Gretel Navigator Tabular Fine-Tuning
Create high-quality, domain-specific datasets for generative AI
Gretel’s flagship model for generating tabular datasets supporting numerical, categorical, free text, and event-driven data.
Key benefits
High-fidelity synthetic data generation without compromising privacy.
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- Strong privacy guarantees
Protect sensitive entities in the training dataset using differential privacy.
- Flexibility
Leverage an LLM-based system to handle complex tabular datasets all in one job.
- High-quality
Use a pre-trained transformer-based model to deliver best-in-class data fidelity.
- Simplicity
Configure with YAML quickly, with defaults that work well for diverse datasets.
How it works
Preserve data-type nuances and relationships in complex enterprise datasets.
Tabular Fine-Tuning is an LLM-based AI system engineered to support tabular datasets with diverse modalities. Observe how it effectively learns patterns to generate high-quality, realistic data without replicating original data, ensuring robust privacy protection while preserving data utility and integrity.
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