SlimLM: An Efficient Small Language Model for On-Device Document
  Assistance
Authors: name: Thang M. Pham, name: Phat T. Nguyen, name: Seunghyun Yoon, name: Viet Dac Lai, name: Franck Dernoncourt, name: Trung Bui

While small language models (SLMs) show promises for mobile deployment, their
real-world performance and applications on smartphones remains underexplored.
We present SlimLM, a series of SLMs optimized for document assistance tasks on
mobile devices. Through extensive experiments on a Samsung Galaxy S24, we
identify the optimal trade-offs between model size (ranging from 125M to 7B
parameters), context length, and inference time for efficient on-device
processing. SlimLM is pre-trained on SlimPajama-627B and fine-tuned on
DocAssist, our constructed dataset for summarization, question answering and
suggestion tasks. Our smallest model demonstrates efficient performance on S24,
while larger variants offer enhanced capabilities within mobile constraints. We
evaluate SlimLM against existing SLMs, showing comparable or superior
performance and offering a benchmark for future research in on-device language
models. We also provide an Android application, offering practical insights
into SLM deployment. Our findings provide valuable insights and illuminate the
capabilities of running advanced language models on high-end smartphones,
potentially reducing server costs and enhancing privacy through on-device
processing.