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Max-and-Omnis/
Nemotron-3-Super-64B-A12B-Math-REAP-GGUF

Name: Nemotron-3-Super-64B-A12B-Math-REAP-GGUF
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About The Models

These are the GGUF quantized models of Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-BF16

See details in https://huggingface.co/Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-BF16

Training Data

nguyen599/AstralMath-v1 — HF dataset
AIMO3 competition data — Kaggle, AI Mathematical Olympiad - Progress Prize 3

Training Data Licensing Note

Due to Kaggle competition data redistribution restrictions, the AIMO3 training data is not bundled with this model. Users who want to reproduce the training need to accept the competition rules on Kaggle and download the data separately.

This model was fine-tuned on data including AIMO3 reference problems (CC BY-SA 4.0) and AstralMath-v1 (CC BY-SA 4.0). The applicability of CC BY-SA's ShareAlike provision to ML model weights is an unsettled legal question; industry practice generally treats trained model weights as not being derivatives of training data for the purposes of license propagation. This model is released under the licenses described above on that basis.

Citations

@misc{nvidia_nemotron_3_2025,
  title  = {NVIDIA Nemotron 3: Efficient and Open Intelligence},
  author = {{NVIDIA}},
  year   = {2025},
  url    = {https://arxiv.org/abs/2512.20856},
  note   = {White Paper}
}

@misc{balunovic_srimatharena_2025,
  title = {MathArena: Evaluating LLMs on Uncontaminated Math Competitions},
  author = {Mislav Balunović and Jasper Dekoninck and Ivo Petrov and Nikola Jovanović and Martin Vechev},
  copyright = {MIT},
  url = {https://matharena.ai/},
  publisher = {SRI Lab, ETH Zurich},
  month = feb,
  year = {2025},
}

@misc{nguyen2026astralmath,
  title={AstralMath-v1: A Large-Scale Multi-Model Tool-Integrated Reasoning Dataset for Mathematical Problem Solving},
  author={Nguyen Nguyen},
  year={2026},
  url={https://huggingface.co/datasets/nguyen599/AstralMath-v1},
}

@inproceedings{
    lasby2026reap,
    title={{REAP} the Experts: Why Pruning Prevails for One-Shot MoE compression},
    author={Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026},
    url={https://openreview.net/forum?id=ukGxWd2aDG}
}

License

This model is a derivative work distributed under dual-layer licensing:

Base Model

The underlying NVIDIA Nemotron weights and architecture remain governed by the NVIDIA Nemotron Open Model License (last modified December 15, 2025).

See NVIDIA-Nemotron-Open-Model-License-12-12-25.pdf in this repository, or the official page:

https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/

"Licensed by NVIDIA Corporation under the NVIDIA Nemotron Model License."

Modifications

Modifications contributed by Max & Omnis Inc.

This modified model is licensed under the Apache License 2.0. See LICENSE-APACHE-MAX-AND-OMNIS.txt.

https://www.maxandomnis.com/en

Important: When redistributing this model or any derivative, you must comply with both licenses. The NVIDIA Nemotron Open Model License applies to the base weights; the Apache 2.0 license covers only the specific modifications listed above.

Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF