  By harnessing the capabilities of large language models (LLMs), recent large
multimodal models (LMMs) have shown remarkable versatility in open-world
multimodal understanding. Nevertheless, they are usually parameter-heavy and
computation-intensive, thus hindering their applicability in
resource-constrained scenarios. To this end, several lightweight LMMs have been
proposed successively to maximize the capabilities under constrained scale
(e.g., 3B). Despite the encouraging results achieved by these methods, most of
them only focus on one or two aspects of the design space, and the key design
choices that influence model capability have not yet been thoroughly
investigated. In this paper, we conduct a systematic study for lightweight LMMs
from the aspects of model architecture, training strategy, and training data.
Based on our findings, we obtain Imp -- a family of highly capable LMMs at the
2B-4B scales. Notably, our Imp-3B model steadily outperforms all the existing
lightweight LMMs of similar size, and even surpasses the state-of-the-art LMMs
at the 13B scale. With low-bit quantization and resolution reduction
techniques, our Imp model can be deployed on a Qualcomm Snapdragon 8Gen3 mobile
chip with a high inference speed of about 13 tokens/s.
