Imp: Highly Capable Large Multimodal Models for Mobile Devices

TL;DR

This paper introduces Imp, a family of highly capable lightweight large multimodal models (LMMs) at 2B-4B scale, optimized for mobile devices, outperforming similar-sized models and rivaling larger models in capability.

Contribution

The paper systematically studies design choices for lightweight LMMs and develops Imp, a new family of models that balance performance and efficiency for mobile deployment.

Findings

Imp-3B outperforms similar-sized lightweight LMMs.

Imp models surpass state-of-the-art 13B LMMs in capability.

Imp can run on mobile hardware with high inference speed.

Abstract

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.