  Multimodal Large Language Models (MLLMs) have showcased impressive skills in
tasks related to visual understanding and reasoning. Yet, their widespread
application faces obstacles due to the high computational demands during both
the training and inference phases, restricting their use to a limited audience
within the research and user communities. In this paper, we investigate the
design aspects of Multimodal Small Language Models (MSLMs) and propose an
efficient multimodal assistant named Mipha, which is designed to create synergy
among various aspects: visual representation, language models, and optimization
strategies. We show that without increasing the volume of training data, our
Mipha-3B outperforms the state-of-the-art large MLLMs, especially
LLaVA-1.5-13B, on multiple benchmarks. Through detailed discussion, we provide
insights and guidelines for developing strong MSLMs that rival the capabilities
of MLLMs. Our code is available at https://github.com/zhuyiche/llava-phi.
