LLM-From-Scratch Build Progression
Walk a practitioner through building a working LLM on a laptop in seven stages. Each stage produces something runnable, so the internals stop being a black box.
Description
A seven-stage learning path for building a working LLM from scratch on a laptop. Each stage builds on the last and produces something you can run before the next stage starts. Stage 1 builds the text-data pipeline. Stage 2 builds the attention mechanism. Stage 3 builds the full architecture. Stage 4 is pre-training. Stage 5 is supervised fine-tuning for classification. Stage 6 is supervised fine-tuning for instructions. Stage 7 is evaluation with an LLM judge. The goal is not to beat frontier models. It is to remove the black box.
When to apply
Use this to onboard ML engineers or applied scientists onto LLM projects, to deepen the instincts of people who have only ever called APIs, or for any team that needs a real grasp of attention, tokenisation, pre-training versus fine-tuning, and instruction tuning. Do not apply it when the goal is to ship product on a deadline. This is a learning path, not a delivery one. Skip it when the team already has deep in-house expertise and the time is better spent on the application itself.
What it involves
- Stage 1: text data and tokenisation
- Stage 2: attention
- Stage 3: full transformer architecture
- Stage 4: pre-training
- Stage 5: SFT for classification
- Stage 6: SFT for instructions
- Stage 7: LLM-judge evaluation
Open the full interactive page →
Diagram, neighbourhood map, code examples, related patterns and full provenance.