Instructions to use empero-ai/openNemo-9B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use empero-ai/openNemo-9B-abliterated-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 empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/openNemo-9B-abliterated-GGUF:Q4_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 empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/openNemo-9B-abliterated-GGUF:Q4_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 empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use empero-ai/openNemo-9B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "empero-ai/openNemo-9B-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "empero-ai/openNemo-9B-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M

Ollama
How to use empero-ai/openNemo-9B-abliterated-GGUF with Ollama:
```
ollama run hf.co/empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M
```
Unsloth Desktop

How to use empero-ai/openNemo-9B-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use empero-ai/openNemo-9B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use empero-ai/openNemo-9B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.openNemo-9B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use empero-ai/openNemo-9B-abliterated-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 empero-ai/openNemo-9B-abliterated-GGUF:Q4_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 empero-ai/openNemo-9B-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use empero-ai/openNemo-9B-abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/openNemo-9B-abliterated-GGUF:Q4_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 "empero-ai/openNemo-9B-abliterated-GGUF:Q4_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"

openNemo-9B-abliterated-GGUF

GGUF quantizations of openNemo-9B-abliterated for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible tools.

Abliterated (uncensored) version of NVIDIA's Nemotron-H 9B, with safety refusals removed using Empero AI's Snakehead — an abliteration tool specialized for hybrid Mamba2 + sparse attention architectures.

By Empero AI

Available quantizations

File	Quant	Size (approx)	Notes
`openNemo-9B-abliterated-Q2_K.gguf`	Q2_K	~3.5 GB	Smallest, lower quality
`openNemo-9B-abliterated-Q3_K_S.gguf`	Q3_K_S	~4.1 GB	Small 3-bit
`openNemo-9B-abliterated-Q3_K_M.gguf`	Q3_K_M	~4.5 GB	Medium 3-bit
`openNemo-9B-abliterated-Q3_K_L.gguf`	Q3_K_L	~4.9 GB	Large 3-bit
`openNemo-9B-abliterated-Q4_0.gguf`	Q4_0	~5.2 GB	Basic 4-bit
`openNemo-9B-abliterated-Q4_K_S.gguf`	Q4_K_S	~5.3 GB	Small 4-bit k-quant
`openNemo-9B-abliterated-Q4_K_M.gguf`	Q4_K_M	~5.5 GB	Recommended — best balance of size and quality
`openNemo-9B-abliterated-Q5_0.gguf`	Q5_0	~6.3 GB	Basic 5-bit
`openNemo-9B-abliterated-Q5_K_S.gguf`	Q5_K_S	~6.3 GB	Small 5-bit k-quant
`openNemo-9B-abliterated-Q5_K_M.gguf`	Q5_K_M	~6.5 GB	Medium 5-bit k-quant
`openNemo-9B-abliterated-Q6_K.gguf`	Q6_K	~7.5 GB	6-bit, near-lossless
`openNemo-9B-abliterated-Q8_0.gguf`	Q8_0	~9.5 GB	8-bit, virtually lossless
`openNemo-9B-abliterated-IQ4_XS.gguf`	IQ4_XS	~4.8 GB	imatrix 4-bit, very efficient

Which quant should I use?

Low VRAM (6–8 GB): Q4_K_M — best quality-per-bit at this size
Medium VRAM (8–12 GB): Q5_K_M or Q6_K
High VRAM / quality priority: Q8_0
Absolute minimum size: Q2_K or Q3_K_S (expect some quality loss)

Usage

llama.cpp

llama-cli -m openNemo-9B-abliterated-Q4_K_M.gguf -p "Your prompt here" -n 512

Ollama

Create a Modelfile:

FROM ./openNemo-9B-abliterated-Q4_K_M.gguf

Then:

ollama create opennemo-uncensored -f Modelfile
ollama run opennemo-uncensored

LM Studio

Download the desired quant file and load it directly in LM Studio.

About the base model

openNemo-9B-uncensored is an abliterated version of openNemo-9B — a pure-PyTorch reimplementation of NVIDIA's Nemotron-H architecture (hybrid Mamba2 + Transformer, 56 layers).

Ablation results

Metric	Value
Pre-ablation refusal rate	97%
Post-ablation refusal rate	13%
KL divergence	0.022
Ablation config	c=15, r=25, w=1.37, g40l

The extremely low KL divergence (0.022) means model quality is virtually identical to the original on non-refused prompts.

Disclaimer

This model has had its safety alignment removed. It will comply with requests that the original model would refuse. The creators are not responsible for how this model is used. Intended for research, creative writing, and applications where the user takes responsibility for output filtering.