NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF

Instructions to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF", device_map="auto")

llama-cpp-python

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF",
	filename="NVIDIA-Nemotron-3-Super-120B-A12B-BF16.i1-Q6_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

Use Docker

docker model run hf.co/Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with Ollama:
```
ollama run hf.co/Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K
```

Unsloth Studio

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF to start chatting

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

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": "Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

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 "Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K" \
  --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 Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K
```

Lemonade

How to use Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Iambackup/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF:Q6_K

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF-Q6_K

List all available models

lemonade list

NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF / README.md

Iambackup

Duplicate from mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF

680478c about 1 month ago

preview code

Raw

History Blame Contribute Delete

7.29 kB

metadata

base_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
datasets:
  - nvidia/nemotron-post-training-v3
  - nvidia/nemotron-pre-training-datasets
language:
  - en
  - fr
  - es
  - it
  - de
  - ja
  - zh
library_name: transformers
license: other
license_link: >-
  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/
license_name: nvidia-nemotron-open-model-license
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
  - nvidia
  - pytorch
  - nemotron-3
  - latent-moe
  - mtp

About

weighted/imatrix quants of https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	imatrix	0.4	imatrix file (for creating your own quants)
GGUF	i1-IQ1_S	45.1	for the desperate
GGUF	i1-IQ1_M	46.5	mostly desperate
GGUF	i1-IQ2_XXS	49.0
GGUF	i1-IQ2_XS	51.0
GGUF	i1-IQ2_S	51.1
GGUF	i1-IQ2_M	53.0
GGUF	i1-Q2_K	53.6	IQ3_XXS probably better
GGUF	i1-Q2_K_S	54.4	very low quality
GGUF	i1-IQ3_XXS	56.9	lower quality
GGUF	i1-IQ3_XS	57.9
GGUF	i1-IQ3_S	60.0	beats Q3_K*
GGUF	i1-Q3_K_S	60.0	IQ3_XS probably better
GGUF	i1-IQ3_M	60.9
GGUF	i1-IQ4_XS	66.4
GGUF	i1-Q3_K_M	67.3	IQ3_S probably better
GGUF	i1-Q4_0	68.9	fast, low quality
GGUF	i1-Q3_K_L	70.8	IQ3_M probably better
GGUF	i1-Q4_K_S	75.9	optimal size/speed/quality
GGUF	i1-Q4_1	75.9
GGUF	i1-Q4_K_M	86.2	fast, recommended
GGUF	i1-Q5_K_S	86.9
GGUF	i1-Q5_K_M	95.8
GGUF	i1-Q6_K	113.0	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.