MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Xiangxiang Chu; Limeng Qiao; Xinyang Lin; Shuang Xu; Yang Yang; Yiming Hu; Fei Wei; Xinyu Zhang; Bo Zhang; Xiaolin Wei; Chunhua Shen

arXiv · 2312.16886

MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Xiangxiang Chu·Limeng Qiao·Xinyang Lin·Shuang Xu·Yang Yang·Yiming Hu·Fei Wei·Xinyu Zhang·Bo Zhang·Xiaolin Wei·Chunhua Shen

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Abstract

We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models at the scale of 1.4B and 2.7B parameters, trained from scratch, a multimodal vision model that is pre-trained in the CLIP fashion, cross-modality interaction via an efficient projector. We evaluate MobileVLM on several typical VLM benchmarks. Our models demonstrate on par performance compared with a few much larger models. More importantly, we measure the inference speed on both a Qualcomm Snapdragon 888 CPU and an NVIDIA Jeston Orin GPU, and we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively. Our code will be made available at: https://github.com/Meituan-AutoML/MobileVLM.

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Xiangxiang Chu, Limeng Qiao, Xinyang Lin, Shuang Xu, Yang Yang, Yiming Hu, Fei Wei, Xinyu Zhang, Bo Zhang, Xiaolin Wei, Chunhua Shen. 2023-12-30. MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices. https://arxiv.org/abs/2312.16886

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