Part 2/10:
Shapiro begins by explaining how he generated a substantial dataset of nearly 700 synthetic scenes. These scenes serve as training data, formatted with a prompt (a random scene) and a follow-up using specific tokens, including "next paragraph" and "stop," to guide the model during fine-tuning. The data resides on GitHub, setting the stage for customizing AI outputs.
The goal was to train a model specifically on story progression patterns so that subsequent generations would naturally follow from previous context. By meticulously curating this dataset, the model learns to continue stories coherently, avoiding abrupt or nonsensical transitions.