Example code in Hugging Face model cards serves as an important entry point for users to understand and adopt pretrained models; however, during execution, users frequently encounter various errors, which in turn undermine reproducibility and practical usability. Given the long-standing lack of systematic evaluation of this phenomenon, we conduct a large-scale empirical study on the executability of model-card example code. Our results show that, among 291 model cards, 65.98% of example code fails to execute successfully, indicating substantial limitations in real-world usability. Further analysis reveals that failure rates increase with model size and vary significantly across task types, with speech, vision, multimodal, and generative tasks being particularly fragile. Based on execution logs, we identify three dominant root causes: insufficient documentation of dependency and version requirements, implicit runtime environment assumptions embedded in example code, and incompatibilities induced by ecosystem evolution (e.g., API changes). This study provides a preliminary empirical evidence for understanding the effectiveness of model-card examples in real-world usage scenarios.