Build a Large Language Model (From Scratch)

Overview

A hands-on guide to implementing attention mechanisms and GPT-style transformer models from the ground up. It walks through building a complete large language model with annotated code, skipping high-level APIs in favor of low-level control.

Best for

Best for
Developers and students who want to deeply understand LLM internals by building one from scratch

Use cases

• Learning how transformers and attention layers work by coding them yourself
• Training a small GPT-style model on custom text data
• Understanding the full training pipeline from tokenization to inference

Notes

A hands-on guide to implementing attention mechanisms and GPT-style transformer models from the ground up. It walks through building a complete large language model with annotated code, skipping high-level APIs in favor of low-level control.

Use cases

• Learning how transformers and attention layers work by coding them yourself
• Training a small GPT-style model on custom text data
• Understanding the full training pipeline from tokenization to inference

Pros

• Teaches foundational concepts without relying on abstractions
• Includes runnable code examples for each stage of the model
• Covers both forward pass and training loop details

Cons

• Focuses only on decoder‑only transformer architecture, not encoders or hybrids
• Assumes prior Python and basic ML knowledge, not for absolute beginners
• Not a production‑ready framework; designed for learning and experimentation

Indexed from awesome-llm and enriched against its public facts.