In plain words: CodeTF is a free library with one interface for loading code-focused language models, datasets, and tasks, plus code-reading tools that extract code structure. Unlike other code-AI tools that handle only part of the workflow, it bundles ready-made models, benchmarks, training, serving, and code parsing.
Abstract · CodeTF: One-stop Transformer Library for State-of-the-art Code LLMs
Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. However, the development and deployment of such models often require expertise in both machine learning and software engineering, creating a barrier for the model adoption. In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Following the principles of modular design and extensible framework, we design CodeTF with a unified interface to enable rapid access and development across different types of models, datasets and tasks. Our library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes. In this paper, we describe the design principles, the architecture, key modules and components, and compare with other related library tools. Finally, we hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering, providing a comprehensive open-source solution for developers, researchers, and practitioners.
Nghi D. Q. Bui, Hung Le, Yue Wang, Junnan Li, Akhilesh Deepak Gotmare, Steven C. H. Hoi
arXiv:2306.00029 · cs.SE, cs.AI · submitted May 31, 2023 · updated Dec 22, 2025
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It would be helpful to see some Colab notebook examples of how I could use this or incorporate my own codebase with these open source coding models.
The examples show some smaller interesting prediction task & translation between csharp and java, but probably easier to try it out in Colab than having to install locally.
I would also want to be able to compare Github Copilot's autocomplete with what CodeT5 would return as a prediction.