Revolutionizing Adapter Techniques: Qualcomm AI’s Sparse High Rank Adapters (SHiRA) for Efficient and Rapid Deployment in Large Language Models
A significant challenge in deploying large language models (LLMs) and latent variable models (LVMs) is balancing low inference overhead with the ability to rapidly switch adapters. Traditional methods such as Low Rank Adaptation (LoRA) either fuse adapter parameters into the base model weights, losing rapid switching capability, or maintain adapter parameters separately, incurring significant latency.
Practical Solution: Sparse High Rank Adapters (SHiRA) framework offers a highly sparse adapter solution that modifies only 1-2% of the base model’s weights, enabling rapid switching and mitigating concept loss through its sparse structure.
Value: SHiRA ensures rapid adapter switching and minimal inference overhead, making it highly efficient and practical for deployment in resource-constrained environments such as mobile devices.
SHiRA demonstrates superior performance in extensive experiments on both LLMs and LVMs, consistently outperforming traditional LoRA methods and achieving up to 2.7% higher accuracy in commonsense reasoning tasks.
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