Overcoming linguistic barriers in code assistants: creating a QLoRA adapter to improve support for Russian-language code writing instructions

TL;DR

This paper presents a novel adapter for the zephyr-7b-beta model that significantly enhances its ability to understand and generate Python code based on Russian-language instructions, addressing linguistic barriers in code assistants.

Contribution

The paper introduces a new adapter trained on diverse Russian programming data, improving the model's performance in Russian code tasks and expanding its linguistic capabilities.

Findings

Significant improvement in Russian language understanding and code generation.

Enhanced performance in Python coding tasks with Russian instructions.

Outperforms existing models in relevant benchmarks.

Abstract

In this paper, an approach to training and evaluating an adapter model for the popular language model "zephyr-7b-beta" is described. The adapter was developed to improve the performance of the base model in tasks related to programming and understanding the Russian language. Considering the high quality of the original model in tasks in the English language, the goal of the research was to expand its linguistic and technical spectrum. The proposed adapter was trained using a large and diverse dataset, including question-answer pairs related to programming, as well code-related texts in Russian language. The applied training methodology ensures an improvement in the model's quality of answers in understanding and generating Python code based on Russian instructions. We evaluated the performance of the base model with the installed adapter using various metrics, comparing it to the base model as well as other state-of-the-art models in this field. The obtained results showed significant improvement, both in tasks related to writing Python code and in processing the Russian language, confirming the effectiveness of the proposed adapter.