22 lines
3.5 KiB
TeX
22 lines
3.5 KiB
TeX
% 摘要
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\begin{center}
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{\zihao{3}\textbf{毕业论文系统设计}}\par
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{\zihao{-4}\songti 计算机科学与技术 \quad 专业 \quad 计科211（创） \quad 张三 \par
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指导教师：李四教授}
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\end{center}
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% 中文摘要
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\begin{onecolabstract}
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\noindent{}\makebox[5em][l]{{\zihao{4}\textbf{摘要}}}{\songti \zihao{-4}大型语言模型（LLMs）在通用代码生成任务中表现出色，但在处理包含专有知识的企业私有代码库时，其性能往往受限。针对此问题，本文提出并实现了一个基于文档驱动的自适应编码大模型微调框架。该框架的核心创新在于：首先，通过深度解析技术文档（以Markdown格式为例），自动抽取关键信息（如函数签名、类定义、用法示例等）并结合预设模板生成高质量的指令微调（SFT）训练语料；其次，利用参数高效微调技术（如QLoRA）对预训练的编码大模型（以Qwen为例）进行针对性优化，使其精准适配私有库的特定语法、结构和编程范式；最后，整合了包括数据持久化（SQLite+TinyDB）、训练监控（TensorBoard）和交互式前端（Gradio）在内的完整工作流。实验结果表明，该框架能够有效提升大模型在私有库代码生成任务上的准确性和实用性，显著减少对人工标注的依赖，为实现企业级软件开发的智能化和高效化提供了一套自动化、可扩展的解决方案。
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}\par
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\noindent{}\makebox[5em][l]{{\zihao{4}\textbf{关键词}}}{\zihao{-4}\songti 大型语言模型; 代码生成; 模型微调; 参数高效微调; QLoRA; 文档驱动; 自动化; 私有库; 自然语言处理; Gradio
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}\par
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\end{onecolabstract}
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% 英文摘要
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\begin{onecolabstract}
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\noindent{}\makebox[10em][l]{{\zihao{4} \textbf{ABSTRACT}}}{\zihao{-4}Large Language Models (LLMs) excel in general code generation tasks, but their performance is often limited when handling enterprise private code repositories containing proprietary knowledge. To address this issue, this paper proposes and implements a document-driven adaptive fine-tuning framework for large code models. The core innovations of this framework are: first, by deeply parsing technical documentation (using Markdown format as an example), it automatically extracts key information (such as function signatures, class definitions, usage examples, etc.) and combines them with preset templates to generate high-quality instruction fine-tuning (SFT) training data; second, it utilizes parameter-efficient fine-tuning techniques (such as QLoRA) to specifically optimize a pre-trained large code model (taking Qwen as an example), enabling it to accurately adapt to the specific syntax, structure, and programming paradigms of the private library; finally, it integrates a complete workflow including data persistence (SQLite+TinyDB), training monitoring (TensorBoard), and an interactive frontend (Gradio). Experimental results demonstrate that this framework can effectively improve the accuracy and practicality of large models in private library code generation tasks, significantly reduce reliance on manual annotation, and provide an automated, scalable solution for intelligent and efficient enterprise software development.
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}\par
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\noindent{}\makebox[10em][l]{{\zihao{4}\textbf{KEYWORDS}}}{\zihao{-4}Large Language Models; Code Generation; Model Fine-tuning; Parameter-Efficient Fine-tuning; QLoRA; Document-Driven; Automation; Private Library; Natural Language Processing; Gradio
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}\par
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\end{onecolabstract} |