# Forecasting Open-Weight AI Model Growth on HuggingFace

arXiv: https://arxiv.org/abs/2502.15987 · HTML: https://mlsanity.com/paper/2502.15987/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models](https://mlsanity.com/paper/2312.14751/) (2023)

## Built on by (most cited first, 3 of 3)

- [Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter](https://mlsanity.com/paper/2605.11685/) (2026)
- [Selecting Language Models for Social Science: Start Small, Start Open, and Validate](https://mlsanity.com/paper/2601.10926/) (2026)
- [TensorDex: A Compact, Tensor-Centric Storage System for Modern AI Models](https://mlsanity.com/paper/2604.17104/) (2026)

## Abstract (arXiv)

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.