The largest synthetic pre-training datasets for Large Language Models (LLMs) to date.

Genesis provides the global AI community with the high-quality data needed to level the playing field, accelerating the development of open-source LLMs that compete with leading closed-source / proprietary models

Check out Genesis I

Genesis I

We start with Genesis I, a synthetic dataset purpose-built for education-specific content, offering deep and comprehensive coverage across key STEM domains.

The high-quality dataset has been rigorously validated across multiple educational benchmarks, demonstrating superior performance across school and college-level subjects like Logical Deduction, Mathematics, Biology, and Medicine.

Test using our evaluation model

Test Genesis I yourself using our open-source pre-trained base model.

Perform continual pre-training, test, and compare on a proven baseline instantly and discover how Genesis I provides a practical foundation for developing next-generation STEM learning assistants that genuinely understand complex STEM concepts.

See the evaluation model

The largest synthetic pre-training datasets for Large Language Models (LLMs) to date.

Genesis I

Test using our evaluation model

FAQ

1. What is Genesis and what is the difference to other pre-training datasets?

2. Why focus on synthetic data rather than naturally occurring data?

3. If it’s 'Genesis I' this means there will be future releases?

4. How large is the Genesis I dataset in terms of tokens and storage size?

5. What are the system requirements for downloading and using Genesis?

6. Can I test the dataset without having to train a model myself?

7. Is Genesis compatible with popular LLM training frameworks like PyTorch?

8. What specific STEM subjects and topics are covered in Genesis I?

9. What educational levels does the dataset cover (undergraduate, graduate)?

10. How is the content distributed across different subject areas?

11. Does Genesis include multilingual content or is it English-only?

12. What benchmarks were used to evaluate Genesis, and what were the results?

13. How do you ensure the educational content is factually accurate?

14. What quality control processes were implemented during dataset creation?

15. How was the synthetic data in Genesis generated?

16. What safeguards are in place to prevent biases or inaccuracies in synthetic content?

17. Can you explain the methodology behind creating education-specific synthetic data?

18. How can I access and download the Genesis dataset?

19. What is the licensing model for Genesis and are there usage restrictions?

20. How does Genesis address concerns about data privacy and copyright?

21. What types of models is Genesis best suited for training?

22. Can Genesis be used for fine-tuning existing models?

23. How does Genesis complement existing open-source datasets?

24. How can I contribute to or provide feedback on Genesis?

25. Where can I find documentation and tutorials for using Genesis?

26. What steps were taken to ensure Genesis promotes responsible AI development?

27. Does Genesis contain any filtering for harmful or inappropriate content?