AI / A CONCEPT NOTE
Pre-training
training on massive unlabeled data
Overview · mechanism
pitfall · examples
01 / THE SHORT VERSION
The idea in a few sentences.
The first phase of LLM training where a model learns language structures, grammar, and facts by predicting hidden tokens across web-scale text collections.
02 / FOLLOW THE MECHANISM
How pre-training flows
Data crawl
collects raw text archives (Wikipedia, books, codebases).
Token mask
masks words in sequences, forcing the model to guess hidden tokens.
Feedback loop
adjusts parameter weights to minimize token guess errors.
04 / COMMAND NOTES
Read the command, then the result.
Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.
load a raw pre-trained checkpoint
python -c "from transformers import AutoModel; ..."The ellipsis omits required code or values. This sketch is not runnable as written.
05 / CHECK YOURSELF
Could you explain Pre-training to a teammate?
Try it out loud in two sentences: what it is, and the one detail that changes the picture. If you stall, the gap is the part to reread.
Up next in AI engineeringBase Modelthe foundational pre-trained model