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HydraLoRA Asymmetric Fine-Tuning Method — Poster

This poster introduces HydraLoRA, an asymmetric Low-Rank Adaptation method utilizing a shared A matrix and multiple B matrices to enhance fine-tuning efficiency and performance, demonstrated through system design diagrams and extensive benchmark evaluations.

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Paper title: HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning Abstract: This poster introduces HydraLoRA, an asymmetric Low-Rank Adaptation method utilizing a shared A matrix and multiple B matrices to enhance fine-tuning efficiency and performance, demonstrated through system design diagrams and extensive benchmark evaluations. Paper body (method & results): <!DOCTYPE html> <html lang="en"> <head> <meta content="text/html; charset=utf-8" http-equiv="content-type"/> <title>HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning</title>  <meta content="width=device-width, initial-scale=1, shrink-to-fit=no" name="viewport"/> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/ar5iv.0.7.9.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/ar5iv-fonts.0.7.9.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/latexml_styles.css" rel="stylesheet" type="text/css"/> <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/html2canvas/1.3.3/html2canvas.min.js"></script> <script src="/static/browse/0.3.4/js/addons_new.js"></script> <script src="/static/browse/0.3.4/js/feedbackOverlay.js"></script> <base href="/html/2404.19245v1/"/></head> <body> <nav class="ltx_page_navbar"> <nav class="ltx_TOC"> <ol class="ltx_toclist"> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S1" title="In HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">1 Introduction</a></li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S2" title="In HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">2 Background and Motivation</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S2.SS1" title="In 2 Background and Motivation ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">2.1 LoRA Basics</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S2.SS2" title="In 2 Background and Motivation ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">2.2 LoRA’s Practical Dilemma</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S2.SS3" title="In 2 Background and Motivation ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">2.3 Observations</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S3" title="In HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">3 HydraLoRA</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S3.SS1" title="In 3 HydraLoRA ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">3.1 Asymmetric LoRA architecture</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S3.SS2" title="In 3 HydraLoRA ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">3.2 Workflow of HydraLoRA</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_subsubsection"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S3.SS2.SSS1" title="In 3.2 Workflow of HydraLoRA ‣ 3 HydraLoRA ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">3.2.1 Initialization via K-means</a></li> <li class="ltx_tocentry ltx_tocentry_subsubsection"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S3.SS2.SSS2" title="In 3.2 Workflow of HydraLoRA ‣ 3 HydraLoRA ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">3.2.2 Training</a></li> <li class="ltx_tocentry ltx_tocentry_subsubsection"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S3.SS2.SSS3" title="In 3.2 Workflow of HydraLoRA ‣ 3 HydraLoRA ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">3.2.3 Inference</a></li> </ol> </li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4" title="In HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">4 Experiments</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS1" title="In 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">4.1 Experiment Setting</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS1.SSS0.Px1" title="In 4.1 Experiment Setting ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">Benchmarks</a></li> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS1.SSS0.Px2" title="In 4.1 Experiment Setting ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">Baselines</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS2" title="In 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">4.2 Overall Performance</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS2.SSS0.Px1" title="In 4.2 Overall Performance ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">RQ1: Is it more effective to use multiple smaller LoRA heads for specific tasks rather than one single LoRA for the entire domain dataset, given the same parameter count?</a></li> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS2.SSS0.Px2" title="In 4.2 Overall Performance ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">RQ2: Will multiple LoRA heads, individually trained on different data, improve efficiency by distinguishing matrix B parameters?</a></li> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS2.SSS0.Px3" title="In 4.2 Overall Performance ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">RQ3: How does HydraLoRA fare against other merge methods in complex, multi-task domains, considering scalability and robustness?</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS3" title="In 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">4.3 Energy and throughput analysis</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS3.SSS0.Px1" title="In 4.3 Energy and throughput analysis ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">RQ4: How does the “Hydra” structure in HydraLoRA enhance system efficiency, particularly in reducing training energy consumption and latency?</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS4" title="In 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">4.4 Ablation Study</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS4.SSS0.Px1" title="In 4.4 Ablation Study ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">RQ6: What impact do the MoE architecture and the gate function have on the fine-tuning process?</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS5" title="In 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">4.5 Hyper-parameter Analysis</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S4.SS5.SSS0.Px1" title="In 4.5 Hyper-parameter Analysis ‣ 4 Experiments ‣ HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning">RQ7: How do the number of intrinsic component of HydraLoRA influence performance outcomes?</a></li> </ol> </li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2404.19245v1#S5" title="In HydraLoRA: An Asymmetric LoRA Architecture f

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