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FAQ_Of_LLM_Interview

大模型算法岗面试题(含答案):常见问题和概念解析 "大模型面试题"、"算法岗面试"、"面试常见问题"、"大模型算法面试"、" 大模型应用基础"

欢迎PR

目录

FAQ_Of_LLM_Interview/
|
├── LICENSE
├── README.md
├── requirements.txt
├── 面试必问问题.md
├── 1-大模型应用基础/
│   ├── CNN卷积神经网络基础.md
│   ├── PyTorch从0开始编写Transformer.md
│   ├── PyTorch搭建神经网络.md
│   ├── Pytorch基本用法.md
│   ├── RNN循环神经网络基础.md
│   ├── Transformer模型结构.md
│   ├── Yolo基础知识了解.md
│   ├── 大模型的泛化能力.md
│   ├── 聚类.分类_算法.md
│   └── 训练与推理.md
├── 2-大模型优化技术/
│   ├── 常见大模型调用代码.md
│   ├── 微调优化.md
│   └── fine_tune/
│       ├── LLM_Fine_Tuning.ipynb
│       └── LLM_Fine_Tuning.md
├── 3-interview_qa/
│   ├── ant.md
│   ├── atom.md
│   ├── liantong.md
│   ├── pdd.md
│   ├── relx.md
│   ├── saikai.md
│   ├── torch.md
│   ├── txyz.md
│   └── ucloud.md
├── 4-分布式训练篇/
│   ├── Accelerate-使用进阶.md
│   ├── DataParallel原理与应用.md
│   ├── Distributed-DataParallel分布式数据并行原理与应用.md
│   ├── README.md
│   └── 分布式训练与环境配置.md
├── 5-高效微调篇/
│   ├── Lora 原理与实战.md
│   ├── P-Tuning 原理与实战.md
│   ├── PEFT 进阶操作.md
│   ├── Prefix-Tuning 原理与实战.md
│   ├── Prompt-Tuning原理与实战.md
│   └── README.md
└── langchain/
    ├── GPT&Bert.md
    ├── LC&Extract.md
    ├── LangChain&Agents.md
    ├── LangChain&CSV.md
    ├── LangChain&LCEL.md
    ├── LangChain&SQL.md
    ├── LangChain&Server&Cli.md
    ├── LangChain.md
    ├── LoRA..ETC.md
    ├── Pinecone&Faiss&Chroma.md
    ├── Pytorch&DeepSpeed.md
    ├── fine-tune参数解释.md
    └── paddle&tensorflow.md

来一个测试环境

pip freeze > requirements.txt
conda create -n myPlot python=3.11
conda activate myPlot
pip install -r requirements.txt --proxy=127.0.0.1:10809

必备知识

在阅读本文前,建议补充一些相关知识。若你之前未了解过相关原理,可以参考以下的链接:

特别鸣谢

  • 张老师(提供了此库的最开始的思路,没有他就没有此库)
  • 赵老师(无偿回答了我很多问题)

吐槽

大模型这方向真的卷,新paper,新模型疯狂出,东西出的比我读的还快.
欢迎有偿咨询:sisconsavior

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