# Analyzing the Evolution and Maintenance of ML Models on Hugging Face

arXiv: https://arxiv.org/abs/2311.13380 · HTML: https://mlsanity.com/paper/2311.13380/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability](https://mlsanity.com/paper/2204.06425/) (2022)
- [Is this GitHub Project Maintained? Measuring the Level of Maintenance Activity of Open-Source Projects](https://mlsanity.com/paper/2003.04755/) (2020)

## Built on by (most cited first, 22 of 22)

- [LLM Post-Training: A Deep Dive into Reasoning Large Language Models](https://mlsanity.com/paper/2502.21321/) (2025)
- [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024)
- [Deepfakes on Demand: the rise of accessible non-consensual deepfake image generators](https://mlsanity.com/paper/2505.03859/) (2025)
- [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024)
- [What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims](https://mlsanity.com/paper/2406.08205/) (2024)
- [Lessons Learned from Mining the Hugging Face Repository](https://mlsanity.com/paper/2402.07323/) (2024)
- [An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face](https://mlsanity.com/paper/2502.04484/) (2025)
- [A Large-Scale Study on the Development and Issues of Multi-Agent AI Systems](https://mlsanity.com/paper/2601.07136/) (2026)
- [Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentation](https://mlsanity.com/paper/2503.15222/) (2025)
- [Demystifying Issues, Causes and Solutions in LLM Open-Source Projects](https://mlsanity.com/paper/2409.16559/) (2024)
- [How do Machine Learning Models Change?](https://mlsanity.com/paper/2411.09645/) (2024)
- [Attention to Mamba: A Recipe for Cross-Architecture Distillation](https://mlsanity.com/paper/2604.14191/) (2026)
- [The ATOM Report: Measuring the Open Language Model Ecosystem](https://mlsanity.com/paper/2604.07190/) (2026)
- [Broken on Arrival: Silently Defective LLM Artifacts in Public Model Registries and How to Catch Them](https://mlsanity.com/paper/2609.05881/) (2026)
- [Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings](https://mlsanity.com/paper/2506.03013/) (2025)
- [A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects](https://mlsanity.com/paper/2509.25397/) (2025)
- [How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI](https://mlsanity.com/paper/2602.00056/) (2026)
- [Is Open Source the Future of AI? A Data-Driven Approach](https://mlsanity.com/paper/2501.16403/) (2025)
- [An Empirical Framework for Evaluating Semantic Preservation Using Hugging Face](https://mlsanity.com/paper/2512.07983/) (2025)
- [MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generation](https://mlsanity.com/paper/2604.23539/) (2026)
- [ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery](https://mlsanity.com/paper/2605.16902/) (2026)
- [After the Party: Growth, Governance, and Security Scanning in the OpenClaw Agent Skill Ecosystem](https://mlsanity.com/paper/2609.17274/) (2026)

## Abstract (arXiv)

Hugging Face (HF) has established itself as a crucial platform for the development and sharing of machine learning (ML) models. This repository mining study, which delves into more than 380,000 models using data gathered via the HF Hub API, aims to explore the community engagement, evolution, and maintenance around models hosted on HF, aspects that have yet to be comprehensively explored in the literature. We first examine the overall growth and popularity of HF, uncovering trends in ML domains, framework usage, authors grouping and the evolution of tags and datasets used. Through text analysis of model card descriptions, we also seek to identify prevalent themes and insights within the developer community. Our investigation further extends to the maintenance aspects of models, where we evaluate the maintenance status of ML models, classify commit messages into various categories (corrective, perfective, and adaptive), analyze the evolution across development stages of commits metrics and introduce a new classification system that estimates the maintenance status of models based on multiple attributes. This study aims to provide valuable insights about ML model maintenance and evolution that could inform future model development strategies on platforms like HF.