MobileVLM : A Fast, Reproducible and Strong Vision Language Assistant
  for Mobile Devices
Authors: name: Xiangxiang Chu, name: Limeng Qiao, name: Xinyang Lin, name: Shuang Xu, name: Yang Yang, name: Yiming Hu, name: Fei Wei, name: Xinyu Zhang, name: Bo Zhang, name: Xiaolin Wei, name: Chunhua Shen

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.