Quick Tour

Below we outline a few examples of using DataDreamer for various use cases to help you get started. It is by no means exhaustive, but should give you a good idea of what is possible with DataDreamer and how to use it. For more details on the various components of DataDreamer, please see the Overview Guide.

Synthetic Data Generation

• Training an “Abstract to Tweet Model” with Fully Synthetic Data

• Generating Training Data with Attributed Prompts

• Distilling GPT-4 Capabilities to GPT-3.5

• Augmenting an Existing Dataset

• Cleaning an Existing Dataset

• Bootstrapping Synthetic Few-Shot Examples

Instruction-Tuning and Aligning Models

• Instruction-Tuning a LLM

• Aligning a LLM with Human Preferences (RLHF)

• Training a Self-Improving LLM with Self-Rewarding (RLAIF)