Instructions to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

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-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_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 majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_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 majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

LM Studio
Jan

vLLM

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K"
# 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-4B-RotorQuant-GGUF-Q2_K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Ollama
How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Ollama:
```
ollama run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
```

Unsloth Studio

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K to start chatting

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K:Q2_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": "majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K:Q2_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 majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K 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-4B-RotorQuant-GGUF-Q2_K:Q2_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 "majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_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 majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Docker Model Runner:
```
docker model run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
```

Lemonade

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Run and chat with the model

lemonade run user.Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K-Q2_K

List all available models

lemonade list

majentik commited on May 6

Commit

28f0927

verified ·

1 Parent(s): da268df

docs: Tier 2 polish — variant matrix + quant trade-off

Browse files

Files changed (1) hide show

README.md +35 -0

README.md CHANGED Viewed

@@ -160,3 +160,38 @@ For VRAM-constrained setups, standard q8_0 KV cache quantization already halves
 - [TurboQuant paper (arXiv: 2504.19874)](https://arxiv.org/abs/2504.19874)
 - [Base model: nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16)
 - [Nemotron-3-Nano-4B announcement](https://huggingface.co/blog/nvidia/nemotron-3-nano-4b)

 - [TurboQuant paper (arXiv: 2504.19874)](https://arxiv.org/abs/2504.19874)
 - [Base model: nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16)
 - [Nemotron-3-Nano-4B announcement](https://huggingface.co/blog/nvidia/nemotron-3-nano-4b)
+## Quant trade-off (GGUF lane)
+| Quant | Approx size | Use case | Recommendation |
+|---|---|---|---|
+| **Q2_K** | ~2.2 GB | Lossy, low-RAM CPU/edge | **Resource-constrained inference** |
+| Q3_K_M | ~2.4 GB | Smaller-than-Q4, modest quality drop | Edge devices with ~16 GB RAM |
+| IQ4_XS | ~2.1 GB | Importance-quant 4-bit, smaller than Q4_K_M | Best size/quality at 4-bit |
+| Q4_K_M | ~3.0 GB | Balanced default | Recommended for most users |
+| Q5_K_M | ~3.1 GB | Higher fidelity than Q4 | Quality-sensitive applications |
+| Q6_K | ~3.6 GB | Approaching FP16 quality | High-fidelity CPU/edge |
+| Q8_0 | ~4.1 GB | Near-lossless reference | Fidelity-critical work |
+| MXFP4_MOE | ~2.2 GB | Microscaling FP4 (MoE-aware) | vLLM / transformers users |
+(Current variant — **Q2_K** — is bolded.)
+## Variants in this family
+(Showing 13 sibling variants under `majentik/nemotron3-nano-4b-*`. The current variant — `RotorQuant-GGUF-Q2_K` — is **bolded**.)
+| Variant | Runtime | Approx size | Use case |
+|---|---|---|---|
+| [RotorQuant](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
+| [RotorQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-gguf-IQ4_XS) | llama.cpp | ~3.4 GB | Lossy 4-bit, low-RAM CPU/edge |
+| **RotorQuant-GGUF-Q2_K** | llama.cpp | ~2.4 GB | Lossy, low-RAM CPU/edge |
+| [RotorQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-gguf-Q3_K_M) | llama.cpp | ~3.1 GB | Smaller 3-bit, CPU-friendly |
+| [RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-gguf-Q4_K_M) | llama.cpp | ~4.4 GB | Balanced default |
+| [RotorQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-mlx-2bit) | mlx-lm | ~1.3 GB | Apple Silicon, smallest |
+| [RotorQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-mlx-4bit) | mlx-lm | ~2.5 GB | Apple Silicon balanced |
+| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-4b-rotorquant-mlx-8bit) | mlx-lm | ~4.7 GB | Apple Silicon reference |
+| [TurboQuant](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
+| [TurboQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant-gguf-Q4_K_M) | llama.cpp | ~4.4 GB | Balanced default |
+| [TurboQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant-mlx-2bit) | mlx-lm | ~1.3 GB | Apple Silicon, smallest |
+| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant-mlx-4bit) | mlx-lm | ~2.5 GB | Apple Silicon balanced |
+| [TurboQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-4b-turboquant-mlx-8bit) | mlx-lm | ~4.7 GB | Apple Silicon reference |