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EMLoC:基于模拟器的内存高效微调与LoRA校正

EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction

June 13, 2025
作者: Hsi-Che Lin, Yu-Chu Yu, Kai-Po Chang, Yu-Chiang Frank Wang
cs.AI

摘要

开源基础模型已迅速获得广泛采用与发展,为跨领域提供了强大的通用能力。然而,针对特定领域或个性化任务对大型基础模型进行微调,由于远超推理所需的内存开销,对大多数用户而言仍成本过高。我们提出了EMLoC,一种基于模拟器的内存高效微调框架,结合LoRA校正技术,使得模型微调能在与推理相同的内存预算内完成。EMLoC通过在小规模下游校准集上采用激活感知的奇异值分解(SVD)构建任务特定的轻量级模拟器。随后,通过LoRA在此轻量级模拟器上进行微调。为解决原始模型与压缩模拟器之间的偏差,我们提出了一种新颖的补偿算法,用于校正微调后的LoRA模块,使其能够无缝融入原始模型进行推理。EMLoC支持灵活的压缩比和标准训练流程,使其能适应广泛的应用场景。大量实验表明,EMLoC在多个数据集和模态上均优于其他基线方法。更为显著的是,无需量化处理,EMLoC便能在单块24GB消费级GPU上实现38B模型的微调,为个体用户带来了高效且实用的模型适配方案。
English
Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains. However, fine-tuning large foundation models for domain-specific or personalized tasks remains prohibitively expensive for most users due to the significant memory overhead beyond that of inference. We introduce EMLoC, an Emulator-based Memory-efficient fine-tuning framework with LoRA Correction, which enables model fine-tuning within the same memory budget required for inference. EMLoC constructs a task-specific light-weight emulator using activation-aware singular value decomposition (SVD) on a small downstream calibration set. Fine-tuning then is performed on this lightweight emulator via LoRA. To tackle the misalignment between the original model and the compressed emulator, we propose a novel compensation algorithm to correct the fine-tuned LoRA module, which thus can be merged into the original model for inference. EMLoC supports flexible compression ratios and standard training pipelines, making it adaptable to a wide range of applications. Extensive experiments demonstrate that EMLoC outperforms other baselines across multiple datasets and modalities. Moreover, without quantization, EMLoC enables fine-tuning of a 38B model on a single 24GB consumer GPU-bringing efficient and practical model adaptation to individual users.
PDF22June 18, 2025