The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub

• URL: http://arxiv.org/abs/2405.13058v2
• Date: Wed, 5 Jun 2024 15:28:43 GMT
• Title: The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub
• Authors: Cailean Osborne, Jennifer Ding, Hannah Rose Kirk,
• Abstract summary: We analyse development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. Activity is imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. We find that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers.
• Score: 2.595302141947391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. 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, 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. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. Second, we analyse a snapshot of the social network structure of collaboration in model repositories, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing the isolate developers from the network, 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, activity on the HF Hub is characterised by Pareto distributions, congruent with OSS development patterns on platforms like GitHub. We conclude with recommendations for researchers, companies, and policymakers to advance our understanding of open AI development.

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