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## 项目背景
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本项目是广州大学计算机科学与网络工程学院计算机科学与技术专业某2025届本科生的毕业设计。
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## Latex编译要求
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编译环境要求:
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## 论文编译
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论文LaTeX源码位于`paper/`目录,编译环境要求:
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- 操作系统:Windows 11
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- TeX发行版:TeXLive 2023+
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- 编译引擎:XeLaTeX
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- 字体要求:方正楷体简体(需预先安装)
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## 相关latex资源
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- 论文LaTeX源码位于`paper/`目录,
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- 答辩PPT相关资源位于`report/`目录
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## 项目简介
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### 项目概述
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@ -16,7 +16,7 @@
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\node[title, above=0.1cm of business.north west, anchor=west] {业务逻辑层};
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\node[layer, fill=orange!10, below=0.5cm of business] (data) {};
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\node[title, above=0.1cm of data.north west, anchor=west] {数据访问层};
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\node[title, above=0.1cm of data.north west, anchor=west] {数据访问层模块};
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% 表现层模块
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\node[module, fill=blue!20] (ui1) at (-3.5, 1.5) {模型管理};
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BIN
paper/latex/pic/training_metrics.png
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paper/latex/pic/training_metrics.png
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report/.gitignore
vendored
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report/.gitignore
vendored
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# LaTeX临时文件
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*.aux
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*.log
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*.out
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*.toc
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*.synctex.gz
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*.bbl
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*.blg
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*.dvi
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*.fdb_latexmk
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*.fls
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*.lof
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*.lot
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*.idx
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*.ilg
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*.ind
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*.nav
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*.snm
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*.vrb
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*.xdv
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*.pdf
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@ -1,7 +0,0 @@
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\begin{frame}
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\frametitle{致谢}
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\begin{center}
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\LARGE{\textbf{感谢聆听!}}\\[0.5cm]
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\large{欢迎各位老师提出宝贵意见}
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\end{center}
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\end{frame}
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@ -1,10 +0,0 @@
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% 研究背景
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\section{研究背景}
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\begin{frame}
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\frametitle{研究背景}
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\begin{itemize}
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\item[\faCheckCircle] 研究领域现状:大语言模型在公开数据集与开源项目中表现优异,但在处理企业私有库时存在局限性
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\item[\faExclamationTriangle] 存在的问题:缺乏对私有库专有函数、类及其交互细节的深度理解,无法精准引用库中的类、方法或属性
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\item[\faFlask] 研究意义:通过深度解析私有库文档,对大语言模型进行微调与优化,提升生成代码的准确性和实用性
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\end{itemize}
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\end{frame}
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@ -1,26 +0,0 @@
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% 总结与展望
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\section{总结与展望}
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\begin{frame}
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\frametitle{总结与展望}
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\begin{itemize}
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\item[\faBook] 主要工作总结
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\begin{itemize}
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\item 提出了文档驱动的自适应编码大模型微调框架
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\item 实现了三层架构设计(表现层、业务逻辑层和数据访问层)
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\item 实现了大语言模型的微调
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\end{itemize}
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\item[\faLightbulb] 创新点总结
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\begin{itemize}
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\item 开发了基于栈结构的Markdown文档解析器
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\item 采用了QLoRA参数高效微调方法
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\item 构建了基于提示工程的数据集生成工具
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\end{itemize}
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\item[\faRoad] 未来工作展望
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\begin{itemize}
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\item 边缘计算部署
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\item 更多的格式输入支持
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\item CICD集成
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\end{itemize}
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\item[\faGithub] 软件仓库地址:\href{https://gitea.carry.fit/carry/gzhu-biyesheji}{https://gitea.carry.fit/carry/gzhu-biyesheji}
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\end{itemize}
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\end{frame}
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% 系统设计
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\section{系统设计}
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\begin{frame}
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\frametitle{系统架构}
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\begin{center}\includegraphics[width=\textwidth,height=0.7\textheight,keepaspectratio]{./pic/system_architecture.png}\end{center}
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\end{frame}
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@ -1,85 +0,0 @@
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% 实验结果
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\section{实验结果}
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\begin{frame}
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\frametitle{实验条件介绍}
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\begin{itemize}
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\item[\faLaptop] 硬件配置
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\begin{itemize}
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\item 笔记本型号:Lenovo Legion R7000P 2021H
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\item CPU:AMD Ryzen 7 5800H
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\item GPU:NVIDIA GeForce RTX 3060 Laptop GPU(6GB显存)
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\item 内存:16GB DDR4
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\end{itemize}
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\item[\faUbuntu] 软件环境
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\begin{itemize}
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\item 操作系统:Ubuntu 22.04 LTS(通过WSL2运行)
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\item Python版本:3.11.6
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\item 深度学习框架:PyTorch 2.1.0+cu121
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\end{itemize}
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\item[\faRobot] 微调模型:unsloth官方qwen-2.5-3b-4bit量化模型(\href{https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit}{https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit})
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\item[\faBook] 仓库文档:unsloth官方仓库文档(\href{https://docs.unsloth.ai/}{https://docs.unsloth.ai/})
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{训练指标变化}
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\begin{figure}[h]
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\centering
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\begin{tikzpicture}
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\begin{axis}[
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xlabel=Step,
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ylabel=Loss,
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width=\textwidth,
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height=0.8\textheight]
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\addplot[mark=none] table[x=Step,y=loss,col sep=comma] {./figures/training_data.csv};
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\end{axis}
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\end{tikzpicture}
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\caption{训练损失变化曲线}
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\end{figure}
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\end{frame}
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\begin{frame}
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\frametitle{训练指标变化}
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\begin{figure}[h]
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\centering
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\begin{tikzpicture}
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\begin{axis}[
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xlabel=Step,
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ylabel=Grad Norm,
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width=\textwidth,
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height=0.8\textheight]
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\addplot[mark=none] table[x=Step,y=grad_norm,col sep=comma] {./figures/training_data.csv};
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\end{axis}
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\end{tikzpicture}
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\caption{梯度范数变化曲线}
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\end{figure}
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\end{frame}
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\begin{frame}
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\frametitle{训练指标变化}
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\begin{figure}[h]
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\centering
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\begin{tikzpicture}
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\begin{axis}[
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xlabel=Step,
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ylabel=Learning Rate,
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width=\textwidth,
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height=0.8\textheight]
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\addplot[mark=none] table[x=Step,y=learning_rate,col sep=comma] {./figures/training_data.csv};
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\end{axis}
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\end{tikzpicture}
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\caption{学习率变化曲线}
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\end{figure}
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\end{frame}
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\begin{frame}
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\frametitle{微调效果验证}
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\begin{figure}[htbp]
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\centering
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\includegraphics[width=0.45\textwidth]{./pic/before_train.png}
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\hspace{0.05\textwidth}
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\includegraphics[width=0.45\textwidth]{./pic/after_train.png}
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\caption{训练前后对比}
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\end{figure}
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\end{frame}
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@ -1,24 +0,0 @@
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% 技术介绍
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\section{技术介绍}
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\begin{frame}
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\frametitle{技术介绍}
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\begin{itemize}
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\item[\faDesktop] 前端技术栈
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\begin{itemize}
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\item Gradio - Python 机器学习前端框架
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\end{itemize}
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\item[\faServer] 后端技术栈
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\begin{itemize}
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\item Python - 主要开发语言
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\item SQLite - 轻量级关系型数据库
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\item TinyDB - 轻量级非关系型数据库
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\item LangChain - 大语言模型应用框架
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\end{itemize}
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\item[\faBrain] 人工智能技术
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\begin{itemize}
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\item Qwen2.5 - 开源大语言模型
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\item Unsloth - 开源微调工具
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\item QLoRA - 低秩适应微调技术
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\end{itemize}
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\end{itemize}
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\end{frame}
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Load Diff
@ -1,46 +0,0 @@
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\documentclass[aspectratio=43]{beamer}
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\usepackage{ctex}
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\usepackage{graphicx}
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\usepackage{amsmath}
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\usepackage{hyperref}
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\usepackage{listings}
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\usepackage{xcolor}
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\usepackage{fontawesome5}
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\usepackage{subcaption}
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\usepackage{booktabs}
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\usepackage{tikz}
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\usepackage{pgfplots}
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\pgfplotsset{compat=1.18}
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% 设置主题
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\usetheme{Madrid}
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\usecolortheme{default}
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\setbeamertemplate{navigation symbols}{}
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% 标题信息
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\title{基于文档驱动的自适应编码大模型微调框架}
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\subtitle{毕业设计答辩}
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\author[XXX]{姓名:XXX \\ 学号:XXX \\ 指导教师:XXX}
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\institute{广州大学}
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\date{\today}
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\begin{document}
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% 标题页
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\frame{\titlepage}
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% 目录
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\begin{frame}
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\frametitle{目录}
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\tableofcontents
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\end{frame}
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% 引入各章节内容
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\input{chapters/background}
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\input{chapters/technology}
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\input{chapters/design}
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\input{chapters/results}
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\input{chapters/conclusion}
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\input{chapters/acknowledgements}
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\end{document}
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毕业论文.pdf
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毕业论文.pdf
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endstream
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endobj
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13 0 obj
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