Synthetic Data and Backbones for Fine-Tuning LLMs
- Generative AI
- H2O Cascade
This lesson walks through why synthetic datasets are generated to stand in for real data when real data is scarce, expensive, or sensitive, and how they support controlled experiments, privacy preserving sharing, and data augmentation. It then turns to fine tuning, explaining that a model is only as good as the dataset behind it, and that a single bad example among a thousand can throw a model off course. The lesson closes with ten practical questions to weigh when choosing a pretrained backbone for fine tuning, covering task fit, model size, domain match, multilingual needs, and hardware limits.
Key takeaways
- Why synthetic datasets help with privacy, data augmentation, and testing when real data is limited or sensitive
- How a single bad training example among a thousand can send a fine tuned model off the rails
- What ten questions to ask before choosing a pretrained backbone, from task type to hardware requirements
- How H2O's data preparation tooling works like automated exploratory analysis for a language model dataset
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