# PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software arXiv: https://arxiv.org/abs/2402.00699 ยท HTML: https://mlsanity.com/paper/2402.00699/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 36 of its 102 references that are in the map. - First posted on arXiv 1 Feb 2024. ## Builds on (most cited first, 30 of 36) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020) - [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021) - [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017) - [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://mlsanity.com/paper/2108.05542/) (2021) - [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (2021) - [Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence](https://mlsanity.com/paper/2002.04803/) (2020) - [Cramming: Training a Language Model on a Single GPU in One Day](https://mlsanity.com/paper/2212.14034/) (2022) - [On Testing Machine Learning Programs](https://mlsanity.com/paper/1812.02257/) (2018) - [Behavioral Use Licensing for Responsible AI](https://mlsanity.com/paper/2011.03116/) (2020) - [A Systematic Survey of Chemical Pre-trained Models](https://mlsanity.com/paper/2210.16484/) (2022) - [ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection](https://mlsanity.com/paper/2106.08890/) (2021) - [Understanding the Factors that Impact the Popularity of GitHub Repositories](https://mlsanity.com/paper/1606.04984/) (2016) - [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023) - [Analyzing the Evolution and Maintenance of ML Models on Hugging Face](https://mlsanity.com/paper/2311.13380/) (2023) - [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021) - [EvilModel 2.0: Bringing Neural Network Models into Malware Attacks](https://mlsanity.com/paper/2109.04344/) (2021) - [Machine Learning Software Engineering in Practice: An Industrial Case Study](https://mlsanity.com/paper/1906.07154/) (2019) - [Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends](https://mlsanity.com/paper/2401.13177/) (2024) - [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023) - [SOTorrent: Reconstructing and Analyzing the Evolution of Stack Overflow Posts](https://mlsanity.com/paper/1803.07311/) (2018) - [Scalpel: The Python Static Analysis Framework](https://mlsanity.com/paper/2202.11840/) (2022) - [Understanding Flaky Tests: The Developer's Perspective](https://mlsanity.com/paper/1907.01466/) (2019) - [Code Smells for Machine Learning Applications](https://mlsanity.com/paper/2203.13746/) (2022) - [What Do Users Ask in Open-Source AI Repositories? An Empirical Study of GitHub Issues](https://mlsanity.com/paper/2303.09795/) (2023) - [PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages](https://mlsanity.com/paper/2303.08934/) (2023) - [Usage and Attribution of Stack Overflow Code Snippets in GitHub Projects](https://mlsanity.com/paper/1802.02938/) (2018) - [What Makes a Popular Academic AI Repository?](https://mlsanity.com/paper/2010.02472/) (2020) ## Built on by (most cited first, 9 of 9) - [What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims](https://mlsanity.com/paper/2406.08205/) (2024) - [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024) - [How do Machine Learning Models Change?](https://mlsanity.com/paper/2411.09645/) (2024) - [Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity](https://mlsanity.com/paper/2602.08816/) (2026) - [From Registry to Repository: How AI Agent Skills Are Written, Adapted, and Maintained](https://mlsanity.com/paper/2607.00911/) (2026) - [An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process](https://mlsanity.com/paper/2603.13584/) (2026) - [AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agents](https://mlsanity.com/paper/2510.03495/) (2025) - [A governance horizon for ethical-use constraints in open-weight AI models](https://mlsanity.com/paper/2605.24383/) (2026) - [The evolution of sex for artificial intelligence: a population-genetic framework for multigenerational model populations](https://mlsanity.com/paper/2609.18560/) (2026) ## Abstract (arXiv) The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset's comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model's training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and cross-cutting questions.