# Ecosystem of Large Language Models for Code

arXiv: https://arxiv.org/abs/2405.16746 · HTML: https://mlsanity.com/paper/2405.16746/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2024.

## Built on by (most cited first, 10 of 10)

- [Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents](https://mlsanity.com/paper/2505.05283/) (2025)
- [Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem](https://mlsanity.com/paper/2512.03073/) (2025)
- [How do Machine Learning Models Change?](https://mlsanity.com/paper/2411.09645/) (2024)
- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning](https://mlsanity.com/paper/2502.14086/) (2025)
- [Towards a Classification of Open-Source ML Models and Datasets for Software Engineering](https://mlsanity.com/paper/2411.09683/) (2024)
- [Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings](https://mlsanity.com/paper/2506.03013/) (2025)
- [Insights into resource utilization of code small language models serving with runtime engines and execution providers](https://mlsanity.com/paper/2412.15441/) (2024)
- [GPT-5 vs Other LLMs in Long Short-Context Performance](https://mlsanity.com/paper/2602.14188/) (2026)
- [ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding](https://mlsanity.com/paper/2606.07524/) (2026)

## Abstract (arXiv)

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.