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.

翻译：开源开发者已成为人工智能政治经济学中的关键行动者，开放模型开发被视为闭源AI开发的替代路径。然而，我们对开源AI领域的协作实践仍缺乏深入理解。本文通过对主流模型构建、共享与演示平台Hugging Face Hub的三维度量化分析来填补这一空白。首先，我们基于348,181个模型、65,761个数据集及156,642个空间仓库的分析发现，各类活动均呈现右偏分布。仓库间活动存在极端不平衡：例如超过70%的模型下载量为零，而1%的模型贡献了99%的下载量。其次，通过模型协作社交网络的结构快照分析，我们发现社区呈现核心-边缘结构：核心由高产开发者构成，而孤立开发者占比达89%。剔除孤立节点后，无论开发者在网络中的位置如何，协作均表现出高度互惠性。再次，通过空间中的模型使用情况考察模型采纳度，我们发现少数由个别公司开发的模型在HF Hub上被广泛使用。总体而言，HF Hub上各类活动均呈现帕累托分布特征，这与GitHub等平台开源软件开发模式的既有观察相符。最后，我们探讨了研究发现的启示，并为（开源）AI研究者、开发者及政策制定者提出建议。