EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction
Authors: name: Hsi-Che Lin, name: Yu-Chu Yu, name: Kai-Po Chang, name: Yu-Chiang Frank Wang

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.