A single LoRA adapter placed at the gradient-energy-dominant shallow FFN module outperforms distributed LoRA across instruction, math, code, and conversation tasks.
Train more parameters but mind their placement: Insights into language adaptation with PEFT
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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.