Forecasting Open-Weight AI Model Growth on Hugging Face

Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao

Forecasting Open-Weight AI Model Growth on Hugging Face: 12 upvotes on Hugging Face Daily Papers, #17 of 33 papers on 2025-02-25. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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