Papers › Imp: Highly Capable Large Multimodal Models for Mobile Devices
Imp: Highly Capable Large Multimodal Models for Mobile Devices
Zhenwei Shao, Zhou Yu, Jun Yu, Xuecheng Ouyang, Lihao Zheng, Zhenbiao Gai, Mingyang Wang, Jiajun Ding
By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-heavy and computation-intensive, thus hindering their applicability in resource-constrained scenarios. To this end, several lightweight LMMs have been proposed successively to maximize the capabilities under constrained scale (e.g., 3B). Despite the encouraging results achieved by these methods, most of them only focus on one or two aspects of the design space, and the key design choices that influence model capability have not yet been thoroughly investigated. In this paper, we conduct a systematic study for lightweight LMMs from the aspects of model architecture, training strategy, and training data. Based on our findings, we obtain Imp -- a family of highly capable LMMs at the 2B-4B scales. Notably, our Imp-3B model steadily outperforms all the existing lightweight LMMs of similar size, and even surpasses the state-of-the-art LMMs at the 13B scale. With low-bit quantization and resolution reduction techniques, our Imp model can be deployed on a Qualcomm Snapdragon 8Gen3 mobile chip with a high inference speed of about 13 tokens/s.
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Code
Syntology Ran 6 of 7 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 3 ran · our draft was wrong; 1 ran with no contract checked.
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Code Syntology ran Syntology
7 samples harvested; 6 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering | MM-Vet | Imp-4B | GPT-4 score | 44.6 | #87 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Imp-4B | Params | 4B | #87 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Imp-3B | GPT-4 score | 43.3 | #95 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Imp-3B | Params | 3B | #95 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Imp-2B | GPT-4 score | 33.5 | #173 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Imp-2B | Params | 2B | #173 of 231 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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