  Recently, several approaches successfully demonstrated that weight-sharing
Neural Architecture Search (NAS) can effectively explore a search space of
elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning
(PEFT) and compression of large language models. In this paper, we introduce a
novel approach called Shears, demonstrating how the integration of
cost-effective sparsity and a proposed Neural Low-rank adapter Search (NLS)
algorithm can further improve the efficiency of PEFT approaches. Results
demonstrate the benefits of Shears compared to other methods, reaching high
sparsity levels while improving or with little drop in accuracy, utilizing a
single GPU for a pair of hours.
