SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual Inference

Yanming Liu; Xinyue Peng; Ningjing Sang; Yafeng Yan; Xiaolan Ke; Zhiting Zheng; Shaobo Liu; Songhang Deng; Jiannan Cao; Le Dai; Xingzu Liu; Ruilin Nong; Weihao Liu

arXiv · 2403.07088

SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual Inference

Yanming Liu·Xinyue Peng·Ningjing Sang·Yafeng Yan·Xiaolan Ke·Zhiting Zheng·Shaobo Liu·Songhang Deng·Jiannan Cao·Le Dai·Xingzu Liu·Ruilin Nong·Weihao Liu

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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.

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Yanming Liu, Xinyue Peng, Ningjing Sang, Yafeng Yan, Xiaolan Ke, Zhiting Zheng, Shaobo Liu, Songhang Deng, Jiannan Cao, Le Dai, Xingzu Liu, Ruilin Nong, Weihao Liu. 2024-03-11. SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual Inference. https://arxiv.org/abs/2403.07088

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