Beyond Visibility and Technical Reuse: Public Application Transformation in Open-Source Model Ecosystems
Software EngineeringSocial and Information Networks
Summary
The authors studied how AI models shared on the Hugging Face platform are not just published, but also turned into public applications called Spaces. They found that only a few models become public apps and these apps are made from a small group of popular models. They also discovered that making an app is different from just reusing a model technically, meaning different measures like downloads or likes show different kinds of impact. Their work helps understand how AI models move from being just files to actual usable applications on the platform.
Authors
Duorong Wang, Xiaoting Wei, Bin Liu, Jiannan Yang
Abstract
Open-source model platforms have made it easier to publish AI models, but model release alone does not reveal whether models become visible, technically reused, or incorporated into public applications. This study introduces public application transformation as a platform-visible dimension of model impact and examines it through structured Model-Space links on Hugging Face. We construct a platform-scale dataset of 2.56 million model repositories, 1.06 million Spaces, 810,087 dataset repositories, and 1.22 million account profiles, together with Model-Space, Dataset-Space, and model-to-model technical reuse links. The analysis shows that public application transformation is highly selective and concentrated: only a small share of models are linked to Spaces, and most Model-Space links are concentrated among a limited set of models. More importantly, application transformation is associated with platform visibility but is not equivalent to technical reuse, indicating that downloads, likes, downstream model reuse, and application-facing uptake capture different forms of model impact. Additional analyses show that application-transformed models tend to exhibit stronger metadata-based readiness and enter heterogeneous Space configurations involving datasets, SDKs, and task-specific application categories. By tracing how models move from repositories into public applications and demos, this study extends the measurement of open-source model impact from artifact availability and technical reuse to platform-mediated transformation across AI information objects.