Capstone Lesson 39: Instruction Tuning by Supervised Fine-Tuning

A pretrained base model can extend a sequence but cannot follow an instruction. Supervised fine-tuning is the smallest change that fixes this: feed the model paired examples of an instruction and a desired response, and train the body to predict the response tokens. The trick is that you only want the loss to count the response, not the instruction. This lesson builds an Alpaca-style SFT loop with a custom collate function that masks instruction tokens with `ignore_index=-100`, trains on 200 instruction-response pairs, and evaluates on a held-out split using exact-match.