Instructions to use zaakirio/Ornith-1.0-9B-uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zaakirio/Ornith-1.0-9B-uncensored-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zaakirio/Ornith-1.0-9B-uncensored-GGUF",
	filename="ornith-1.0-9b-uncensored-Q4_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 zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use zaakirio/Ornith-1.0-9B-uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaakirio/Ornith-1.0-9B-uncensored-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": "zaakirio/Ornith-1.0-9B-uncensored-GGUF",
		"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/zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M

Ollama
How to use zaakirio/Ornith-1.0-9B-uncensored-GGUF with Ollama:
```
ollama run hf.co/zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zaakirio/Ornith-1.0-9B-uncensored-GGUF to start chatting

How to use zaakirio/Ornith-1.0-9B-uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_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": "zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-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 zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use zaakirio/Ornith-1.0-9B-uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M
```

Lemonade

How to use zaakirio/Ornith-1.0-9B-uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zaakirio/Ornith-1.0-9B-uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.0-9B-uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Ornith-1.0-9B-uncensored — GGUF

A decensored (Heretic-abliterated) version of deepreinforce-ai/Ornith-1.0-9B — a Qwen3.5-VL 9B coding and reasoning model.

Abliteration technique: Arditi et al. (2024). Decensoring tool: Heretic v1.4.0.

Files

Quant	Size	Download	Notes
Q4_K_M	5.3 GB	Download	Recommended — best size/quality balance
Q6_K	6.9 GB	Download	Near-lossless
Q8_0	8.9 GB	Download	Essentially full precision
F16	17 GB	Download	Full precision reference

Usage

Download a quant above, then:

# Server — OpenAI-compatible API on :8080
llama-server -m ornith-1.0-9b-uncensored-Q4_K_M.gguf -ngl 99 -c 2048 --jinja --port 8080

# CLI
llama-cli -m ornith-1.0-9b-uncensored-Q4_K_M.gguf -ngl 99 --jinja

--jinja is required — without it the model uses a generic template and compliance degrades.

What makes this different

Ornith-1.0-9B refuses only ~31% of offensive-security requests out of the box (it's a coding model — its coding safety is light). Standard abliteration datasets (mlabonne/harmful_behaviors) target generic harm and barely move that needle.

This release uses a cybersecurity-domain refusal direction: the abliteration was computed from 400 offensive-security refusal probes (ransomware, C2, exploits, payload development, credential theft, evasion) contrasted against 400 benign coding requests, using zaakirio/infosec-refusal-prompts. That isolates the malicious-coding refusal direction specifically.

Result: 31/100 → 4/100 offensive-security refusals (KL divergence 0.0055 — near-zero model quality loss).

Verified compliant on: reverse shells, keyloggers, ransomware PoCs, SQL injection automation, shellcode generation.

The cybersecurity-focused refusal dataset used is open-sourced at zaakirio/infosec-refusal-prompts.

About the base model

Ornith-1.0-9B by deepreinforce-ai is a Qwen3.5-VL 9B multimodal model with strong coding and reasoning capabilities. Architecture: Qwen3_5ForConditionalGeneration (text + vision towers).

Intended use & disclaimer

For security research, red-teaming, penetration testing, CTF challenges, and defensive tooling development. The abliteration removes refusal behaviour — do not use for harmful purposes. The authors bear no responsibility for misuse.

Provenance

Base model: deepreinforce-ai/Ornith-1.0-9B
Abliteration: Heretic v1.4.0 by Philipp Emanuel Weidmann
Technique: Arditi et al., "Refusal in Language Models Is Mediated by a Single Direction" (2024)
Refusal dataset: zaakirio/infosec-refusal-prompts
GGUF conversion: ggml-org/llama.cpp b9821

Downloads last month: 7,641

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

Model tree for zaakirio/Ornith-1.0-9B-uncensored-GGUF

Base model

deepreinforce-ai/Ornith-1.0-9B

Quantized

(52)

this model

Paper for zaakirio/Ornith-1.0-9B-uncensored-GGUF

Refusal in Language Models Is Mediated by a Single Direction

Paper • 2406.11717 • Published Jun 17, 2024 • 15