arXiv:2312.16886cs.CV
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
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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.
MobileVLM [1] is another recent small multimodal model. They trained their own 1.4B/2.7B LLaMa from scratch using RedPajama and Vicuna instead of leveraging Phi-2. The papers only have one common benchmark (GQA, MobileVLM scores better) so hard to say how they compare otherwise.…
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