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A Note on LoRA
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LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy. This note extends the original LoRA paper by offering new perspectives that were not initially discussed and presents a series of insights for deploying LoRA at scale. Without introducing new experiments, we aim to improve the understanding and application of LoRA.
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Cited by 3 Pith papers
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A Reference-Free Framework for Evaluating Single-Frame ISP Pipelines
A proxy-reference network trained on synthetic camera pipelines estimates PSNR, SSIM, and LPIPS without a ground-truth reference, with LoRA fine-tuning adapting it to real pipelines.
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PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models
PLoP selects LoRA adapter placement by ranking normalized feature norms and placing adapters on the lowest-scoring module types, using only forward passes.
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Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting
MoLA adapts a pre-trained short-horizon forecaster to multiple forecast steps via segment-specific mixtures of shared low-rank adapters, reporting modest mean-squared-error gains over the base models on most of eight ...
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