# Building Large Language Models from Scratch: A Systematic Bottom-Up Learning Path

> This article introduces a structured learning project that helps learners gain an in-depth understanding of the working principles of large language models (LLMs) by building all components from scratch.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-25T22:11:00.000Z
- 最近活动: 2026-04-25T22:20:58.355Z
- 热度: 148.8
- 关键词: LLM, education, from scratch, Transformer, neural networks, deep learning, tutorial
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-axjsawsuy29t-ai-learning
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-axjsawsuy29t-ai-learning
- Markdown 来源: floors_fallback

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## [Introduction] Building LLMs from Scratch: A Systematic Bottom-Up Learning Path

This article introduces the ai-learning project, which helps learners gain an in-depth understanding of the working principles of large language models (LLMs) by building all components of an LLM from scratch. Addressing the limitations of existing resources, the project adopts a bottom-up, progressive approach, allowing learners to gradually master everything from basic tools to complete architectures, transitioning from 'knowing what' to 'knowing why'.

## Learning Background: Limitations of Existing Resources and the LLM Black Box Problem

LLMs have become a technical hot topic, but they remain a 'black box' for most people. Existing resources fall into two extremes: either high-level overviews lack implementation details, or they directly call ready-made frameworks/pre-trained models, making it difficult for learners to grasp the underlying principles and limiting the in-depth development of the AI field.

## Core Philosophy of the Project: Bottom-Up Construction and Progressive Complexity

The project adopts a bottom-up, from-scratch construction method. Its core is to understand LLM principles by hands-on implementation of each component, drawing on classic concepts in computer science education (such as learning operating systems by writing a simple kernel). It uses a progressive design, gradually building complex systems from simple components, lowering the barrier to entry and clearly showing the role and collaboration of each component.

## Learning Path: From Basic Tools to Complete Transformer Architecture

The learning path is divided into five stages:
1. **Basic Mathematics and Tools**: Master the application of linear algebra/probability theory in deep learning, and implement tensor operations, matrix multiplication, and automatic differentiation;
2. **Neural Network Basics**: Build forward/backward propagation, activation functions/loss functions, and implement a simple multi-layer perceptron;
3. **Sequence Models and Attention**: Implement RNN/LSTM, and understand dot-product attention, multi-head attention, and positional encoding;
4. **Transformer Architecture**: Assemble encoder-decoder, layer normalization, residual connections, and complete the full model;
5. **Training and Optimization**: Learn data preprocessing, batch training, learning rate scheduling, and understand pre-training/fine-tuning and distributed training.

## Practical Value: In-Depth Understanding, Engineering Capabilities, and Research Foundation

The practical value is reflected in three aspects:
1. **In-Depth Understanding**: Master the internal mechanisms of the model, quickly diagnose problems, and guide architecture design;
2. **Engineering Capabilities**: Cultivate skills such as project organization, debugging and training, and performance evaluation;
3. **Research Foundation**: Provide a solid foundation for AI research, cultivate 'first principles' thinking, and support original solutions.

## Learning Recommendations: Active Practice, Recording and Reflection, and Community Communication

Learning recommendations:
1. **Active Practice**: Learn by doing, do not skip implementation steps; try to solve problems independently first before referring to solutions;
2. **Recording and Reflection**: Maintain notes to record ideas, problems, and solutions, and review them regularly;
3. **Community Communication**: Participate in discussions and sharing, use community resources to solve difficulties and expand horizons.

## Summary and Future: Project Significance and Future Development Directions

The ai-learning project enables in-depth understanding of LLMs through hands-on construction, which is an important investment in AI learning. After completion, you can explore directions such as advanced architectures (sparse attention, state space models), multimodal learning, model compression/efficient inference, alignment and safety, where the project's foundation will play a key role.