<ul><li>Parameter-Efficient Fine-Tuning (PEFT) methods have become popular for adapting pre-trained Large Language Models (LLMs) to downstream tasks efficiently.</li><li>Existing PEFT techniques usually apply LoRA adapters uniformly across all layers, leading to redundant parameter allocation and suboptimal adaptation efficiency.</li><li>To address these issues, FLoE is introduced as a PEFT framework that utilizes Fisher information and Bayesian optimization for dynamic layer selection and optimal LoRA rank allocation, resulting in impressive efficiency-accuracy trade-offs.</li><li>FLoE is particularly beneficial in resource-constrained environments where rapid adaptation is crucial.</li></ul>

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts

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