# Building Large Language Models from Scratch: 20 Projects to Deeply Understand Every Layer of LLM Architecture

> An in-depth analysis of a systematic LLM learning project. Through 20 progressive hands-on projects, from basic principles to advanced architecture, you will fully master the technologies of building, debugging, and optimizing large language models.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-21T15:14:20.000Z
- 最近活动: 2026-05-21T15:21:48.697Z
- 热度: 163.9
- 关键词: 大语言模型, 从零构建, Transformer, 深度学习, 注意力机制, 反向传播, AI教育, 神经网络, 模型优化, 实践项目
- 页面链接: https://www.zingnex.cn/en/forum/thread/20llm
- Canonical: https://www.zingnex.cn/forum/thread/20llm
- Markdown 来源: floors_fallback

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## [Introduction] Building LLMs from Scratch: 20 Projects to Master Core Architecture and Principles

# Building Large Language Models from Scratch: 20 Projects to Deeply Understand Every Layer of LLM Architecture Introduction
Large Language Models (LLMs) like ChatGPT and Claude have become major breakthroughs in the AI field, but most developers have little knowledge of their internal principles. The \"Under the Hood\" project provides a complete path to building LLMs from scratch through 20 progressive hands-on projects, helping learners transition from API users to model builders, master the core architecture, debugging, and optimization techniques of LLMs, and develop deep understanding capabilities.

## Background: Pain Points in AI Education and Project Philosophy

## Background: Pain Points in AI Education and Project Philosophy
Current AI education generally has the problem that learners stay at the API calling level and have little knowledge of the internal principles of models, which limits innovation and deep understanding. The \"Under the Hood\" project created by Ramchand Kumaresan adopts the practice-oriented concept of \"Build it, Break it, Measure it\", allowing learners to understand the working principles of LLMs by building components with their own hands. This method is based on cognitive science research: active knowledge construction promotes deeper understanding than passive acceptance.

## Methodology: Progressive Learning Path of 20 Projects

## Methodology: Progressive Learning Path of 20 Projects
The project designs 20 sub-projects from basic to advanced:
- **Early stage**: Focus on basic neural network components (linear layers, activation functions, loss functions), understand mathematical principles and computational details;
- **Mid stage**: Introduce convolution, recurrent neural networks, and attention mechanisms (implement scaled dot-product attention and multi-head attention from scratch, assemble Transformer encoder/decoder);
- **Late stage**: LLM-specific technologies (positional encoding, layer normalization, residual connections), large-scale training optimization (KV caching, quantization).
Each project follows the \"Build-Test-Optimize\" cycle, simulating real engineering practice.

## Close Integration of Mathematics and Code Implementation

## Close Integration of Mathematics and Code Implementation
The project is characterized by the deep integration of mathematical theory and code implementation:
- Each component implementation is accompanied by mathematical principle explanations (matrix operations, gradient descent, probability distributions, etc.), establishing a mapping from abstract mathematics to concrete code;
- Taking backpropagation as an example, it not only shows the code implementation but also explains the chain rule and automatic differentiation principles;
- It focuses on numerical stability issues (such as softmax avoiding exponential explosion, cross-entropy logarithmic space operations preventing underflow), which are key details hidden in off-the-shelf frameworks.

## Cultivation of Debugging and Performance Analysis Skills

## Cultivation of Debugging and Performance Analysis Skills
The \"Break it\" phase is a feature of the project:
- Intentionally introduce bugs and performance bottlenecks to cultivate diagnostic and repair capabilities (visualize activation distributions, analyze gradient flow, identify numerical anomalies);
- Teach performance analysis (identify computational bottlenecks, memory access patterns, evaluate efficiency) to facilitate deployment in resource-constrained environments;
- Emphasize test-driven development, write unit tests to ensure code correctness, and cultivate good software engineering habits.

## Evolution from Toy Models to Practical LLM Systems

## Evolution from Toy Models to Practical LLM Systems
The project gradually transitions from toy models to practical systems:
- After understanding component principles, you can make wise architecture choices and balance design decisions;
- Covers core technologies of modern LLMs (pre-training strategies, fine-tuning, alignment methods), understanding their motivations and theoretical foundations;
- Focuses on computational efficiency (parallel computing, distributed training) and provides practical guidance for scaling model size.

## Learning Community and Resource Ecosystem

## Learning Community and Resource Ecosystem
As an open-source project, \"Under the Hood\" has an active community:
- Learners can share implementations, discuss problems, and contribute improvements;
- Rich supporting resources (documents, video explanations, reference implementations) adapt to different learning styles;
- Synchronized with academic papers and industrial practices to ensure cutting-edge content, and the community will integrate new architectures/technologies to maintain freshness.

## Conclusion: Paradigm Shift in AI Education and the Path to Deep Builders

## Conclusion: Paradigm Shift in AI Education and the Path to Deep Builders
\"Under the Hood\" represents a paradigm shift in AI education: in the era of API popularity, it emphasizes the importance of basic principles and fills the gap in current education. The project provides a replicable teaching template for educational institutions, proving that complex concepts can be effectively taught through practical projects. Through training with 20 projects, learners not only master the technology of building LLMs but also develop the thinking to understand and debug complex systems, becoming deep builders in the AI era.