Building a Large Language Model from Scratch: A Complete Learning and Practice Project

This project was published by patilmanas04 on GitHub (original link: https://github.com/patilmanas04/LLM-from-Scratch, published on 2026-05-24). It aims to explain core components of large language models (tokenizers, embedding layers, attention mechanisms, positional encoding, etc.) step-by-step using Jupyter Notebooks, helping learners gain an in-depth understanding of the internal working principles of LLMs and break the "black box" perception.

Large language models (such as GPT, Claude, Llama) are powerful but remain a "black box" to most people. Most tutorials on the market only cover API calls or the use of pre-trained models, lacking details on internal implementations. This project helps learners master the working principles of LLMs by building a simplified version from scratch.

The project adopts a progressive strategy, breaking down LLMs into independent modules:

1. Tokenizer: Implement BPE tokenization from scratch and an industrial-grade solution based on TikToken;
2. Word Embedding Layer: Convert discrete words into continuous vectors;
3. Positional Encoding: Implement sine/cosine encoding and learnable encoding;
4. Attention Mechanism: From single-head to multi-head self-attention, adding causal masking;
5. Data Preprocessing: Generate training samples using sliding windows and connect the workflows of various components.

Project highlights:

• Progressive Complexity: Modules can run independently, suitable for learners with different foundations;
• Real Datasets: Use literary works like Harry Potter to intuitively demonstrate results;
• Visual Debugging: Real-time viewing of tokenization results, attention heatmaps, etc.;
• Minimal Dependencies: Core implementations do not rely on high-level frameworks, exposing details of mathematical operations.

Learning Value: Gain an in-depth understanding of Transformer design logic, cultivate engineering intuition, lay the foundation for fine-tuning optimization, and bridge theory and practice. Target Audience: Deep learning beginners, developers with framework experience, NLP researchers, and technical managers.

Current Limitations: Omits layer normalization, residual connections, multi-layer Transformer stacking, and large-scale training. Extension Directions: Add missing components, pre-training practice, learn fine-tuning techniques (LoRA, etc.), inference optimization (KV caching, quantization), and multimodal expansion.

This project helps learners understand the underlying principles of LLMs through hands-on construction, which is a valuable investment for long-term development in the AI field. Learning Suggestions: Learn in order, conduct hands-on experiments, compare with mature libraries, and try extension challenges (such as adding residual connections).