---
license: cc-by-sa-4.0
datasets:
- ontocord/megawiki_with_gov_docs
- nvidia/OpenCodeReasoning
- nvidia/Llama-Nemotron-Post-Training-Dataset-v1
language:
- en
multilingual: true
tags:
- pretraining
- open-access
- synthetic
- multimodal
- legally-permissive
pretty_name: MixtureVitae
---

![MixtureVitae Dataset](https://huggingface.co/datasets/ontocord/MixtureVitae/resolve/main/mixturevitae-logo.png)


# MixtureVitae: A Permissive, High-Performance, Open-Access Pretraining Dataset

## Overview

**MixtureVitae** is an open-source, permissive, high-quality dataset designed for pretraining large language models (LLMs) across a wide variety of modalities, domains, and languages. The goal of MixtureVitae is to accelerate the development of transparent, open-access AI while lowering legal uncertainty around copyright and data provenance. See our [blog](https://aurora-lm.github.io/posts/mixturevitae/).
- **Please note this dataset is still being uploaded in parts. More shards will appear over time. Please be patient.**

## Features

- **1 Trillion+ Tokens**: MixtureVitae includes over 1 trillion tokens of diverse text and multimodal content, carefully filtered for copyright-permissiveness and enriched with high-quality synthetic data.
- **Cross-Modality**: Includes textual, visual, and auditory elements; sourced and generated to support multimodal and multilingual LLM training.
- **Transparent and Open**: Based on publicly available data, permissive licenses (e.g. CC-BY, MIT, Apache), and public domain sources. Built with rigorous filtering and legal and ethical considerations.
- **Diversity & Balance**: Includes multimodal, narrative, conversational, instructive, educational, legal, scientific, and programming content across multiple domains and languages.

## Data Components

MixtureVitae comprises three main categories:

### Web-Based Open Datasets (Filtered)
- **Nemotron-CC**, **Cosmopedia**, **FineWeb-Edu**, **TxT360**, **Cultura-Y**, etc.
- Global deduplication and permissive heuristic filtering applied (e.g. .gov domains, CC-BY keywords, spam/obscenity filtering).

### Curated Datasets
- Includes subsets and cleanups from **Open License Corpus**, **PG-19**, **Freelaw**, **Stack v1**, **Euro-Pat**, **USPTO**, **Wikipedia**, **arXiv**, **OpenWebMath**, **Megawika**, **Europarl**, **HackerNews**, and more.
- Covers legal, scientific, technical, conversational, and multilingual data.

### Synthetic Data
- **Math textbooks**, **Tiny-stories style narratives**, **Cross-language code translation**, **MCQ generation**, **Multimodal grounding**, **Multilingual translations**, and more.

## Preprocessing & Filtering

- **Permissive Filtering**: Heuristic and keyword filtering to retain CC-BY, public domain, and .gov sources while excluding unsafe/unclear cases.
- **Light Global Deduplication**: Prefix-based matching due to deduplication already performed in source corpora.
- **Sentence Deduplication**: Low-information duplicate detection with WordNet substitution.
- **FastText Filtering & Classification**:
  - **Domain Classifier** (based on FineWeb & Pile)
  - **Genre/Register Classifier** (TurkuNLP)
  - **Math/Education quality Rankers** (inspired by DeepSeekMath & Phi-3)
  - **Red Pajama** quality rankers
- **Quality Upsampling**: Classification and rank allows users to apply targeted upsampling of diverse content types.

## Dataset Size & Format

- Over **1 trillion tokens** total, not including multimodal data.
- **Multimodal shards** include aligned image captions, audio transcripts, and instruction-style text.
- Currently releasing only mostly english text shards, but will slowly release multimodal and transaltions.
- Sharded and deduplicated to enable scalable training on clusters or cloud.

## Links To Component Datsets

- TBD: List component datasets such as MixtureVitae-atomic_2024, and other MixtureVitae-* datasets.

## Legal Considerations

MixtureVitae is designed with legal caution, transparency, and fair-use alignment:
- Heavy reliance on public domain, open licenses, and US federal government content.
- Filtering for third-party copyrighted content.
- Ethical justifications and fair use arguments applied to .gov content.
- **We do not guarantee non-infringement and disclaim legal liability** — researchers are advised to consult legal experts before commercial use.

## Intended Uses

- Pretraining LLMs across text and multimodal domains.
- Research into legal-compliant open model development.
- Instruction tuning, alignment training, and multilingual or cross-domain generalization.

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## Licensing

We license our own contributions and annotaitons under CC-BY-SA. MixtureVitae itself includes sources under their own individual licenses:
- Creative Commons (CC-BY, CC-BY-SA)
- Public domain or governmental data (.gov, .mil) 
- Permissive software/data licenses (MIT, BSD, Apache)  
However, as with any large corpus: **Use at your own legal discretion.**

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## Contributors

This dataset was created by **Ontocord.AI**, with support from collaborators and references from open AI research ecosystems. Built as part of the [Aurora-M2](https://aurora-lm.github.io/) project. We thank the contributors of datasets like Nemotron-CC, Cosmopedia, FineWeb, Open License Corpus, and many others.

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## How to Cite

```bibtex
@misc{txt360data2024,
      title={MixtureVitae: A Fully Permissive, High-Performance, Open-Access Pretraining Dataset}, 
      author={Harsh Raj, Huu Nguyen, Ken Tsui, Diganta Misra, Victor May, Vu Minh Chien},
      year={2025}
}