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 (ajayp@upenn.edu) and Professor Chris Callison-Burch (ccb@upenn.edu) at the University of Pennsylvania.