Reusing pre-trained machine learning models is becoming very popular due to model hubs such as Hugging Face (HF). However, similar to when reusing software, many issues may arise when reusing an ML model. In many cases, users resort to asking questions on discussion forums such as the HF community forum. In this paper, we study how we can reduce the community's workload in answering these questions and increase the likelihood that questions receive a quick answer. We analyze 11,278 discussions from the HF model community that contain user questions about ML models. We focus on the effort spent handling questions, the high-level topics of discussions, and the potential for standardizing responses in model cards based on a model card template. Our findings indicate that there is not much effort involved in responding to user questions, however, 40.1% of the questions remain open without any response. A topic analysis shows that discussions are more centered around technical details on model development and troubleshooting, indicating that more input from model providers is required. We show that 42.5% of the questions could have been answered if the model provider followed a standard model card template for the model card. Based on our analysis, we recommend that model providers add more development-related details on the model's architecture, algorithm, data preprocessing and training code in existing documentation (sub)sections and add new (sub)sections to the template to address common questions about model usage and hardware requirements.

Datasets and models are two key artifacts in machine learning (ML) applications. Although there exist tools to support dataset and model developers in managing ML artifacts, little is known about how these datasets and models are integrated into ML applications. In this paper, we study how datasets and models in ML applications are managed. In particular, we focus on how these artifacts are stored and versioned alongside the applications. After analyzing 93 repositories, we identified the most common storage location to store datasets and models is the file system, which causes availability issues. Notably, large data and model files, exceeding approximately 60 MB, are stored exclusively in remote storage and downloaded as needed. Most of the datasets and models lack proper integration with the version control system, posing potential trace-ability and reproducibility issues. Additionally, although datasets and models are likely to evolve during the application development, they are rarely updated in application repositories.