Most LLM courses teach you to use a product. This one rebuilds the understanding underneath it, in the order the ideas actually developed: text as sparse counts, then dense meaning, then attention, then the models that scale it, then everything we have learned about making them behave.
The labs are the point, and they all run on your laptop. You will trace self-attention by hand on a toy example, with shapes, before you ever see the library call. The LoRA layer in module 7 is written directly against torch rather than pulled from a package, because the whole point is to see the low-rank update. The ROUGE implementation in module 8 is thirty lines of Counter. Nothing here is a black box you are asked to trust.
Every number in this course was measured, not estimated. The module 7 fine-tune takes 233 seconds and the capstone’s four-system comparison about six minutes, both on one laptop CPU with sixteen torch threads. Timings are machine specific and labelled that way in the lessons; accuracies reproduce at the stated seeds.
