Instructions to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled",
	filename="nemotron-120b-q4-k-m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled
# Run inference directly in the terminal:
llama-cli -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled
# Run inference directly in the terminal:
llama-cli -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

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 blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled
# Run inference directly in the terminal:
./llama-cli -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

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 blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled
# Run inference directly in the terminal:
./build/bin/llama-cli -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

Use Docker

docker model run hf.co/blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

LM Studio
Jan

vLLM

How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

Ollama
How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with Ollama:
```
ollama run hf.co/blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled
```

Unsloth Studio new

How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled 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 blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled 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 blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled to start chatting

Pi new

How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

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": "blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

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 blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

Run Hermes

hermes

Docker Model Runner
How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with Docker Model Runner:
```
docker model run hf.co/blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled
```

Lemonade

How to use blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

Run and chat with the model

lemonade run user.Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled-{{QUANT_TAG}}

List all available models

lemonade list

Beta Release - This is a beta release. A v2 is expected with more training data and improved training methodology. As of now, this model is fine-tuned exclusively on the nohurry/Opus-4.6-Reasoning-3000x-filtered dataset (2,326 reasoning traces from Claude Opus 4.6).

Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

GGUF quantizations of the fine-tuned NVIDIA Nemotron-3-Super-120B-A12B, distilled from Claude Opus 4.6 reasoning traces.

Available Quantizations

Quantization	File	Size	Description
Q4_K_M	`nemotron-120b-q4-k-m.gguf`	~50 GB	Best balance of quality and size. Medium quality, recommended for most users.
Q8_0	`nemotron-120b-q8-0.gguf`	~120 GB	Near-lossless quantization. Best quality, requires more RAM.

Model Details

Property	Value
Base Model	nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
Architecture	Nemotron-H (Mamba-2 SSM + MoE + Attention hybrid)
Parameters	120B total / 12B active (MoE)
Fine-tuning Method	LoRA (r=32, alpha=64) merged into base weights
Training Data	nohurry/Opus-4.6-Reasoning-3000x-filtered
Epochs	3
Final Training Loss	0.42

What's Different

This model has been fine-tuned on 2,326 high-quality reasoning traces from Claude Opus 4.6. The model produces structured reasoning with <think> tags before answering, similar to o1/reasoning-style models.

Usage

With llama.cpp

# Download the Q4_K_M quantization
huggingface-cli download blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled \
  nemotron-120b-q4-k-m.gguf --local-dir ./models

# Run inference
./llama-cli -m ./models/nemotron-120b-q4-k-m.gguf \
  -p "<|im_start|>system\nYou are a helpful reasoning assistant. Think step by step before answering.<|im_end|>\n<|im_start|>user\nWhat is 7 * 13?<|im_end|>\n<|im_start|>assistant\n" \
  --temp 1.0 --top-p 0.95 -n 512

With Ollama

Create a Modelfile:

FROM ./nemotron-120b-q4-k-m.gguf
PARAMETER temperature 1.0
PARAMETER top_p 0.95
SYSTEM You are a helpful reasoning assistant. Think step by step before answering.

Then:

ollama create nemotron-reasoning -f Modelfile
ollama run nemotron-reasoning "What is the sum of all prime numbers less than 20?"

Recommended Sampling Parameters

Per NVIDIA's recommendation, use temperature=1.0 and top_p=0.95 across all tasks — reasoning, tool calling, and general chat alike.

Other Formats

BF16 (safetensors): blobbybob/Nemotron-3-Super-120B-A12B-BF16-Claude-4.6-Opus-Reasoning-Distilled
FP8 (safetensors): blobbybob/Nemotron-3-Super-120B-A12B-FP8-Claude-4.6-Opus-Reasoning-Distilled

Limitations

Fine-tuned on only 2,326 examples — may not generalize to all domains
Reasoning traces are from Claude Opus 4.6; model behavior reflects that style
Beta release — expect improvements in v2

License

This model inherits the NVIDIA Nemotron Open Model License from the base model.

Downloads last month: 453

GGUF

Model size

121B params

Architecture

nemotron_h_moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for blobbybob/Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled

Base model

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

Quantized

(43)

this model

blobbybob
/

Nemotron-3-Super-120B-A12B-GGUF-Claude-4.6-Opus-Reasoning-Distilled