Building a Small Language Model from Scratch: A Practical Deep Learning Tutorial

This article introduces an open-source learning project that builds a small language model from scratch using PyTorch, helping developers gain an in-depth understanding of the core components and implementation principles of LLMs. The project covers key parts such as BPE tokenization, data sampling, embedding layer, positional encoding, causal self-attention mechanism, and multi-head attention. It comes from the GitHub project Building-Own-LLM, inspired by Sebastian Raschka's book Build A Large Language Model (From Scratch), and is suitable for developers who want to master the Transformer architecture in depth.

• Original Author/Maintainer: aadim112
• Source Platform: GitHub
• Original Title: Building-Own-LLM
• Original Link: https://github.com/aadim112/Building-Own-LLM
• Project Inspiration: Sebastian Raschka's book Build A Large Language Model (From Scratch)
• Source Code Release Date: June 13, 2026

This project is for educational purposes. It helps users understand the process of building modern language models by writing code themselves. Unlike directly using ready-made libraries, it requires an in-depth understanding of the mathematical principles and code implementation of each component. The learning path follows the natural language processing pipeline and builds incrementally, which is suitable for understanding the Transformer architecture.

BPE Tokenization

The project uses the tiktoken library to implement BPE tokenization (the method used by the GPT series). It builds a vocabulary by iteratively merging high-frequency character pairs, balancing semantic integrity and vocabulary size. The code uses the GPT-2 tokenizer to encode text into integer sequences and provides a decoding function to verify the correctness of the output.

Data Sampling and Batch Construction

A custom GPTDataset class is implemented, which uses sliding windows to extract training samples: the input sequence is a continuous text segment, and the target sequence is the input shifted right by one position, allowing the model to learn to predict the next token. The data loader supports configuring batch size, maximum sequence length, and step size; adjusting the step size can balance data utilization and computational efficiency.

Embedding Layer

The PyTorch nn.Embedding layer is used to convert discrete token IDs into continuous vectors (256 dimensions in the example), realizing the vector representation of tokens.

Positional Encoding

Pure token embedding cannot capture positional information. The project implements positional encoding to generate unique positional vectors, which are added to the token embeddings, enabling the model to perceive both token identity and position. This is the foundation of Transformer for processing sequence data.

Causal Self-Attention Mechanism

The input is converted into Query, Key, and Value matrices through linear transformation, and the dot-product attention scores between Q and K are calculated. An upper triangular mask is used to ensure the model only focuses on the current and previous positions (causality). A scaling factor is introduced to stabilize training, and Dropout is added to prevent overfitting.

Parallel Optimization of Multi-Head Attention

Two implementations are shown: sequential calculation of each attention head (intuitive but inefficient); parallel calculation (splitting vectors into multiple subspaces via reshape, with parallelism in batch and head dimensions to improve efficiency). Parallel implementation is the standard practice for modern Transformers.

Practical Significance

1. Understand the reasons behind the design of the Transformer architecture
2. Master advanced PyTorch tensor operation skills
3. Experience the transition from theory to practice
4. Lay the foundation for reading the source code of large models (e.g., Llama, GPT-4)

Learning Suggestions

• Gradually reproduce each module according to the project structure, and verify the output shape and values
• Read Sebastian Raschka's original book to get systematic theoretical explanations

Building a language model from scratch is challenging but highly valuable, as it can cultivate intuition for deep learning model design. This project shows that small LLMs involve multiple complex components. Understanding the underlying principles helps to better use, debug, and improve large models, which is applicable to model fine-tuning, domain-specific application development, or cutting-edge research. Solid basic knowledge is indispensable.