Summary of Mm1.5: Methods, Analysis & Insights From Multimodal Llm Fine-tuning, by Haotian Zhang et al.

MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning

by Haotian Zhang, Mingfei Gao, Zhe Gan, Philipp Dufter, Nina Wenzel, Forrest Huang, Dhruti Shah, Xianzhi Du, Bowen Zhang, Yanghao Li, Sam Dodge, Keen You, Zhen Yang, Aleksei Timofeev, Mingze Xu, Hong-You Chen, Jean-Philippe Fauconnier, Zhengfeng Lai, Haoxuan You, Zirui Wang, Afshin Dehghan, Peter Grasch, Yinfei Yang

First submitted to arxiv on: 30 Sep 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A new family of multimodal large language models (MLLMs), called MM1.5, is designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning. Building upon the MM1 architecture, MM1.5 adopts a data-centric approach to model training, systematically exploring the impact of diverse data mixtures across the entire model training lifecycle. The models range from 1B to 30B parameters, encompassing both dense and mixture-of-experts (MoE) variants. Our results demonstrate that careful data curation and training strategies can yield strong performance even at small scales (1B and 3B). We also introduce two specialized variants: MM1.5-Video, designed for video understanding, and MM1.5-UI, tailored for mobile UI understanding.
Low	GrooveSquid.com (original content)	Low Difficulty Summary MM1.5 is a new kind of AI model that can understand images and text together. It’s good at figuring out what’s in pictures and how it relates to the words describing them. The model was trained on lots of different data sets and shows promise even with smaller amounts of training data. We also created special versions for understanding videos and mobile device user interfaces.