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 Apr 14

Commit

03ea283

verified ·

1 Parent(s): 6f5c210

Add model card

Browse files

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README.md +85 -0

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	@@ -0,0 +1,85 @@

+---
+library_name: gguf
+base_model: nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
+tags:
+  - gguf
+  - rotorquant
+  - kv-cache-quantization
+  - nemotron
+  - nvidia
+  - mamba2
+  - hybrid
+  - llama-cpp
+  - quantized
+license: other
+license_name: nvidia-open-model-license
+license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+---
+# Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K
+GGUF Q2_K weight-quantized variant of [nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16) with **RotorQuant** KV cache compression for efficient inference with llama.cpp, Ollama, and LM Studio. Features a dense hybrid Mamba-2 + Attention architecture with 4B parameters and up to 262K context length. Maximum compression for memory-constrained environments.
+## Overview
+This model combines two compression techniques:
+- **GGUF Q2_K weight quantization** -- reduces model size from ~8 GB to ~1.5 GB
+- **RotorQuant KV cache compression** -- block-diagonal rotations (Clifford algebra) for 3-bit KV cache, 5.3x faster prefill
+## Quickstart
+### llama.cpp
+```bash
+llama-cli -m Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K.gguf \
+  --cache-type-k planar3 --cache-type-v iso3 \
+  -p "Explain quantum computing"
+```
+### Ollama
+```bash
+ollama run majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K
+```
+### LM Studio
+Download the GGUF file and load in LM Studio. Enable RotorQuant KV cache in advanced settings.
+## Specifications
+| Property | Value |
+|----------|-------|
+| Base Model | nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 |
+| Parameters | 4B (dense, Mamba-2 + Attention hybrid) |
+| Context Length | 262,144 tokens (262K) |
+| Weight Quantization | GGUF Q2_K |
+| KV Cache | RotorQuant 3-bit (planar/iso) |
+| File Size | ~1.5 GB |
+| License | NVIDIA Open Model License (commercial use OK) |
+| Compatible | llama.cpp, Ollama, LM Studio, koboldcpp |
+## What is RotorQuant?
+RotorQuant applies block-diagonal rotations (Clifford algebra) for KV cache compression. When used with llama.cpp's `--cache-type-k planar3 --cache-type-v iso3` flags:
+| Metric | RotorQuant | TurboQuant |
+|--------|-----------|-----------|
+| Prefill Speed | 3,822 tok/s | 722 tok/s |
+| Decode Speed | 119 tok/s | 93 tok/s |
+| Perplexity | 6.91 | 7.07 |
+## GGUF Quant Variants
+| Quant | File Size | Quality | Variant |
+|-------|-----------|---------|---------|
+| **Q2_K** | **~1.5 GB** | **Lowest (max compression)** | **This model** |
+| Q3_K_M | ~1.8 GB | Low | [Q3_K_M](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q3_K_M) |
+| IQ4_XS | ~2 GB | Medium-Low | [IQ4_XS](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-IQ4_XS) |
+| Q4_K_M | ~2.5 GB | Medium | [Q4_K_M](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q4_K_M) |
+| Q5_K_M | ~3 GB | Medium-High | [Q5_K_M](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q5_K_M) |
+| Q8_0 | ~4.5 GB | High | [Q8_0](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q8_0) |
+## See Also
+- [nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16) -- Base model
+- [majentik/Nemotron-3-Nano-4B-RotorQuant](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant) -- RotorQuant KV-cache (transformers)
+- [majentik/Nemotron-3-Nano-4B-RotorQuant-MLX-2bit](https://huggingface.co/majentik/Nemotron-3-Nano-4B-RotorQuant-MLX-2bit) -- MLX 2-bit variant
+- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)