PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages
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Abstract (arXiv)
Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as "model hubs" support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult - there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data. We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset. The PTMTorrent dataset (v1) is available at: https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F~%2F. Our dataset generation tools are available on GitHub: https://doi.org/10.5281/zenodo.7570357.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Mar 2023.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 2.
7 papers.
- 2024PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Softwarecited by 9
- 2024What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claimscited by 9
- 2023Contrastive Knowledge Amalgamation for Unsupervised Image Classificationcited by 6
- 2025An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Facecited by 6
- 2025PickleBall: Secure Deserialization of Pickle-based Machine Learning Models (Extended Report)cited by 3
- 2024ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidancecited by 1
- 2024Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosurecited by 0
- 2025PickleBall: Secure Deserialization of Pickle-based Machine Learning Models (Extended Report)21 Aug 2025
- 2025An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face6 Feb 2025
- 2024ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance9 Dec 2024
- 2024Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure27 Sep 2024
- 2024What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims12 Jun 2024
- 2024PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software1 Feb 2024
- 2023Contrastive Knowledge Amalgamation for Unsupervised Image Classification27 Jul 2023