As innovation in deep learning continues, many engineers are incorporating Pre-Trained Models (PTMs) as components in computer systems. Some PTMs are foundation models, and others are fine-tuned variations adapted to different needs. When these PTMs are named well, it facilitates model discovery and reuse. However, prior research has shown that model names are not always well chosen and can sometimes be inaccurate and misleading. The naming practices for PTM packages have not been systematically studied, which hampers engineers' ability to efficiently search for and reliably reuse these models. In this paper, we conduct the first empirical investigation of PTM naming practices in the Hugging Face PTM registry. We begin by reporting on a survey of 108 Hugging Face users, highlighting differences from traditional software package naming and presenting findings on PTM naming practices. The survey results indicate a mismatch between engineers' preferences and current practices in PTM naming. We then introduce DARA, the first automated DNN ARchitecture Assessment technique designed to detect PTM naming inconsistencies. Our results demonstrate that architectural information alone is sufficient to detect these inconsistencies, achieving an accuracy of 94% in identifying model types and promising performance (over 70%) in other architectural metadata as well. We also highlight potential use cases for automated naming tools, such as model validation, PTM metadata generation and verification, and plagiarism detection. Our study provides a foundation for automating naming inconsistency detection. Finally, we envision future work focusing on automated tools for standardizing package naming, improving model selection and reuse, and strengthening the security of the PTM supply chain.

翻译：随着深度学习技术的持续创新，众多工程师将预训练模型（PTMs）作为组件集成到计算机系统中。部分PTMs属于基础模型，另一些则是针对不同需求进行微调的变体。良好的模型命名有助于模型的发现与复用。然而，已有研究表明模型命名并非总是恰当选择，有时可能不够准确甚至产生误导。目前对PTM软件包命名实践缺乏系统性研究，这阻碍了工程师高效搜索和可靠复用这些模型的能力。本文首次对Hugging Face PTM注册库中的命名实践开展实证研究。我们首先报告了对108位Hugging Face用户的调研结果，揭示了其与传统软件包命名的差异，并呈现了关于PTM命名实践的发现。调研结果表明工程师的命名偏好与当前实践存在错配。随后我们提出DARA——首个用于检测PTM命名不一致性的自动化深度神经网络架构评估技术。实验结果表明，仅依靠架构信息即可有效检测此类不一致性，在模型类型识别方面达到94%的准确率，在其他架构元数据识别方面也展现出良好性能（超过70%）。我们进一步阐述了自动化命名工具的潜在应用场景，包括模型验证、PTM元数据生成与校验以及剽窃检测。本研究为自动化命名不一致性检测奠定了基础。最后，我们展望未来研究方向，包括开发标准化软件包命名的自动化工具、改进模型选择与复用机制，以及加强PTM供应链的安全性。