---
title: "LLMForEverybody"
type: "tool"
slug: "luhengshiwo-llmforeverybody"
canonical_url: "https://www.graphcanon.com/tools/luhengshiwo-llmforeverybody"
github_url: "https://github.com/luhengshiwo/LLMForEverybody"
homepage_url: "https://www.learnllm.ai"
stars: 6881
forks: 638
primary_language: "Jupyter Notebook"
license: "Apache-2.0"
categories: ["model-training", "developer-tools"]
tags: ["interview-practice", "learnllm", "rag", "interview-questions", "agent"]
updated_at: "2026-07-07T18:37:26.199827+00:00"
---

# LLMForEverybody

> 每个人都能看懂的大模型知识分享

LLMForEverybody是一个专注于帮助非专业人士理解和准备大模型相关面试的资源库，通过Jupyter Notebook提供互动学习体验。

## Facts

- Repository: https://github.com/luhengshiwo/LLMForEverybody
- Homepage: https://www.learnllm.ai
- Stars: 6,881 · Forks: 638 · Open issues: 0 · Watchers: 31
- Primary language: Jupyter Notebook
- License: Apache-2.0
- Last pushed: 2026-05-31T06:44:22+00:00

## Categories

- [Model Training](/categories/model-training.md)
- [Developer Tools](/categories/developer-tools.md)

## Tags

interview-practice, learnllm, rag, interview-questions, agent

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## README (excerpt)

```text
<p align="center"> 
<img src="pic/common/pr/learnllm.png" alt="LearnLLM.AI" width="600">
</p>
<p align="center"> 
  <a href="https://mp.weixin.qq.com/s/cV6v7yGmwYa2WwNDZjLPiQ"><img src="pic/common/svg/wechat.svg" alt="微信公众号" height="20"></a>
  &nbsp;
  <a href="https://www.zhihu.com/people/luhengshiwo"><img src="pic/common/svg/zhihu.svg" alt="知乎" height="20"></a>
  &nbsp;
  <a href="https://blog.csdn.net/qq_25295605?spm=1011.2415.3001.5343"><img src="pic/common/svg/csdn.svg" alt="CSDN" height="20"></a>
  &nbsp;
  <a href="https://juejin.cn/user/3824524390049531"><img src="pic/common/svg/juejin.svg" alt="掘金" height="20"></a>
</p>

<p align="center"><strong>Learning LLM is all you need.</strong></p>

<p align="center">
  中文 | <a href="README.en.md">English</a> | <a href="README.ru.md">Русский</a>
</p>

<p align="center"><strong>👉 点击 <a href="https://learnllm.ai?ref=github">LearnLLM.AI</a> | 学习大模型，从这里开始</strong></p>

## LearnLLM.AI 核心亮点

 **精选大模型面试题库**：覆盖从基础到前沿的实战题目，助您高效备战求职，抓住职业机遇；

 **系统化论文研读**：从2017年Transformer奠基性论文出发，按清晰的知识体系梳理技术演进，适合不同基础的开发者循序渐进地深度提升；

 **精选实战课程**：围绕 AI Agent、RAG 知识库、大模型微调与 LLM 应用开发等核心方向，打磨成体系的中文实战课程，覆盖 LangChain、LlamaIndex、Dify、MCP 等主流工具链，配套项目代码与讲师答疑，支持按主题灵活拆分、按需选学，帮你由点及面搭建完整的大模型知识体系。👉 [浏览全部课程](https://learnllm.ai/courses?ref=github)


**专属优惠码**

我们为Github用户准备了限时专属优惠码：***GITHUB50*** ，期待在 [LearnLLM.AI](https://learnllm.ai?ref=github) 与您继续同行，共同成长！

**配套视频教程(持续更新中)**：

👉 点击这里 [bilibili](https://space.bilibili.com/37863979/lists?sid=7144646)   

👉 点击这里 [YouTube](https://www.youtube.com/@learnllm-ai)

*如有疑问，欢迎随时联系我们。*

*Happy Learning！*

*LearnLLM.AI 团队*

---

## LLM 精选论文

| 时间 | 论文 | 介绍 | 视频 | 开始学习 |
| --- | --- | --- | --- | --- |
| 2017-06-12 | [Transformer](https://arxiv.org/abs/1706.03762) | 提出自注意力与 Transformer 架构 | [<img src="https://learnllm.ai/video_cover/transformer.jpg" width="200">](https://www.bilibili.com/video/BV1YPrKBuEjk) |  |
| 2018-06-11 | [GPT-1](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) | 预训练 + 微调的生成式 Transformer | [<img src="https://learnllm.ai/video_cover/gpt1.jpg" width="200">](https://www.bilibili.com/video/BV1gW6QBFEG4) |  |
| 2018-10-11 | [BERT](https://arxiv.org/abs/1810.04805) | 双向编码器：MLM + NSP | [<img src="https://learnllm.ai/video_cover/bert.jpg" width="200">](https://www.bilibili.com/video/BV1n2kFBgEJ5) |  |
| 2019-02-14 | [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) | 大规模无监督文本生成 | [<img src="https://learnllm.ai/video_cover/gpt2.jpg" width="200">](https://www.bilibili.com/video/BV1VwkWBtEfe) |  |
| 2019-10-23 | [T5](https://arxiv.org/abs/1910.10683) | 文本到文本统一框架 | [<img src="https://learnllm.ai/video_cover/t5.jpg" width="200">](https://www.bilibili.com/video/BV1fHBfBdEGY) |  |
| 2020-05-28 | [GPT-3](https://arxiv.org/abs/2005.14165) | 大模型与少样本学习能力 | [<img src="https://learnllm.ai/video_cover/gpt3.jpg" width="200">](https://www.bilibili.com/video/BV14Z63ByEWV) |  |
| 2020-10 | [ViT](https://arxiv.org/abs/2010.11929) | 将 Transformer 主干引入视觉领域 | [<img src="https://learnllm.ai/video_cover/ViT.jpg" width="200">](https://www.bilibili.com/video/BV1UYAuzaEHd/) |  |
| 2021-02 | [ViLT](https://arxiv.org/abs/2102.03334) | 极简视觉语言预训练架构 | [<img src="https://learnllm.ai/video_cover/ViLT.jpg" width="200">](https://www.bilibili.com/video/BV1vgXDBAEzM) |  |
| 2021-02 | [CLIP](https://arxiv.org/abs/2103.00020) | 用自然语言监督实现零样本视觉学习 | [<img src="https://learnllm.ai/video_cover/CLIP.jpg" width="200">](https://www.bilibili.com/video/BV1wGDvBfEv6) |  |
| 2021-02 | [DALL·E 1](https://arxiv.org/abs/2102.12092) | 自回归文本生成图像的开端 | [<img src="https://learnllm.ai/video_cover/DALLE1.jpg" width="200">](https://www.bilibili.com/video/BV1zPXDBTE3c) |  |
| 2021-07-07 | [CodeX](https://arxiv.org/abs/2107.03374) | 面向代码生成的 GPT 系列模型 | [<img src="https://learnllm.ai/video_cover/codex.jpg" width="200">](https://www.bilibili.com/video/BV1JC67BEE7b) |  |
| 2021-12 | [Stable Diffusion](https://arxiv.org/abs/211
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/luhengshiwo-llmforeverybody`](/api/graphcanon/tools/luhengshiwo-llmforeverybody)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_