Comparative Analysis and Optimization of LoRA Adapter Co-serving for Large Language Models
Jiaxuan Chen · 2024
Large language models (LLMs) are widely deployed across a range of applications, but their efficient use for serving diverse tasks remains a challenge due to the significant resource demands of running numerous specialized models. Parameter-efficient fine-tuning (PEFT) addresses this by allowing models to be adapted using only a small set of additional parameters, leaving the backbone model unchanged.