HuggingGraph: Understanding the Supply Chain of LLM Ecosystem

Large language models (LLMs) leverage deep learning architectures to process and predict sequences of words based on context, enabling them to perform a wide range of natural language processing tasks, such as translation, summarization, question answering, and content generation. However, the increasing size and complexity of developing, training, and deploying cutting-edge LLMs demand extensive computational resources and large-scale datasets. This creates a significant barrier for researchers and practitioners. Because of that, platforms that host models and datasets have gained widespread popularity. For example, on one of the most popular platforms, i.e., Hugging Face, there are more than 1.8 million models and more than 450K datasets by the end of June 2025, and the trend does not show any slowdown.