Instructions to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

SGLang

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with Ollama:
```
ollama run hf.co/Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M
```

Unsloth Studio

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF to start chatting

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with Docker Model Runner:
```
docker model run hf.co/Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M
```

Lemonade

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nemotron-3-Super-64B-A12B-Math-REAP-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

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.

Downloads last month: 352

GGUF

Model size

64B params

Architecture

nemotron_h_moe

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

8-bit

16-bit

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

Base model

nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16

Finetuned

Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-BF16

Quantized

(3)

this model

Dataset used to train Max-and-Omnis/Nemotron-3-Super-64B-A12B-Math-REAP-GGUF

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

NVIDIA Nemotron 3: Efficient and Open Intelligence

Paper • 2512.20856 • Published Dec 24, 2025 • 44