为Zephyr模型定制俄语代码指令适配器，显著提升俄语编程理解与生成能力。
Overcoming linguistic barriers in code assistants: creating a QLoRA adapter to improve support for Russian-language code writing instructions
- 基于Zephyr-7b-beta构建适配器，融合俄语编程问答数据微调。
- 在俄语代码生成任务中，性能超越原始模型及主流竞品。
- 适合需要支持俄语编程的开发者、教育机构及多语言AI研究者。
本文描述了一种针对流行语言模型Zephyr-7b-beta训练和评估适配器模型的方法。该适配器旨在提升基础模型在编程任务及俄语理解方面的表现。鉴于原模型在英语任务中已具备高质量表现，本研究目标是扩展其语言与技术覆盖范围。所提出的适配器使用大规模多样化数据集进行训练，包含与编程相关的问答对及俄语文本。采用的训练方法有效提升了模型根据俄语指令理解与生成Python代码的能力。通过多种指标评估安装适配器后的基线模型，结果表明其在俄语编程任务和语言处理方面均取得显著改进，验证了该适配器的有效性。
原文摘要 · Abstract (English)
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.
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