Part 7/8:
Part three will focus on actually using the fine-tuned model to generate plot outlines, showcasing how the model has learned from the synthetic dataset.
He revisits the importance of quality data—highlighting that more isn't necessarily better if the data is noisy or incomplete. Filtering out poor samples and structuring the data properly are critical for achieving a reliable, targeted model.
Summary: Key Takeaways
Data Generation: Use GPT-3 to synthesize a broad range of plot outlines based on genre, setting, period, and tone.
Data Cleanup: Remove short or mismatched samples to ensure the training dataset is high quality.
Data Structuring: Format data into prompt-completion pairs, maintaining consistency and clarity.