CodeTF: One-stop Transformer Library for State-of-the-art Code LLMs

TL;DR

CodeTF is an open-source, modular Transformer library designed to facilitate the development, training, and deployment of state-of-the-art code language models, bridging the gap between machine learning and software engineering.

Contribution

It introduces a unified, extensible framework with pretrained models and tools for code analysis, addressing barriers in adopting deep learning for code intelligence.

Findings

Supports multiple pretrained Code LLMs and benchmarks

Provides standardized training and serving interfaces

Includes language-specific parsers and code attribute extractors

Abstract

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.