Summary of Molmo and Pixmo: Open Weights and Open Data For State-of-the-art Vision-language Models, by Matt Deitke et al.

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

by Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, Jiasen Lu, Taira Anderson, Erin Bransom, Kiana Ehsani, Huong Ngo, YenSung Chen, Ajay Patel, Mark Yatskar, Chris Callison-Burch, Andrew Head, Rose Hendrix, Favyen Bastani, Eli VanderBilt, Nathan Lambert, Yvonne Chou, Arnavi Chheda, Jenna Sparks, Sam Skjonsberg, Michael Schmitz, Aaron Sarnat, Byron Bischoff, Pete Walsh, Chris Newell, Piper Wolters, Tanmay Gupta, Kuo-Hao Zeng, Jon Borchardt, Dirk Groeneveld, Crystal Nam, Sophie Lebrecht, Caitlin Wittlif, Carissa Schoenick, Oscar Michel, Ranjay Krishna, Luca Weihs, Noah A. Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, Aniruddha Kembhavi

First submitted to arxiv on: 25 Sep 2024

GrooveSquid.com Paper Summaries

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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 The proposed Molmo family of vision-language models (VLMs) achieves state-of-the-art performance in its class of openness, relying on a collection of new datasets called PixMo. The paper presents a well-tuned training pipeline and careful modeling choices to build performant VLMs from scratch. This research outperforms open-weight models that rely heavily on synthetic data from proprietary VLMs, achieving better results than larger proprietary models like Claude 3.5 Sonnet, Gemini 1.5 Pro and Flash, and second only to GPT-4o based on both academic benchmarks and human evaluation.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The researchers created a new family of vision-language models called Molmo that are open-source and perform well. They also made some datasets called PixMo that help these models learn. The team used careful choices when building the models and fine-tuned their training process to get good results. This work is important because it shows how to make good language and image understanding models from scratch, without using proprietary models.