A single LoRA adapter placed at the gradient-energy-dominant shallow FFN module outperforms distributed LoRA across instruction, math, code, and conversation tasks.
Layer-wise importance matters: Less memory for better performance in parameter-efficient fine-tuning of large language models
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Rethinking Adapter Placement: A Dominant Adaptation Module Perspective
A single LoRA adapter placed at the gradient-energy-dominant shallow FFN module outperforms distributed LoRA across instruction, math, code, and conversation tasks.