Instructions to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M",
	filename="Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M 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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

LM Studio
Jan

vLLM

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Ollama
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Ollama:
```
ollama run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
```

Unsloth Studio

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M 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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M 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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M to start chatting

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Docker Model Runner:
```
docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
```

Lemonade

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Run and chat with the model

lemonade run user.Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M-Q3_K_M

List all available models

lemonade list

majentik commited on 4 days ago

Commit

1b73122

verified ·

1 Parent(s): 77c30e2

docs: upstream-first KV-cache guidance (q8_0/q4_0, mainline Hadamard rotation); fork demoted to experimental

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -12,6 +12,23 @@ datasets: [nvidia/Nemotron-Image-Training-v3]
 inference: false
 ---
 # Nemotron-3-Nano-Omni-30B-A3B-Reasoning - RotorQuant GGUF Q3_K_M
 GGUF Q3_K_M quantization of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` (`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`) with RotorQuant weight method.

 inference: false
 ---
+> [!TIP]
+> **KV-cache quantization without any fork (recommended, 2026):** upstream
+> llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0`
+> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or
+> `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In
+> Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep
+> K and V types symmetric to stay on the fast fused Flash-Attention path.
+> Since April 2026, mainline llama.cpp also applies Hadamard rotation to
+> KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)),
+> which greatly improves low-bit KV quality (opt-out:
+> `LLAMA_ATTN_ROT_DISABLE=1`).
+>
+> The RotorQuant/TurboQuant fork flow below is **experimental/legacy**: the
+> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork
+> is unmaintained relative to mainline. It is NOT required to use this model.
+<!-- kv-upstream-note -->
 # Nemotron-3-Nano-Omni-30B-A3B-Reasoning - RotorQuant GGUF Q3_K_M
 GGUF Q3_K_M quantization of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` (`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`) with RotorQuant weight method.