Arxiv Paper - NVLM: Open Frontier-Class Multimodal LLMs

In this episode, we discuss NVLM: Open Frontier-Class Multimodal LLMs by Wenliang Dai, Nayeon Lee, Boxin Wang, Zhuolin Yang, Zihan Liu, Jon Barker, Tuomas Rintamaki, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping. The paper introduces NVLM 1.0, a set of advanced multimodal large language models that achieve state-of-the-art performance on vision-language tasks and improve upon their text-only capabilities. It outlines the benefits of a novel architecture that enhances training efficiency and reasoning abilities using a 1-D tile-tagging design, emphasizing the importance of dataset quality and task diversity over scale. NVLM 1.0's models excel in multimodal and text-only tasks through the integration of high-quality data, and the model weights are released with plans to open-source the training code.