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现代LLM笔记本 是一款基于 Jupyter Notebook 开发的开源工具，专注于 ai、llm、jupyter 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
现代LLM笔记本 是一款基于 Jupyter Notebook 开发的开源工具，专注于 ai、llm、jupyter 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
# 克隆仓库 git clone https://github.com/walkinglabs/modern-llm-notebook cd modern-llm-notebook # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 modern-llm-notebook --help # 基本运行 modern-llm-notebook [options] <input> # 详细使用说明请查阅文档 # https://github.com/walkinglabs/modern-llm-notebook
# modern-llm-notebook 配置说明 # 查看配置选项 modern-llm-notebook --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export MODERN_LLM_NOTEBOOK_CONFIG="/path/to/config.yml"
<p align="center"> <strong>Build modern LLMs from scratch through 23 runnable Jupyter Notebooks.</strong> </p>
<p align="center"> <a href="README.md"><strong>English</strong></a> · <a href="README-CN.md"><strong>中文文档</strong></a> · <a href="https://walkinglabs.github.io/modern-llm-notebook/"><strong>Read Online</strong></a> · <a href="https://colab.research.google.com/github/walkinglabs/modern-llm-notebook/blob/main/notebooks-en/part1-foundation/01-tokenizer-basics.ipynb"><strong>Start in Colab</strong></a> </p>
<p align="center"> <a href="https://github.com/walkinglabs/modern-llm-notebook/stargazers"> <img alt="GitHub stars" src="https://img.shields.io/github/stars/walkinglabs/modern-llm-notebook?style=social"> </a> <a href="https://github.com/walkinglabs/modern-llm-notebook/actions/workflows/quality.yml"> <img alt="Quality checks" src="https://github.com/walkinglabs/modern-llm-notebook/actions/workflows/quality.yml/badge.svg"> </a> <a href="https://github.com/walkinglabs/modern-llm-notebook/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/badge/license-CC%20BY--NC--SA%204.0-blue"> </a> <img alt="Python" src="https://img.shields.io/badge/Python-3.9%2B-3776AB"> <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-2.0%2B-EE4C2C"> <img alt="Notebooks" src="https://img.shields.io/badge/Notebooks-23-orange"> <img alt="Languages" src="https://img.shields.io/badge/Languages-English%20%7C%20Chinese-2ea44f"> </p>
<p align="center"> <a href="#overview">Overview</a> · <a href="#what-you-will-build">What You Will Build</a> · <a href="#why-this-project">Why</a> · <a href="#what-is-included">What Is Included</a> · <a href="#quick-start">Quick Start</a> · <a href="#project-status">Status</a> · <a href="#curriculum">Curriculum</a> · <a href="#quality-bar">Quality Bar</a> · <a href="#contributing">Contributing</a> </p>
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Modern LLM Notebook is a hands-on course for building modern LLM systems from the ground up in PyTorch. Instead of treating the model as a black box, you implement the core pieces yourself: tokenizers, embeddings, attention, Transformer blocks, training loops, MoE, LoRA, RLHF, decoding, KV Cache, long context, VLMs, evaluation, distillation, and on-policy distillation.
The repository ships with a full English notebook mirror under notebooks-en/. The web viewer supports language switching from the home page and the notebook sidebar (or via ?lang=en in the URL), so both the curriculum and the browsing experience stay bilingual end to end.
The project is designed as an educational reference implementation. It is not a model zoo, not a production serving framework, and not a wrapper around hosted APIs. Its purpose is to make the internal machinery of LLMs legible to engineers who want to reason from first principles.
Each notebook follows the same learning contract:
intuition -> hand calculation -> implementation -> experimentThat contract matters. A reader should not only know that BPE merges frequent pairs, or that KV Cache speeds up generation. They should be able to trace the numbers, write the minimal code, and explain why the behavior appears.
By the end, you will have implemented a compact version of the systems that power modern LLMs:
| Stage | You build | Why it matters |
|---|---|---|
| Text to tokens | Character, word, and BPE tokenizers | See exactly how raw text becomes model input |
| Tokens to vectors | Token embeddings and position encodings | Understand what the model can compute over |
| Transformer core | Self-Attention, Multi-Head Attention, Transformer blocks, Mini-GPT | Reconstruct the core forward pass |
| Training system | Cross-Entropy, batching, gradient flow, scaling-law intuition | Connect loss curves to real model behavior |
| Adaptation | LoRA, continued pretraining, reward modeling, PPO/DPO style objectives | Learn how base models become useful assistants |
| Inference system | Sampling, beam search, KV Cache, speculative decoding | Understand why serving is a systems problem |
| Frontiers | Long context, CoT experiments, VLM patch embeddings and cross-attention | Turn newer papers into small runnable examples |
| Production loop | Evaluation, win-rate matrices, distillation, OPD | Measure, compress, and improve model behavior |
raw text -> tokens -> embeddings -> attention -> Transformer -> Mini-GPT
-> training -> alignment -> inference -> evaluation -> distillationSome sandboxed environments disallow opening local sockets, which breaks the standard Jupyter kernel protocol (and tools like nbclient / nbconvert --execute). For those cases we ship a no-kernel executor that runs code cells via plain Python and writes outputs back into the English notebooks:
python scripts/execute_notebooks_en_no_kernel.py高质量的LLM构建教程
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
经综合评估，现代LLM笔记本 在AI工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | modern-llm-notebook |
| 原始描述 | 开源AI工具：A hands-on course for building modern LLMs from scratch in PyTorch, with 23 runn。⭐15 · Jupyter Notebook |
| Topics | aillmjupyter |
| GitHub | https://github.com/walkinglabs/modern-llm-notebook |
| License | NOASSERTION |
| 语言 | Jupyter Notebook |
收录时间：2026-05-26 · 更新时间：2026-05-26 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。