha, Jingchao Ni, Dongsheng Luo ·

    使用手机NPU进行一阶LoRA训练

    arXiv:2610.24816v2 Announce Type: replace-cross Abstract: On-device fine-tuning lets a large language model (LLM) learn from its user's private data without uploading it. Low-rank adaptation (LoRA) reduces the trainable parameters, but each first-order training step still multipl…