Forecasting Open-Weight AI Model Growth on HuggingFace
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910876537192448 |
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| author | Bhandari, Kushal Raj Chen, Pin-Yu Gao, Jianxi |
| author_facet | Bhandari, Kushal Raj Chen, Pin-Yu Gao, Jianxi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15987 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Open-Weight AI Model Growth on HuggingFace Bhandari, Kushal Raj Chen, Pin-Yu Gao, Jianxi Artificial Intelligence Computers and Society Physics and Society 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. |
| title | Forecasting Open-Weight AI Model Growth on HuggingFace |
| topic | Artificial Intelligence Computers and Society Physics and Society |
| url | https://arxiv.org/abs/2502.15987 |