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

About

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.

Xiangxiang Chu, Limeng Qiao, Xinyang Lin, Shuang Xu, Yang Yang, Yiming Hu, Fei Wei, Xinyu Zhang, Bo Zhang, Xiaolin Wei, Chunhua Shen• 2023

Related benchmarks

TaskDatasetResultRank
Object Hallucination EvaluationPOPE
Accuracy84.9
1455
Visual Question AnsweringVQA v2
Accuracy59
1362
Visual Question AnsweringTextVQA
Accuracy47.5
1285
Visual Question AnsweringGQA
Accuracy59
1249
Multimodal EvaluationMME
Score1.30e+3
658
Multimodal UnderstandingMMBench--
637
Visual Question AnsweringGQA
Accuracy59.03
505
Science Question AnsweringScienceQA--
502
Multimodal UnderstandingSEED-Bench
Accuracy59
343
Visual Question AnsweringTextVQA (val)
VQA Score47.5
343
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Code

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