Heald

Fine-tune CodeLlama with Gretel's Synthetic Text-to-SQL on BIRDBench, achieving a 36% relative improvement in EX and 38% in VES.

Gretel partners with Databricks to seamlessly integrate synthetic data workflows and improve model performance for Enterprise AI.

How thoughtful, high-quality synthetic data generation, rather than 'indiscriminate' use, can prevent model collapse.

Gretel Navigator’s synthetic data generation outperformed OpenAI's GPT-4 by 25.6%, surpassed Llama3-70b by 48.1%, and exceeded human expert-curated data by 73.6%.

Gretel Navigator now enables fine tuning for tabular datasets containing numeric, categorical, free text, JSON, and sequential (time series) data.

How to use Gretel Navigator for generating diverse, high-quality training data to create better language models.

Automate hyperparameter sweeps to create the best synthetic data for your task 🧹

How to automatically generate missing tabular data that maintains contextual relevance.

How differential privacy can generate provably private synthetic text data for a variety of enterprise AI applications.

Effective strategies for leveraging high-quality synthetic data to improve RAG model performance.