title: MobileVLM : A Fast, Reproducible and Strong Vision Language Assistant
    for Mobile Devices
summary: 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.
paper_authors._id: 658e36be04cd2b7fd5fd2b6a
paper_authors.name: Xiangxiang Chu
paper_authors._id: 658e36be04cd2b7fd5fd2b6b
paper_authors.name: Limeng Qiao
paper_authors._id: 658e36be04cd2b7fd5fd2b6c
paper_authors.name: Xinyang Lin
paper_authors._id: 658e36be04cd2b7fd5fd2b6d
paper_authors.name: Shuang Xu
paper_authors._id: 658e36be04cd2b7fd5fd2b6e
paper_authors.name: Yang Yang
paper_authors._id: 658e36be04cd2b7fd5fd2b6f
paper_authors.name: Yiming Hu
paper_authors._id: 658e36be04cd2b7fd5fd2b70
paper_authors.name: Fei Wei
paper_authors._id: 658e36be04cd2b7fd5fd2b71
paper_authors.name: Xinyu Zhang
paper_authors._id: 658e36be04cd2b7fd5fd2b72
paper_authors.name: Bo Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-05T08:49:35.308Z
paper_authors._id: 64688d93dcbb937d56b4fbae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64688d93dcbb937d56b4fbae/AHf01DkvhL8p_E9ldwggW.jpeg
paper_authors.fullname: Bo Zhang
paper_authors.type: user
paper_authors.user: boydcheung
paper_authors._id: 658e36be04cd2b7fd5fd2b73
paper_authors.name: Xiaolin Wei
paper_authors._id: 658e36be04cd2b7fd5fd2b74
paper_authors.name: Chunhua Shen
paper_title: MobileVLM : A Fast, Reproducible and Strong Vision Language Assistant
    for Mobile Devices
paper_summary: 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.
paper_ai_summary: MobileVLM, a small-scale multimodal vision language model, achieves competitive performance and superior inference speed on mobile devices compared to larger models.
paper_ai_keywords: multimodal vision language model MMVLM language models CLIP fashion cross-modality interaction projector