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

**Authors:** 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  
**arXiv:** [2403.09611](https://arxiv.org/abs/2403.09611)  
**Date:** 2024-03-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.09611v1/

## Key points

- Development of high-performing Multimodal Large Language Models (MLLMs) through careful architectural design and data selection strategies
- Importance of strategic blend of image-captioned data with interleaved image-text and text-only data for achieving state-of-the-art few-shot results
- Introduction of MM1, a series of multimodal models with up to 30 billion parameters, as an upscaled version of the proposed methodology
- Leveraging large-scale pre-training to exhibit enhanced in-context learning and multi-image reasoning for prompt few-shot chain-of-thought prompting scenarios
- Valuable insights into building effective MLLMs and advancing multimodal language modeling research

## Summary

The paper "MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training" by Brandon McKinzie et al. explores the development of high-performing Multimodal Large Language Models (MLLMs) through careful architectural design and data selection strategies. Through ablations of the image encoder and vision-language connector with different pre-training data choices, critical insights were uncovered. The study highlights the importance of a strategic blend of image-captioned data with interleaved image-text and text-only data for achieving state-of-the-art few-shot results. MM1 - a series of multimodal models comprising up to 30 billion parameters - was introduced as an upscaled version of their proposed methodology. Leveraging large-scale pre-training, MM1 exhibits desirable attributes such as enhanced in-context learning and multi-image reasoning for prompt few-shot chain-of-thought prompting scenarios. This comprehensive analysis offers valuable insights into building effective MLLMs and advances multimodal language modeling research.