Part 6/8:
Add stop sequences to prevent endless loops.
Increase the volume of training data to enrich context and diversity.
Shapiro mentions that data quality directly influences output variety and coherence. Improving data through synthetic augmentation—beyond simple deletion of poor samples—can further enhance model performance.
Strategies for Improving Fine-Tuning Data
Beyond removing bad samples, future techniques could involve synthetically augmenting data. These methods might include paraphrasing, adding controlled noise, or balancing the dataset across different themes and styles to foster more nuanced and reliable outputs.
Accessing and Implementing Your Fine-Tuned Model
Once training is complete, there are two primary ways to utilize your model: