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. […]

The post Revolutionizing Adapter Techniques: Qualcomm AI’s Sparse High Rank Adapters (SHiRA) for Efficient and Rapid Deployment in Large Language Models appeared first on …

adapter ai paper summary ai shorts applications artificial intelligence challenge deploying deployment editors pick inference language language model language models large language large language model large language models llms lora low machine learning parameters qualcomm qualcomm ai tech news technology