LLM_book
非常详细的大模型LLM的学习教程与笔记
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LLM_book is a learning record and roadmap for programmers with a certain AI foundation to learn Large Language Models (LLM). It covers topics such as PyTorch basics, Transformer architecture, langchain basics, foundational concepts of large models, fine-tuning methods, RAG (Retrieval-Augmented Generation), and building intelligent agents using LLM. The repository provides learning materials, code implementations, and documentation to help users progress in understanding and implementing LLM technologies.
README:
学习路线笔记:从 PyTorch 到大语言模型
这是一个程序员学习 LLM(Large Language Models) 的学习记录与路线图。每一个章节内都有对应的篇章的笔记,对从0开始的同学友好,也适合有基础的程序员挑着看。
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学习资料:
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阶段目标: 掌握 PyTorch 常用模块与基本理论,能够独立实现经典 CNN 模型 ResNet。
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成果:
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学习重点:
- 理解 Transformer 架构:位置编码、多头注意力、残差连接、LayerNorm 等
- 掌握 Encoder-Decoder 整体流程
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阶段目标: 能够独立复现 Transformer 架构模型。
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成果:
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教程:
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学习langchain的核心概念:
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成果:
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学习目标:
- 参考: 斯坦福CS336
- 重点理解:
5. 大模型之训练与微调
大模型的预训练组相关HC门槛极高而且学习很需要资源,作者只是一个database组小硕,所以我们主要进行微调学习。
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学习路线:
- 开源模型的本地部署
- 模型的generate流程
- 全参数微调
- Adapter/Prefix-Tuning
- LoRA(Low-Rank Adaptation)
- PEFT(Parameter-Efficient Fine-Tuning)
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目标:在已有大模型上,快速适配特定任务。
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学习内容:
- 向量数据库(如 FAISS, Milvus)
- 文档检索 + LLM 推理的结合
- 知识增强型对话与问答系统
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学习内容:
- ReAct 框架(Reason + Act)
- 工具调用(Tool Use)
- 多步推理(Chain-of-Thought)
- 自主任务分解与执行
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目标:让 LLM 从单纯对话扩展为 能完成复杂任务的智能体。
本学习路线持续更新中,代码与文档将同步在 GitHub 仓库 中。
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