title: Forecasting Open-Weight AI Model Growth on Hugging Face
summary: 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_authors._id: 67bd46ea3e090b402d70f1f4
paper_authors.name: Kushal Raj Bhandari
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-25T04:30:32.676Z
paper_authors._id: 64dfbcb18e2084e1d7b51b46
paper_authors.avatarUrl: /avatars/fafe30beea2d7e8eec3f3ba985c582f7.svg
paper_authors.fullname: Kushal Raj Bhandari
paper_authors.type: user
paper_authors.user: KBhandari11
paper_authors._id: 67bd46ea3e090b402d70f1f5
paper_authors.name: Pin-Yu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:34:05.126Z
paper_authors._id: 6495dd0b71f6708e0f990032
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6495dd0b71f6708e0f990032/PBIjdKNnpkxvR_3djCGVm.png
paper_authors.fullname: Pin-Yu Chen
paper_authors.type: user
paper_authors.user: pinyuchen
paper_authors._id: 67bd46ea3e090b402d70f1f6
paper_authors.name: Jianxi Gao
paper_title: Forecasting Open-Weight AI Model Growth on Hugging Face
paper_summary: 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_ai_summary: A framework based on citation dynamics tracks and predicts the influence and adoption of open-weight models through parameters of immediacy, longevity, and relative fitness.
paper_ai_keywords: citation dynamics fine-tuned models immediacy longevity relative fitness