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Computer Science > Software Engineering

arXiv:2306.00029 (cs)
[Submitted on 31 May 2023]

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

Authors:Nghi D. Q. Bui, Hung Le, Yue Wang, Junnan Li, Akhilesh Deepak
Gotmare, Steven C. H. Hoi
Download a PDF of the paper titled CodeTF: One-stop Transformer
Library for State-of-the-art Code LLM, by Nghi D. Q. Bui and 5 other
authors
Download PDF

    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.

Comments: Ongoing work - Draft Preview
Subjects: Software Engineering (cs.SE); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2306.00029 [cs.SE]
          (or arXiv:2306.00029v1 [cs.SE] for this version)

Submission history

From: Nghi D. Q. Bui [view email]
[v1] Wed, 31 May 2023 05:24:48 UTC (1,324 KB)
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