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.
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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.
Numerical data
Categorical data
Free text data
Mix of tabular modalities

Resources

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