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.

翻译：随着开源权重AI领域的持续扩张——包括模型开发、重大投资和用户兴趣的激增——预测哪些模型将最终驱动创新并塑造AI生态系统变得日益重要。借鉴科学文献引用动态的相似性，我们提出了一个量化开源权重模型影响力演变的框架。具体而言，我们改编了Wang等人为科学引用提出的模型，利用三个关键参数——即时性、持久性和相对适应度——来追踪一个开源权重模型的微调模型累计数量。我们的研究结果表明，这种引用风格的方法能有效捕捉开源权重模型采纳的多样化轨迹，大多数模型拟合良好，而异常值则揭示了独特的使用模式或使用量的突然跃升。