We Should Chart an Atlas of All the World's Models

https://horwitz.ai/model-atlas There are now millions of publicly available models We need to chart the Model Atlas. It enables model forensics, meta-ML research, and model discovery In practice, the information for most models is missing. We need methods to learn from model populations Figure 1: Position overview: With millions of public models, it becomes important to move beyond individual models and study entire populations (left). The Model Atlas formalizes this shift by representing models as nodes in a graph, with directed edges denoting weight transformations (e.g., fine-tuning). Node size and color, as well as edge color, encode node and edge-level features; light blue indicates missing or unknown information. The atlas enables a range of applications, including model forensics, meta-ML research, and model discovery (center). In practice, most edges and features are unknown. This motivates ML methods that take models as input and infer their properties, thereby completing the missing atlas regions (right). Zoom in to view edges, best viewed in color.