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English(EN) Fine-Tuning of Transformer models with Frames

FrameFT 使用稀疏系数减少微调内存占用

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的参数高效微调 (PEFT) 策略 FrameFT，旨在减少微调大型 Transformer 模型的内存占用。与 LoRA 等现有方法不同，FrameFT 使用融合帧基内的稀疏系数来建模参数更新，允许跨模型层共享帧。这种方法在保持或提高监督微调任务（包括语言和视觉应用）性能的同时，显著减少了可训练参数的数量。 AI

影响 FrameFT 提供了一种更节省内存的方法来微调大型模型，有可能使更广泛的先进人工智能技术的应用和普及成为可能。

排序理由 该集群包含一篇详细介绍 Transformer 模型微调新方法的论文。[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 →

FrameFT 使用稀疏系数减少微调内存占用

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该集群包含一篇详细介绍 Transformer 模型微调新方法的论文。[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) · Harshavardhan Adepu, Li Zhang, Sanjiv Kumar, Vikas Singh ·

    使用Frames对Transformer模型进行微调

    arXiv:2608.26430v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) strategies such as Low-Rank Adaptation (LoRA) are effective solutions for fine-tuning large-scale pre-trained models; however, their memory requirements scale with the size of the model, $\math…

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