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Traditional parameter-efficient fine-tuning (PEFT) methods such as LoRA are tightly coupled with the base model architecture, which constrains their applicability across heterogeneous pretrained large language models (LLMs).
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Gal Kaplun, Eran Malach, Preetum Nakkiran, and Shai Shalev-Shwartz · 2022
Cited alongside, same era.
Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang · 2023
Cited alongside, same era.
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Soufiane Hayou, Nikhil Ghosh, and Bin Yu · 2024
Cited alongside, same era.
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation, February 2025a
Farzad Farhadzadeh, Debasmit Das, Shubhankar Borse, and Fatih Porikli
Cited in the paper.
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models, May 2025b
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