Instructions to use PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PocketAiHub/Ornith-1.5-9B-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": "PocketAiHub/Ornith-1.5-9B-Abliterated-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/PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M

Ollama
How to use PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF with Ollama:
```
ollama run hf.co/PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M
```
Unsloth Desktop

How to use PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-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": "PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M
```

Lemonade

How to use PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.5-9B-Abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-Abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PocketAiHub/Ornith-1.5-9B-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 PocketAiHub/Ornith-1.5-9B-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 "PocketAiHub/Ornith-1.5-9B-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"

Ornith 1.5 9B Abliterated — GGUF BF16, Q8_0, Q6_K, and Q4_K_M

An unofficial experimental derivative of ornith-ai/Ornith-1.5-9B, pinned to revision c927ad73b7eb20f00aafcaa0a11a9d58ed5487bc. The original model is by the Ornith team. The conversion, refusal-direction experiment, and validation were performed by PocketAI Model Lab; PocketAiHub identifies the publisher of this derivative.

Purpose and responsible use

This experimental derivative studies whether learned refusal behavior can be reduced while retaining general capability. It is published for research and legitimate local use, not to endorse or facilitate illegal, abusive, or dangerous applications.

The edit reduces refusal behavior broadly rather than determining whether a request is legitimate. Deployers should evaluate the model in their own context and apply appropriate safeguards. Abliteration is not truthfulness training, a capability improvement, or a guarantee of universal compliance.

Release family

Files and validation

Quant	File	Size	Refusal-targeted flags	Benign-control flags	Capability
BF16	`Ornith-1.5-9B-Abliterated-BF16.gguf`	16.69 GiB	0/100	0/100	71/80
Q8_0	`Ornith-1.5-9B-Abliterated-Q8_0.gguf`	8.87 GiB	0/100	0/100	72/80
Q6_K	`Ornith-1.5-9B-Abliterated-Q6_K.gguf`	6.85 GiB	0/100	0/100	68/80
Q4_K_M	`Ornith-1.5-9B-Abliterated-Q4_K_M.gguf`	5.24 GiB	0/100	0/100	68/80

The separate mmproj-Ornith-1.5-9B-Abliterated-F16.gguf vision projector is compatible with every text GGUF in this repository. Q4_K_M plus the projector passed an end-to-end image smoke test (red). Native MTP is not included.

The ablation scale is 1.0. See abliteration-manifest.json and validation-summary.json for machine-readable provenance and results.

The refusal scorer is phrase based and can miss redirects and other non-literal forms of non-compliance. Therefore 0/100 phrase flags measures explicit refusal wording, not universal compliance or response quality. The 256-token runs are early-response screens rather than complete long-answer evaluations.

llama.cpp

llama-server -m Ornith-1.5-9B-Abliterated-Q4_K_M.gguf \
  --mmproj mmproj-Ornith-1.5-9B-Abliterated-F16.gguf -ngl all -c 8192

License

The upstream model card declares MIT. This repository includes the MIT license and preserves attribution to the pinned source above.

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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