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

• URL: http://arxiv.org/abs/2506.12015v1
• Date: Fri, 13 Jun 2025 17:59:58 GMT
• Title: EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction
• Authors: Hsi-Che Lin, Yu-Chu Yu, Kai-Po Chang, Yu-Chiang Frank Wang,
• Abstract summary: Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains.<n>However, fine-tuning large foundation models for domain-specific or personalized tasks remains expensive for most users due to the significant memory overhead beyond that of inference.<n>We introduceoC, an Emulator-based Memory-efficient fine-tuning framework with LoRA Correction, which enables model fine-tuning within the same memory budget required for inference.
• Score: 23.69537251732286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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