Open Source

Gretel releases a new synthetic financial document dataset to empower AI developers in building customized and highly performant sensitive data detection systems.

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

Augment machine learning datasets with synthetically generated text and labels using an open-source implementation of GPT-3.

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

Train financial LLMs with Gretel's Synthetic Text-to-Python dataset to transform natural language into precise, domain-specific Python code for FinTech.

Gretel fine-tuned, synthetically-enhanced GLiNER models for better PII & PHI detection—datasets included.

Gretel's latest open synthetic dataset aims to enhance LLM interactions and contributes to the popular 'awesome-chatGPT-prompts' GitHub repository.

Gretel releases largest open source Text-to-SQL dataset to accelerate AI model training

Rapid SLM releases highlight the need for clarity on licenses + lineage, which are crucial for enterprises navigating open-weight models and synthetic data ownership

Discover the streamlined Gretel Python SDK. Start building with synthetic data in just 3 lines of code 🚀

Learn the simplest way to generate synthetic data without setting up your own infrastructure and GPUs.

Here's what we learned about privacy engineering from 50+ companies and hundreds of developers.

Generate synthetic time series data with Gretel.ai’s open-source PyTorch implementation of DoppelGANger.

We are launching Gretel Blueprints, making it easy to anonymize and balance datasets with just a few clicks.

We founded Gretel based on our beliefs that data shouldn’t be scary.

How to train a FastCUT GAN on public location data from a few cities to predict realistic e-bike locations across the world.

Set up a cutting-edge environment for deep learning with TensorFlow 2.10, PyTorch, Docker, and GPU support.

Quickly and safely aggregate geolocation data for location density analysis using a hexagonal grid system.

We explore the popular open-source package Optuna to demonstrate how you can optimize your model hyperparameters and build the best synthetic model possible.

Generate artificial records to balance biased datasets and improve overall model accuracy.

Our new configuration templates will help you pick some of the right parameters needed to train your synthetic data models.

In this blog, we will use Fluent Bit to collect logs from AWS EKS cluster applications.

Look at how FastText word embeddings can help to quickly understand new datasets, and build more consistent labels for your own data.

In this post, we walk through building a data pipeline that will automatically transform datasets so they can be safely used in development environments.

Assessing the efficacy and quality of synthetic data with Gretel Evaluate API.

How to use Weights & Biases’ ML hyperparameter sweeps tool to optimize the accuracy of your synthetic data.