Motivation and Design#

DataDreamer is an open-source Python library that is made to help streamline and accelerate increasingly common LLM-related workflows for ML / NLP researchers and users and help increase the rate of research progress through features encouraging open science and reproducibility.

Design Principles#

A few design principles of DataDreamer are:

  • 🔬 Research-Grade and Production-Grade: Implementations of techniques that are consistent with the established work and best practices for correctness and efficiency.

  • 💪 Reproducibility and Robustness: A focus on reproducibility and robustness.

  • 🧩 Simple with Sensible Defaults: Simple and easy to get started with little configuration through sensible defaults.

  • 🛠️ Adaptable, Extensible, and Customizable: Selectively overridable advanced configuration and the ability to support new techniques or models.

  • 👥 Accessible: Aggressive caching and efficiency techniques to make both computationally- and financially-expensive LLM-related workflows more accessible to resource-constrained researchers.

  • 🤝 Community-Driven: Community members can contribute to extend DataDreamer’s abilities.

For Anyone#

While DataDreamer was designed for researchers, by researchers, it is also meant to be accessible to anyone who wants to use it.

Use in Teaching#

While DataDreamer was built to help researchers and practitioners implement complex LLM-related workflows, it is extremely simple to use making bleeding-edge models, techniques, and training accessible to reasonably technical students.

If you are a university professor of a graduate-level NLP or machine learning course and would like to trial using DataDreamer in your course for instruction, assignments, or projects please reach out to Ajay Patel ( and Professor Chris Callison-Burch ( at the University of Pennsylvania.