Abstract
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.
Community
- Paper π: https://arxiv.org/abs/2502.09619
- Project page π: https://horwitz.ai/model-atlas
- Demo πΊοΈ: https://huggingface.co/spaces/Eliahu/Model-Atlas
In our demo, we showcase a single connected component linking over 100K NLP models, amazing to see how interconnected these LLMs really are π€―
Note: Loading takes ~2 minutes (still in alpha!), but worth the wait!
amazing !!!!
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