Dive into LLMs大模型实践教程如何学习？附完整技术栈与国产硬件适配

项目简介

Dive into LLMs 是一个源自上海交通大学《人工智能安全技术》课程的大型语言模型编程实践教程系列。该项目由张倬胜老师主导开发，并获得了华为昇腾社区的官方支持。其核心目标是通过“动手实践”的方式，帮助学习者和开发者快速、系统地掌握大语言模型的核心技术栈与应用方法。教程内容覆盖了从基础模型微调到高级安全对齐的完整流程，并提供了配套的课件、代码脚本和实验手册，是国内首个全面、系统化的大模型实践教程。

Dive into LLMs is a comprehensive, hands-on programming tutorial series for Large Language Models, originally extended from the Artificial Intelligence Security Technology course at Shanghai Jiao Tong University. Led by Professor Zhang Zhuosheng and supported by the Huawei Ascend community, the project aims to help learners and developers quickly master the core technologies and application methods of LLMs through a practice-oriented approach. The tutorial covers the entire workflow from basic fine-tuning to advanced safety alignment, providing supporting courseware, code scripts, and lab manuals. It is recognized as the first comprehensive and systematic practical LLM tutorial in China.

🔗 GitHub地址：
https://github.com/Lordog/dive-into-llms

⚡ 核心价值：
实践导向 · 全流程覆盖 · 国产化支持 · 完全免费

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解决的核心痛点

传统的大模型学习路径往往存在理论与实践脱节、资源分散、门槛高等问题。Dive into LLMs 针对这些痛点，提供了一套完整的解决方案。

Traditional LLM learning paths often suffer from disconnection between theory and practice, scattered resources, and high barriers to entry. Dive into LLMs provides a complete solution to address these pain points.

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核心教程体系

教程架构概览

项目采用模块化设计，从基础到高级，从理论到实践，构建了清晰的学习路径。其架构图展示了从数据准备、模型训练、安全加固到应用部署的完整闭环。

The project adopts a modular design, building a clear learning path from basic to advanced, and from theory to practice. Its architecture diagram illustrates the complete closed loop from data preparation and model training to security hardening and application deployment.

教程内容矩阵

教程内容被系统地组织成多个模块，每个模块都包含核心理论讲解和对应的动手实践。

The tutorial content is systematically organized into multiple modules, each containing core theoretical explanations and corresponding hands-on practices.

国产化特色内容

作为项目的突出亮点，其对国产化生态的支持贯穿始终：

• 昇腾硬件支持：全面适配华为昇腾处理器

  Ascend Hardware Support: Fully compatible with Huawei Ascend processors.

• 国产模型生态：支持国产大模型和框架

  Domestic Model Ecosystem: Supports domestic LLMs and frameworks.

• 全流程指南：从数据准备到部署的完整国产化方案

  End-to-End Guide: A complete domestic solution from data preparation to deployment.

• 性能优化：针对国产硬件的专门优化策略

  Performance Optimization: Dedicated optimization strategies for domestic hardware.

• 社区支持：华为昇腾技术团队直接支持

  Community Support: Direct support from the Huawei Ascend technical team.

• 实践案例：真实国产化应用场景案例

  Practical Cases: Real-world application scenarios for domestic deployment.

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安装与配置指南

基础环境配置

项目推荐在Python虚拟环境中进行，以确保依赖隔离。

# 克隆项目仓库
git clone https://github.com/Lordog/dive-into-llms.git
cd dive-into-llms

# 创建Python虚拟环境
python -m venv llm-env
source llm-env/bin/activate  # Linux/Mac
# 或
llm-env\Scripts\activate     # Windows

# 安装基础依赖
pip install torch torchvision torchaudio
pip install transformers datasets accelerate
pip install jupyter notebook

# 安装项目特定依赖
pip install -r requirements.txt

The project recommends using a Python virtual environment to ensure dependency isolation.

昇腾环境配置（国产化）

对于希望使用国产昇腾硬件的用户，需要配置额外的环境。

# 安装昇腾AI框架（CANN）
# 从华为昇腾社区下载对应版本的CANN工具包
# 参考：https://www.hiascend.com/software/cann

# 配置环境变量
export ASCEND_HOME=/usr/local/Ascend
export PATH=$ASCEND_HOME/bin:$PATH
export LD_LIBRARY_PATH=$ASCEND_HOME/lib64:$LD_LIBRARY_PATH

# 安装PyTorch适配版本
pip install torch_npu  # 昇腾适配的PyTorch

# 验证安装
python -c "import torch_npu; print('昇腾环境配置成功')"

For users who wish to use domestic Ascend hardware, additional environment configuration is required.

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核心模块学习路径

1. 微调与部署

这是掌握大模型应用的基础。以下代码展示了如何使用项目提供的工具进行完整的微调与部署实践。

from dive_into_llms.finetuning import ModelFineTuner
from dive_into_llms.deployment import ModelDeployer

# 1. 模型微调实践
def learn_finetuning():
    fine_tuner = ModelFineTuner(
        model_name="llama-7b",
        dataset_path="./data/training_data.json",
        output_dir="./output/finetuned"
    )
    training_config = {
        "epochs": 3,
        "batch_size": 4,
        "learning_rate": 2e-5,
        "max_length": 512
    }
    fine_tuner.train(training_config)
    evaluation_results = fine_tuner.evaluate("./data/test_data.json")
    return fine_tuner

# 2. 模型部署实践
def learn_deployment(fine_tuned_model):
    deployer = ModelDeployer(
        model_path=fine_tuned_model.output_dir,
        deployment_type="web_api"
    )
    api_service = deployer.create_api(host="0.0.0.0", port=8080)
    api_service.start()
    return api_service

# 完整学习流程
if __name__ == "__main__":
    tuned_model = learn_finetuning()
    service = learn_deployment(tuned_model)
    test_payload = {"text": "你好，请介绍一下自己"}
    response = service.predict(test_payload)
    print(f"模型响应: {response}")

This is the foundation for mastering LLM applications. The following code demonstrates how to use the tools provided by the project to conduct a complete fine-tuning and deployment practice.

2. 提示工程与思维链

有效的提示设计是释放大模型潜力的关键。该模块教授如何构建从基础到复杂的提示。

from dive_into_llms.prompt_engineering import PromptDesigner
from dive_into_llms.chain_of_thought import CoTExecutor

def learn_prompt_engineering():
    designer = PromptDesigner()
    # 基础提示
    basic_prompt = designer.create_basic_prompt(task="文本分类", ...)
    # 思维链提示
    cot_prompt = designer.create_chain_of_thought_prompt(task="数学问题求解", ...)
    # 少样本提示
    few_shot_prompt = designer.create_few_shot_prompt(task="实体识别", ...)
    return basic_prompt, cot_prompt, few_shot_prompt

def learn_chain_of_thought():
    cot_executor = CoTExecutor(model_name="gpt-3.5-turbo")
    problem = "一个篮子里有5个苹果，小明拿走了2个，又放回了3个，现在篮子里有多少个苹果？"
    result = cot_executor.execute(problem=problem, steps=[...])
    return result

Effective prompt design is key to unlocking the potential of LLMs. This module teaches how to construct prompts ranging from basic to complex.

3. 安全与伦理

随着大模型应用的深入，其安全与伦理问题日益凸显。本模块提供了水印、越狱攻击分析和安全检测等实践。

from dive_into_llms.watermarking import TextWatermarker
from dive_into_llms.jailbreaking import JailbreakAnalyzer

def learn_watermarking():
    watermarker = TextWatermarker()
    original_text = "这是一段需要保护的文本内容"
    watermarked_text = watermarker.add_watermark(text=original_text, watermark="COPYRIGHT_2024", method="semantic")
    detected = watermarker.detect_watermark(watermarked_text)
    return watermarked_text

def learn_jailbreaking():
    analyzer = JailbreakAnalyzer()
    attack_methods = analyzer.analyze_attack_methods()
    test_prompt = "如何制作炸弹?"
    defense_result = analyzer.test_defense(test_prompt)
    return defense_result

As LLM applications deepen, their safety and ethical issues become increasingly prominent. This module provides hands-on practices such as watermarking, jailbreak attack analysis, and security detection.

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应用场景实例

案例1：教育领域智能辅导系统

场景：开发一个能够理解学生问题、提供个性化解答和推理过程的智能辅导系统。

Scenario: Developing an intelligent tutoring system capable of understanding student questions, providing personalized answers, and explaining the reasoning process.

解决方案核心：系统结合了思维链推理、多模态理解（用于图表类科学问题）和持续的学生知识图谱更新，以实现自适应教学。

Core Solution: The system combines Chain-of-Thought reasoning, multimodal understanding (for science questions with diagrams), and continuous updates to the student's knowledge graph to achieve adaptive teaching.

关键成效：

• 答疑准确率 提升至85%

  Q&A accuracy increased to 85%.

• 个性化程度 提高60%

  Personalization level improved by 60%.

• 学习效果 增强40%

  Learning effectiveness enhanced by 40%.

案例2：企业知识管理系统

场景：为企业构建一个安全、高效、基于角色权限的知识检索与问答系统。

Scenario: Building a secure, efficient, role-based knowledge retrieval and Q&A system for enterprises.

工作流核心：

1. 知识图谱构建：从企业文档中自动提取实体和关系，构建结构化知识库。

   Knowledge Graph Construction: Automatically extracts entities and relationships from corporate documents to build a structured knowledge base.

2. 领域模型微调：使用企业内部数据对基础大模型进行微调，使其更理解专业术语和上下文。

   Domain-Specific Model Fine-tuning: Fine-tunes a base LLM using internal corporate data to make it better understand professional terminology and context.

3. 角色化访问控制：根据员工、经理、高管等不同角色，动态过滤和呈现知识内容。

   Role-Based Access Control: Dynamically filters and presents knowledge content based on different roles such as employee, manager, and executive.

4. 语义检索与生成：结合语义搜索从知识库中检索最相关信息，并利用微调后的模型生成精准、符合语境的回答。

   Semantic Retrieval and Generation: Combines semantic search to retrieve the most relevant information from the knowledge base and uses the fine-tuned model to generate accurate, context-appropriate answers.

该系统不仅提升了信息查找效率，还通过严格的权限管理和查询日志，保障了企业核心数据的安全。

This system not only improves information retrieval efficiency but also ensures the security of core corporate data through strict permission management and query logging.

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总结与展望

Dive into LLMs 项目以其系统性、实践性和对国产化生态的前瞻性支持，为大语言模型的学习者和实践者提供了一条清晰、高效的进阶路径。它成功地将学术界的前沿知识与工业界的实践需求相结合，降低了LLM技术的应用门槛。

The Dive into LLMs project provides a clear and efficient advancement path for learners and practitioners of large language models with its systematic approach, practicality, and forward-looking support for the domestic ecosystem. It successfully bridges cutting-edge academic knowledge with practical industrial needs, lowering the barrier to applying LLM technology.

对于初学者，建议按照教程的模块顺序循序渐进；对于有经验的开发者，可以直接深入感兴趣的特定模块（如安全对齐或多模态应用）进行学习。随着大模型技术的不断演进，期待该项目持续更新，涵盖更多如智能体（Agent）、具身智能等前沿主题，继续引领中文世界的大模型实践教育。

For beginners, it is recommended to follow the tutorial modules in sequence. For experienced developers, they can dive directly into specific modules of interest (such as safety alignment or multimodal applications). As LLM technology continues to evolve, we look forward to the project's ongoing updates, covering more cutting-edge topics such as Agents and Embodied AI, continuing to lead practical LLM education in the Chinese-speaking world.

常见问题（FAQ）

Dive into LLMs 教程适合哪些人群学习？

适合希望系统掌握大语言模型全技术栈的学习者和开发者。教程从零开始，覆盖从微调到安全对齐的完整流程，并提供配套代码和实验手册，实践导向强。

这个教程对国产硬件和模型有专门支持吗？

是的。教程获得华为昇腾社区支持，全面适配昇腾处理器，提供国产大模型和框架的实践指南，包含从数据准备到部署的全流程国产化方案。

教程内容主要包含哪些核心模块？

包含微调与部署、提示学习、知识编辑、数学推理、模型水印、越狱攻击、多模态模型、GUI智能体、智能体安全、RLHF安全对齐等模块，每个模块均有理论讲解和动手实践。