Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach

TL;DR

This paper introduces a fine-tuning approach for an open-source LLM-based tool that generates customizable code documentation, addressing cost and flexibility limitations of existing solutions.

Contribution

It presents a novel fine-tuning method for open-source LLMs to generate customizable code documentation, overcoming cost and data availability challenges.

Findings

Provides a fine-tuned LLM model for documentation generation

Reduces reliance on costly API-based solutions

Enables user customization of generated documentation

Abstract

Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use as a support tool to generate basic documentation for any publicly available repository. Over the last decade, several papers have been written on generating documentation for source code using neural network architectures. With the recent advancements in LLM technology, some open-source applications have been developed to address this problem. However, these applications typically rely on the OpenAI APIs, which incur substantial financial costs, particularly for large repositories. Moreover, none of these open-source applications offer a fine-tuned model or features to enable users to fine-tune. Additionally, finding suitable data for fine-tuning is often challenging. Our application addresses these issues which is available at https://pypi.org/project/readme-ready/.