SPA: Towards a Computational Friendly Cloud-Base and on-Devices Collaboration Seq2seq Personalized Generation with Causal Inference

EasyChair Preprint 15343, version 2

Abstract

Large language models(LLMs) have shown its outperforming ability on various tasks and question answering. However, LLMs require substantial memory storage on low-resource devices. More critically, the computational speed on these devices is also severely limited. In this paper, we propose SPA(Side Plugin Adaption), a lightweight architecture for fast on-devices inference on the constraints of strict on-devices computation and memory constraints. Compared with other on-devices seq2seq generation, SPA could make a fast and stable inference on low-resource constraints, allowing it to obtain cost effiency. Our method establish an interaction between a pretrained LLMs on-cloud and additive parameters on-devices, which could provide the knowledge on both pretrained LLMs and featured personal feature. Further more, SPA provides a framework to keep feature-base parameters on low computational devices while leave the parameters containing general information on the high computational devices.

Keyphrases: Cloud-device Collaboration, Personalized LLM, inference acceleration

@booklet{EasyChair:15343,
  author    = {Yanming Liu and Xinyue Peng and Shi Bo and Ningjing Sang and Yafeng Yan and Xiaolan Ke and Zhiting Zheng and Shaobo Liu and Songhang Deng and Jiannan Cao and Le Dai and Xingzu Liu and Ruilin Nong and Weihao Liu},
  title     = {SPA: Towards a Computational Friendly Cloud-Base and on-Devices Collaboration Seq2seq Personalized Generation with Causal Inference},
  howpublished = {EasyChair Preprint 15343},
  year      = {EasyChair, 2024}}