  Multimodal Large Language Models (MLLMs) have attracted much attention due to
their multifunctionality. However, traditional Transformer architectures incur
significant overhead due to their secondary computational complexity. To
address this issue, we introduce ML-Mamba, a multimodal language model that
utilizes the latest and efficient Mamba-2 model for inference. Mamba-2 is known
for its linear extension and fast processing of long sequences. We replace the
Transformer based backbone with a pre-trained Mamba-2 model and explore methods
for integrating 2D visual selective scanning mechanisms into multimodal
learning. We also try various visual encoders and Mamba-2 model variants. Our
extensive experiments conducted in various multimodal benchmark tests have
demonstrated the competitive performance of ML-Mamba and highlighted the
potential of state space models in multimodal tasks. The experimental results
show that: (1) ML-Mamba achieves performance comparable to state-of-the-art
methods such as TinyLaVA and MobileVLM v2 through its linear sequential
modeling, while also having faster inference speed; (2) ML-Mamba performs well
in visual hallucinations and spatial relationship judgment in closed set
benchmark tests; (3) ML-Mamba achieves performance comparable to LLaVA while
reducing the number of parameters by 40\%.(4) Compared to the multimodal model
using the original Mamba model, the Mamba-2 based large-scale multimodal
language model has stronger inference performance and effectiveness.
