Open source developers have emerged as key actors in the political economy of
artificial intelligence (AI), with open model development being recognised as
an alternative to closed-source AI development. However, we still have a
limited understanding of collaborative practices in open source AI. This paper
responds to this gap with a three-part quantitative analysis of development
activity on the Hugging Face (HF) Hub, a popular platform for building,
sharing, and demonstrating models. First, we find that various types of
activity across 348,181 model, 65,761 dataset, and 156,642 space repositories
exhibit right-skewed distributions. Activity is extremely imbalanced between
repositories; for example, over 70% of models have 0 downloads, while 1%
account for 99% of downloads. Second, we analyse a snapshot of the social
network structure of collaboration on models, finding that the community has a
core-periphery structure, with a core of prolific developers and a majority of
isolate developers (89%). Upon removing isolates, collaboration is
characterised by high reciprocity regardless of developers' network positions.
Third, we examine model adoption through the lens of model usage in spaces,
finding that a minority of models, developed by a handful of companies, are
widely used on the HF Hub. Overall, we find that various types of activity on
the HF Hub are characterised by Pareto distributions, congruent with prior
observations about OSS development patterns on platforms like GitHub. We
conclude with a discussion of the implications of the findings and
recommendations for (open source) AI researchers, developers, and policymakers.

开源开发者在人工智能（AI）的政治经济中成为关键角色，开放模型开发被认为是封闭源 AI 开发的替代方案。然而，我们对开源 AI 的协作实践仍有有限的了解。本文通过对 Hugging Face（HF） Hub 上的开发活动进行三部分数量分析来填补这一空白，HF Hub 是一个流行的构建、分享和展示模型的平台。