datadreamer.dev 0.14.0

Prompt. Generate Synthetic Data. Train & Align Models.

https://datadreamer.dev

Prompt. Generate Synthetic Data. Train & Align Models.

DataDreamer is a powerful open-source Python library for prompting, synthetic data generation, and training workflows. It is designed to be simple, extremely efficient, and research-grade.

Installation pip3 install datadreamer.dev
demo.py	Result of demo.py
See the full demo script	See the synthetic dataset and the trained model
🚀 For more demonstrations and recipes see the Quick Tour page.

With DataDreamer you can:

• 💬 Create Prompting Workflows: Create and run multi-step, complex, prompting workflows easily with major open source or API-based LLMs.
• 📊 Generate Synthetic Datasets: Generate synthetic datasets for novel tasks or augment existing datasets with LLMs.
• ⚙️ Train Models: Align models. Fine-tune models. Instruction-tune models. Distill models. Train on existing data or synthetic data.
• ... learn more about what's possible in the Overview Guide

DataDreamer is:

• 🧩 Simple: Simple and approachable to use with sensible defaults, yet powerful with support for bleeding edge techniques.
• 🔬 Research-Grade: Built for researchers, by researchers, but accessible to all. A focus on correctness, best practices, and reproducibility.
• 🏎️ Efficient: Aggressive caching and resumability built-in. Support for techniques like quantization, parameter-efficient training (LoRA), and more.
• 🔄 Reproducible: Workflows built with DataDreamer are easily shareable, reproducible, and extendable.
• 🤝 Makes Sharing Easy: Publishing datasets and models is simple. Automatically generate data cards and model cards with metadata. Generate a list of any citations required.
• ... learn more about the motivation and design principles behind DataDreamer.

Citation

If you use DataDreamer, please cite the DataDreamer paper.

@misc{patel2024datadreamer,
      title={DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows}, 
      author={Ajay Patel and Colin Raffel and Chris Callison-Burch},
      year={2024},
      eprint={2402.10379},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contact

Please reach out to us via email ([e-mail]) or on Discord if you have any questions, comments, or feedback.

---

Copyright © 2024, Ajay Patel. Released under the MIT License.

Thank you to the maintainers at Hugging Face and LiteLLM for accepting contributions neccessary for DataDreamer and providing upstream support.