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English(EN) ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning

ChunkFT框架大幅降低LLM微调的内存需求

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ChunkFT，一个新颖的框架，旨在显著减少大型语言模型全参数微调所需的内存。该方法动态激活一组工作参数，无需改变模型架构即可在子张量上进行梯度计算。实验表明，ChunkFT可以在单个消费级GPU上微调Llama 3-8B等模型，在显著减少内存占用的同时，实现与传统全参数微调相当的性能。 AI

影响 使得在消费级硬件上微调大型语言模型成为可能，从而可能使高级模型定制民主化。

排序理由 发布了一篇详细介绍LLM微调新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

ChunkFT框架大幅降低LLM微调的内存需求

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发布了一篇详细介绍LLM微调新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hinrich Schütze ·

    ChunkFT：内存高效全微调的字节流优化

    This work presents \textsc{ChunkFT}, a memory-efficient fine-tuning framework that reformulates full-parameter fine-tuning around a dynamically activated working set. \textsc{ChunkFT} enables gradient computation for arbitrary sub-tensors without modifying the network architectur…

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