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.

我们提出了MobileVLM，这是一个专为移动设备设计的多模式视觉语言模型（MMVLM）。它集成了各种移动设备定向的架构设计和技术，包括一组从头训练的1.4B和2.7B参数规模的语言模型，以及使用CLIP风格预训练的多模式视觉模型，通过高效的投影实现跨模态交互。我们在几个典型的VLM基准测试上评估了MobileVLM。与一些更大的模型相比，我们的模型表现出与之相当的性能。更重要的是，我们在高通骁龙888 CPU和NVIDIA Jeston Orin GPU上测量了推断速度，分别获得了21.5个token和65.3个token每秒的最新性能。我们的代码将在此https URL上提供。

MobileVLM: 移动设备上快速、可重现和强大的视觉语言助手