# Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends

arXiv: https://arxiv.org/abs/2401.13177 · HTML: https://mlsanity.com/paper/2401.13177/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 66 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023)
- [Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs](https://mlsanity.com/paper/2110.10545/) (2021)
- [Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards](https://mlsanity.com/paper/2108.07374/) (2021)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Models](https://mlsanity.com/paper/2203.10965/) (2022)
- [Autonomy Is An Acquired Taste: Exploring Developer Preferences for GitHub Bots](https://mlsanity.com/paper/2302.05048/) (2023)
- [The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions](https://mlsanity.com/paper/2108.09905/) (2021)
- [An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models](https://mlsanity.com/paper/2212.00105/) (2022)

## Built on by (most cited first, 12 of 12)

- [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)
- [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)
- [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)
- [Broken on Arrival: Silently Defective LLM Artifacts in Public Model Registries and How to Catch Them](https://mlsanity.com/paper/2609.05881/) (2026)
- [NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models](https://mlsanity.com/paper/2508.11348/) (2025)
- [Exploring and Leveraging Class Vectors for Classifier Editing](https://mlsanity.com/paper/2510.11268/) (2025)
- [Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?](https://mlsanity.com/paper/2512.23385/) (2025)
- [Attesting Model Lineage by Consisted Knowledge Evolution with Fine-Tuning Trajectory](https://mlsanity.com/paper/2601.11683/) (2026)
- [Novel Deep Neural Network Classifier Characterization Metrics with Applications to Dataless Evaluation](https://mlsanity.com/paper/2407.13000/) (2024)
- [Automatic Model Card Generation Using an LLM](https://mlsanity.com/paper/2608.24807/) (2026)

## Abstract (arXiv)

The ubiquity of large-scale Pre-Trained Models (PTMs) is on the rise, sparking interest in model hubs, and dedicated platforms for hosting PTMs. Despite this trend, a comprehensive exploration of the challenges that users encounter and how the community leverages PTMs remains lacking. To address this gap, we conducted an extensive mixed-methods empirical study by focusing on discussion forums and the model hub of HuggingFace, the largest public model hub. Based on our qualitative analysis, we present a taxonomy of the challenges and benefits associated with PTM reuse within this community. We then conduct a quantitative study to track model-type trends and model documentation evolution over time. Our findings highlight prevalent challenges such as limited guidance for beginner users, struggles with model output comprehensibility in training or inference, and a lack of model understanding. We also identified interesting trends among models where some models maintain high upload rates despite a decline in topics related to them. Additionally, we found that despite the introduction of model documentation tools, its quantity has not increased over time, leading to difficulties in model comprehension and selection among users. Our study sheds light on new challenges in reusing PTMs that were not reported before and we provide recommendations for various stakeholders involved in PTM reuse.