Forecasting Open-Weight AI Model Growth on Hugging Face

Kushal Raj Bhandari	Pin-Yu Chen	Jianxi Gao
Department of Computer Science	IBM Research	Department of Computer Science
Rensselaer Polytechnic Institute	Yorktown Heights, NY, USA	Rensselaer Polytechnic Institute
Troy, NY, USA		Troy, NY, USA

Paper Link

Abstract

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.

Highlights

📈 Given the initial 10 days of downloads, we predict that the DeepSeek-R1 model will exceed 14 billion downloads within 100 days of release.

🔥 While Llama2-7B models are reaching saturation with number of finetuned models, Llama-3.1-8B models are projected to have double the number of finetuned models within next year.

⏳ MistralAI's models show unique adoption trajectory with stepwise increament, but steady growth signaling long-term viability.

📊 In general, number of models released by each companies are growing significantly.

Select Company: Select Metric: Select Model:

Cumulative Number of Finetuned Models: Monthly cumulative number of fine-tuned models released after the base-model is released
Cumulative Number of Downloads: Daily cumulative number of downloads for models released after Sept 02, 2024.

Reference

        @online{ bhandari2024forecasting,
          Author = {Bhandari, Kushal Raj and Chen, Pin-Yu and Gao, Jianxi},
          Title = {Forecasting Open-Weight AI Model Growth on Hugging Face},
          Year = {2025},
          Eprint = = {2502.15987},
          Eprinttype = {arXiv},          
}

*data collected from September 02, 2024, until February 18, 2025.