Building Large Language Models from Scratch: A Practical Guide to Deeply Understanding LLM Principles

LLMs-from-scratch is an educational open-source project aimed at helping learners build and train GPT-like large language models from scratch, deeply understand core principles such as the Transformer architecture and attention mechanism, and address the current black-box dilemma of large language models. Through clear guidance and code examples, the project enables learners with basic programming skills to master the underlying implementation details of LLMs.

Large language models like GPT, Claude, and Llama have changed the way we interact with technology, but most users lack an understanding of their internal working principles, creating a knowledge gap that limits application and debugging capabilities. The LLMs-from-scratch project emerged to address this—it is not an API calling tool, but a hands-on guide to building models from scratch, helping users understand the implementation details of core concepts.

This is an open-source educational project aimed at enabling people with basic programming skills to understand and implement LLMs. It adopts a from-scratch approach, using basic tools like PyTorch to build each component, emphasizing transparency and practice. The learning path is progressive: Data Processing (tokenization, vocabulary, embedding layer) → Attention Mechanism (self-attention, multi-head attention) → Transformer Block (layer normalization, feed-forward network, residual connection) → Training Loop and Generation Logic.

The project provides an in-depth explanation of key concepts:

• Tokenization: Introduces the BPE algorithm, allowing learners to implement a simple tokenizer and understand how subword units balance vocabulary size and expressive power;
• Embedding Layer: Explains the necessity of positional encoding, and implements sinusoidal positional encoding and learnable positional embeddings;
• Attention Mechanism: Derives and implements dot product, scaled dot product, and multi-head attention, helping learners understand the meaning of Q/K/V matrices and the role of scaling factors;
• Transformer Architecture: Covers the differences between layer normalization and batch normalization, the design of feed-forward networks, and how residual connections aid gradient flow.

Completing the project equips learners with multiple skills: proficient use of PyTorch, model debugging capabilities, intuitive understanding of LLMs, and the ability to read research papers. The project complements theoretical learning—it assumes learners have basic ML knowledge and translates theory into code; for those familiar with theory, it helps verify understanding; for beginners, it is recommended to first get an overview of Transformers before diving into details.

The project has an active community: The GitHub repository includes a detailed README, an Issues section for questions and exchanges, and a Discussions section for sharing insights. It links to abundant extended resources (papers, blogs, videos), and advanced learners can extend the project (e.g., efficient attention variants, different positional encodings, large-scale training) to enrich the ecosystem.

Limitations of the project: It is not a production-grade model; its data scale and parameter count are far smaller than GPT-4-level models, and its value lies in understanding principles rather than replicating performance. Learning recommendations: Do not copy code—try modifying experiments (changing hyperparameters, visualizing intermediate states, using different datasets); use debugging tools to inspect tensors; investing dozens of hours is worthwhile, as active building leads to deeper understanding than passive consumption.

LLMs-from-scratch is a valuable resource for AI education, lowering the barrier to understanding LLMs. It is suitable for AI career changers, researchers, and technology enthusiasts. In an era of rapid AI development, understanding underlying principles is essential to keep up with technological evolution, and this project provides a clear path—worth investing time to learn.