# Building Large Language Models from Scratch: A Complete Learning Roadmap

> An in-depth analysis of shivakiran-ai's llm-from-scratch project, which provides a complete learning path from raw text processing to a full GPT-2 model, covering 36 topics including tokenizers, attention mechanisms, and Transformer architecture.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-09T08:51:13.000Z
- 最近活动: 2026-05-09T08:58:45.583Z
- 热度: 143.9
- 关键词: 大语言模型, LLM, GPT-2, Transformer, PyTorch, 深度学习, 注意力机制, 从零实现, 机器学习教育
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-shivakiran-ai-llm-from-scratch
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-shivakiran-ai-llm-from-scratch
- Markdown 来源: floors_fallback

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## Introduction | Building Large Language Models from Scratch: A Complete Learning Roadmap

The open-source llm-from-scratch project by shivakiran-ai offers a 36-topic learning path from raw text processing to a full GPT-2 model. Using a first-principles approach, it requires learners to implement each component by hand to deeply understand the working principles of Large Language Models (LLMs). This project is suitable for researchers, engineers, and students, serving as a practical path to gain a deep understanding of LLMs.

## Project Background and Core Philosophy

The project stems from the goal of understanding the working principles of LLMs. It rejects the use of ready-made advanced APIs like `AutoModel.from_pretrained()` and requires all components to be implemented by hand. As the author says: "If it exists in the final model, it must first be understood, designed, and coded here." This first-principles approach is particularly valuable for students preparing for PhD research in machine learning, as it lays a solid foundation for research contributions.

## Five Phases of the Learning Path

The project divides the learning process into five phases:
1. Data Pipeline (Completed): Covers topics such as tokenizer implementation, Byte Pair Encoding (BPE), data loader design, word embeddings, and positional encoding, enabling conversion of raw text into model inputs;
2. Attention Mechanism (Completed): Evolves from RNN/LSTM to self-attention, including core content like QKV, causal masking, and multi-head attention;
3. Model Architecture (In Progress): Involves GPT-2 structure, layer normalization, GELU activation function, etc. Remaining topics include residual connections and complete Transformer blocks;
4. Pre-training (To Be Started): Includes next token prediction, loss functions, optimizers, decoding strategies, etc.;
5. Fine-tuning (To Be Started): Focuses on adapting to specific tasks such as classification tasks and instruction fine-tuning.

## Unique Organization of Learning Resources

Each topic folder contains three files:
1. README.md: Concise concept summaries, core insights, and paper links, suitable for quick onboarding;
2. TopicN_Title.docx: Complete mathematical derivations, code references, and explanations of design decisions, suitable for in-depth learning;
3. notebook.ipynb: Runnable Python implementations with detailed comments, facilitating hands-on practice.
The three-layer structure caters to different learning needs and flexibly adapts to time and depth requirements.

## Unique Value of the Project

Compared to tutorials available on the market, the core values of this project are:
- Completeness: Covers the entire process from raw text to a trained model;
- Depth: Each component includes mathematical principles, design decisions, and implementation details;
- Practicality: All code can be run directly to observe the actual behavior of components;
- Progressiveness: The 36 topics are arranged in increasing order of difficulty, suitable for long-term learning plans.
It is suitable for researchers, engineers, and students who wish to deeply understand the internal principles of LLMs.

## Conclusion

The era of large language models has arrived, but people who truly understand their internal working principles are still rare. The llm-from-scratch project allows learners to experience the thinking behind design decisions by writing every line of code by hand. This first-principles learning method may be the key to staying rational and creative in the era of rapid AI development.