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GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning
Low-rank adaptation (LoRA) has become a dominant paradigm for parameter-efficient fine-tuning (PEFT) of large-scale deep learning models. However, its bilinear parameterization induces a parameter-dependent geometry: the mapping from trainable parameters to weight updates is not generally distance-p
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