# Building a Large Language Model from Scratch: A Practical Complete Guide

> An in-depth analysis of the codebase accompanying *Build a Large Language Model (From Scratch)*, guiding you to implement a GPT-like large language model from scratch, covering the entire workflow of pre-training and fine-tuning.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-10T11:44:18.000Z
- 最近活动: 2026-06-10T11:51:00.963Z
- 热度: 163.9
- 关键词: 大语言模型, LLM, Transformer, 深度学习, 预训练, 微调, GPT, 从零实现, PyTorch, 教程
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-milistu-llms-from-scratch
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-milistu-llms-from-scratch
- Markdown 来源: floors_fallback

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## [Introduction] A Practical Guide to Building LLMs from Scratch: From Principles to Full Workflow

Original Author/Maintainer: milistu
Source Platform: GitHub
Original Title: LLMs-from-scratch
Original Link: https://github.com/milistu/LLMs-from-scratch
Publish Time: June 10, 2026

This tutorial provides an in-depth analysis of the codebase accompanying *Build a Large Language Model (From Scratch)*, guiding you to implement a GPT-like large language model from scratch, covering the entire workflow of pre-training and fine-tuning. Without relying on ready-made implementations from Hugging Face or advanced PyTorch encapsulations, it starts from basic matrix operations to help developers understand the underlying principles of LLMs.

## Background: Why Build a Large Language Model from Scratch?

Today, with the popularity of LLMs like ChatGPT, most developers are used to calling APIs, but using black boxes leads to a superficial understanding of internal mechanisms. When needing to optimize models, solve hallucination problems, or deploy under resource constraints, understanding the underlying principles is crucial. This tutorial and codebase are prepared for developers who want to "understand" LLMs, building a complete GPT-like model from the basics.

## Project Overview: A Step-by-Step Learning Path

The codebase mainly consists of Jupyter Notebooks (95.5%), with a small number of Python scripts (4.5%), following the chapter structure of the book: from text processing → attention mechanism → Transformer architecture → pre-training → fine-tuning. Each Notebook can run independently, suitable for self-learners to study intermittently without the trouble of complex dependencies.

## Core Technology Breakdown: Underlying Implementation of Transformer Architecture

Core breakdown of Transformer components:
1. Word Embedding and Positional Encoding: Implemented from scratch, converting text into continuous vectors (without directly using nn.Embedding);
2. Attention Mechanism: Manually implement scaled dot-product attention (to understand Q/K/V interactions) and multi-head attention;
3. Transformer Block: Complete implementation of layer normalization, residual connections, and feed-forward networks.

## Pre-training: Autoregressive Modeling and Engineering Details

Pre-training implements the autoregressive language modeling objective (predicting the next word), including a complete data pipeline: processing raw text, building a vocabulary, and sliding window sampling. Engineering details: learning rate scheduling, gradient clipping, checkpoint saving. The project uses the Apache 2.0 license and can be freely used for commercial or research purposes.

## Fine-tuning: Adapting the Model to Specific Tasks

Fine-tuning covers two scenarios:
1. Instruction Fine-tuning: Format question-answer pairs into instruction templates, using LoRA parameters for efficient fine-tuning to reduce costs;
2. Classification Task Fine-tuning: Adding a classification head, handling label imbalance, and evaluating performance. These techniques are also applicable to understanding open-source models like Llama/Qwen.

## Practical Value and Learning Recommendations

Suitable for: AI researchers (to deeply understand Transformer mechanisms), algorithm engineers (to customize LLMs), technical managers (to understand capability boundaries and costs), and students (to systematically learn deep learning). Recommended learning method: Read and run the Notebooks side by side, modify hyperparameters to observe effects, and debug the training process.

## Summary and Outlook: Competitiveness from Returning to Fundamentals

This project embodies the learning concept of "returning to fundamentals". In today's era of easy-to-use AI tools, developers who understand the underlying principles are more competitive. It not only teaches how to build LLMs but also cultivates the thinking of dismantling complex systems. For Chinese developers, you can replace the Tokenizer, train with Chinese corpus, and build a Chinese AI assistant.