Training Loop and Evaluation

A loop that does not measure is a loop that lies. This lesson builds the training loop that drives the GPT model: AdamW with weight decay split, a warmup plus cosine learning rate schedule, a `calc_loss_batch` helper, an `evaluate_model` pass on held out data, a `generate_and_print_sample` qualitative probe every K steps, and a JSONL log of losses you can plot after. The same skeleton trains every decoder LLM you will ever build.