  Hugging Face (HF) has established itself as a crucial platform for the
development and sharing of machine learning (ML) models. This repository mining
study, which delves into more than 380,000 models using data gathered via the
HF Hub API, aims to explore the community engagement, evolution, and
maintenance around models hosted on HF, aspects that have yet to be
comprehensively explored in the literature. We first examine the overall growth
and popularity of HF, uncovering trends in ML domains, framework usage, authors
grouping and the evolution of tags and datasets used. Through text analysis of
model card descriptions, we also seek to identify prevalent themes and insights
within the developer community. Our investigation further extends to the
maintenance aspects of models, where we evaluate the maintenance status of ML
models, classify commit messages into various categories (corrective,
perfective, and adaptive), analyze the evolution across development stages of
commits metrics and introduce a new classification system that estimates the
maintenance status of models based on multiple attributes. This study aims to
provide valuable insights about ML model maintenance and evolution that could
inform future model development strategies on platforms like HF.
