arxiv preprint - MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

In this episode, we discuss MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training by Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, Anton Belyi, Haotian Zhang, Karanjeet Singh, Doug Kang, Hongyu Hè, Max Schwarzer, Tom Gunter, Xiang Kong, Aonan Zhang, Jianyu Wang, Chong Wang, Nan Du, Tao Lei, Sam Wiseman, Mark Lee, Zirui Wang, Ruoming Pang, Peter Grasch, Alexander Toshev, Yinfei Yang. This study investigates how different architectural components and data types impact the performance of Multimodal Large Language Models (MLLMs). The authors discovered that using a combination of different data types is crucial for high performance, and that the design of the image encoder is more influential than the vision-language connector. They applied these insights to create MM1, a series of state-of-the-art multimodal models with up to 30 billion parameters, which excel at few-shot learning and complex reasoning tasks.