  The availability of vast amounts of publicly accessible data of source code
and the advances in modern language models, coupled with increasing
computational resources, have led to a remarkable surge in the development of
large language models for code (LLM4Code, for short). The interaction between
code datasets and models gives rise to a complex ecosystem characterized by
intricate dependencies that are worth studying. This paper introduces a
pioneering analysis of the code model ecosystem. Utilizing Hugging Face -- the
premier hub for transformer-based models -- as our primary source, we curate a
list of datasets and models that are manually confirmed to be relevant to
software engineering. By analyzing the ecosystem, we first identify the popular
and influential datasets, models, and contributors. The popularity is
quantified by various metrics, including the number of downloads, the number of
likes, the number of reuses, etc. The ecosystem follows a power-law
distribution, indicating that users prefer widely recognized models and
datasets. Then, we manually categorize how models in the ecosystem are reused
into nine categories, analyzing prevalent model reuse practices. The top 3 most
popular reuse types are fine-tuning, architecture sharing, and quantization. We
also explore the practices surrounding the publication of LLM4Code,
specifically focusing on documentation practice and license selection. We find
that the documentation in the ecosystem contains less information than that in
general artificial intelligence (AI)-related repositories hosted on GitHub.
Additionally, the license usage is also different from other software
repositories. Models in the ecosystem adopt some AI-specific licenses, e.g.,
RAIL (Responsible AI Licenses) and AI model license agreement.
