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A Note on LoRA
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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.
Forward citations
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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Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
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