Overcoming linguistic barriers in code assistants: creating a QLoRA adapter to improve support for Russian-language code writing instructions
This work addresses the problem of limited multilingual support in code assistants for Russian-speaking programmers, representing an incremental advancement by adapting an existing model to a new language domain.
The researchers tackled the problem of linguistic barriers in code assistants by creating a QLoRA adapter for the zephyr-7b-beta model to improve support for Russian-language programming instructions, resulting in significant improvements in tasks related to writing Python code and processing Russian language as confirmed by various metrics.
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.