超越可见性和技术复用：开源模型生态系统中的公共应用转型
Beyond Visibility and Technical Reuse: Public Application Transformation in Open-Source Model Ecosystems
中文总结 AI 辅助
研究开源模型在平台上的公共应用转型，通过构建大规模数据集及多种链接进行分析，发现其具有选择性和集中性，与平台可见性相关但不等同于技术复用，还揭示了转型模型的特点，扩展了开源模型影响的测量范围。
中文摘要 AI 辅助
开源模型平台使AI模型发布更便捷，但模型发布本身无法揭示模型是否变得可见、在技术上被复用或融入公共应用。本研究引入公共应用转型作为模型影响的平台可见维度，并通过Hugging Face上的结构化模型空间链接进行考察。构建了一个包含256万个模型存储库、106万个空间、810,087个数据集存储库和122万个账户档案的平台规模数据集，以及模型-空间、数据集-空间和模型到模型的技术复用链接。分析表明，公共应用转型具有高度选择性和集中性：只有一小部分模型与空间链接，且大多数模型-空间链接集中在有限的一组模型中。更重要的是，应用转型与平台可见性相关，但不等同于技术复用，这表明下载、点赞、下游模型复用和面向应用的采用捕捉了不同形式的模型影响。进一步分析表明，应用转型的模型往往表现出更强的基于元数据的就绪性，并进入涉及数据集、软件开发工具包和特定任务应用类别的异构空间配置。通过追踪模型如何从存储库进入公共应用和演示，本研究将开源模型影响的测量从工件可用性和技术复用扩展到跨AI信息对象的平台介导转型。
英文摘要
Open-source model platforms have made it easier to publish AI models, but model release alone does not reveal whether models become visible, technically reused, or incorporated into public applications. This study introduces public application transformation as a platform-visible dimension of model impact and examines it through structured Model-Space links on Hugging Face. We construct a platform-scale dataset of 2.56 million model repositories, 1.06 million Spaces, 810,087 dataset repositories, and 1.22 million account profiles, together with Model-Space, Dataset-Space, and model-to-model technical reuse links. The analysis shows that public application transformation is highly selective and concentrated: only a small share of models are linked to Spaces, and most Model-Space links are concentrated among a limited set of models. More importantly, application transformation is associated with platform visibility but is not equivalent to technical reuse, indicating that downloads, likes, downstream model reuse, and application-facing uptake capture different forms of model impact. Additional analyses show that application-transformed models tend to exhibit stronger metadata-based readiness and enter heterogeneous Space configurations involving datasets, SDKs, and task-specific application categories. By tracing how models move from repositories into public applications and demos, this study extends the measurement of open-source model impact from artifact availability and technical reuse to platform-mediated transformation across AI information objects.
