Summary of Ml-mamba: Efficient Multi-modal Large Language Model Utilizing Mamba-2, by Wenjun Huang et al.
ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2
by Wenjun Huang, Jiakai Pan, Jiahao Tang, Yanyu Ding, Yifei Xing, Yuhe Wang, Zhengzhuo Wang, Jianguo Hu
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces ML-Mamba, a novel multimodal large language model that utilizes the efficient Mamba-2 model for inference. The authors replace the traditional Transformer backbone with a pre-trained Mamba-2 model and explore methods for integrating 2D visual selective scanning mechanisms into multimodal learning. They propose a novel multimodal connector called the Mamba-2 Scan Connector (MSC) which enhances representational capabilities. The experiments demonstrate the competitive performance of ML-Mamba in various multimodal benchmark tests, achieving comparable results to state-of-the-art models such as TinyLaVA and MobileVLM v2 while offering faster inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new language model that can understand and process both text and images. The old way of building these models was slow and took up too many computer resources, so the researchers came up with a new approach using the Mamba-2 model. They also developed a special connector that helps the model learn from both texts and images better. The results show that their new model, called ML-Mamba, is as good as other top models but can process information much faster. |
Keywords
» Artificial intelligence » Inference » Language model » Large language model » Transformer
