Charting and Navigating Hugging Face's Model Atlas
Authors: name: Eliahu Horwitz, name: Nitzan Kurer, name: Jonathan Kahana, name: Liel Amar, name: Yedid Hoshen

As there are now millions of publicly available neural networks, searching
and analyzing large model repositories becomes increasingly important.
Navigating so many models requires an atlas, but as most models are poorly
documented charting such an atlas is challenging. To explore the hidden
potential of model repositories, we chart a preliminary atlas representing the
documented fraction of Hugging Face. It provides stunning visualizations of the
model landscape and evolution. We demonstrate several applications of this
atlas including predicting model attributes (e.g., accuracy), and analyzing
trends in computer vision models. However, as the current atlas remains
incomplete, we propose a method for charting undocumented regions.
Specifically, we identify high-confidence structural priors based on dominant
real-world model training practices. Leveraging these priors, our approach
enables accurate mapping of previously undocumented areas of the atlas. We
publicly release our datasets, code, and interactive atlas.