Forecasting Open-Weight AI Model Growth on Hugging Face
Authors: name: Kushal Raj Bhandari, name: Pin-Yu Chen, name: Jianxi Gao

As the open-weight AI landscape continues to proliferate-with model
development, significant investment, and user interest-it becomes increasingly
important to predict which models will ultimately drive innovation and shape AI
ecosystems. Building on parallels with citation dynamics in scientific
literature, we propose a framework to quantify how an open-weight model's
influence evolves. Specifically, we adapt the model introduced by Wang et al.
for scientific citations, using three key parameters-immediacy, longevity, and
relative fitness-to track the cumulative number of fine-tuned models of an
open-weight model. Our findings reveal that this citation-style approach can
effectively capture the diverse trajectories of open-weight model adoption,
with most models fitting well and outliers indicating unique patterns or abrupt
jumps in usage.