SlimLM: An Efficient Small Language Model for On-Device Document
  Assistance
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.
673c3346955070e4cfcac54f
Thang M. Pham
admin_assigned
2024-11-19T15:25:53.374Z
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/avatars/d043adb5a89023eb6ef01c2ec457e8ca.svg
Thang M. Pham
user
pmthangk09
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Phat T. Nguyen
673c3346955070e4cfcac551
Seunghyun Yoon
claimed_verified
2024-11-20T10:09:04.313Z
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David Seunghyun Yoon
user
mysmilesh
673c3346955070e4cfcac552
Viet Dac Lai
extracted_pending
2025-07-25T17:07:20.746Z
6883b98e3d4817f5c4361b4b
/avatars/48a475ab1c1208dfe870d376424363f6.svg
SlimLM-VLM
user
slimlmvlm
673c3346955070e4cfcac553
Franck Dernoncourt
extracted_confirmed
2024-11-19T06:43:47.566Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
673c3346955070e4cfcac554
Trung Bui
claimed_verified
2024-11-20T10:09:02.636Z
6036eb46413a78f892e733a0
/avatars/b15925f89631323c176ecbd0eab99feb.svg
Trung Bui
user
Trung
SlimLM: An Efficient Small Language Model for On-Device Document
  Assistance
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.
SlimLM, a series of small language models optimized for document assistance tasks on mobile devices, demonstrates efficient performance and enhanced capabilities within size and time constraints, providing a benchmark for on-device language models.
small language models document assistance tasks model size context length inference time SlimPajama-627B DocAssist summarization question answering suggestion tasks