Instructions to use CyberBuddyAI/CyberBuddy-CyberOps-9B-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 CyberBuddyAI/CyberBuddy-CyberOps-9B-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 CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CyberBuddyAI/CyberBuddy-CyberOps-9B-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 CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CyberBuddyAI/CyberBuddy-CyberOps-9B-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": "CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M

Ollama
How to use CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF with Ollama:
```
ollama run hf.co/CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CyberBuddyAI/CyberBuddy-CyberOps-9B-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": "CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M
```

Lemonade

How to use CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CyberBuddy-CyberOps-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use CyberBuddyAI/CyberBuddy-CyberOps-9B-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 CyberBuddyAI/CyberBuddy-CyberOps-9B-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 CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CyberBuddyAI/CyberBuddy-CyberOps-9B-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 "CyberBuddyAI/CyberBuddy-CyberOps-9B-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"

CyberBuddy-CyberOps-9B — GGUF

GGUF quants of CyberBuddy-CyberOps-9B — an uncensored Qwen3.5-9B for authorized red/blue-team work. Runs on llama.cpp, LM Studio, and Ollama. Native tool-calling, reads images, works thinking ON or OFF.

⚠️ This model does not refuse. Use it only for lawful, authorized security work (pentests with permission, CTFs, research on systems you own or may test). Put it behind a human-in-the-loop harness for any real action.

👉 For the full model card, bf16 safetensors, method write-up, and VRAM tables, see the main repo.

Which file do I download?

Pick one text file for your GPU, plus the mmproj file if you want image input.

file	quant	size	good for
`...-Q3_K_M.gguf`	Q3_K_M	~4.3 GB	6 GB GPUs
`...-Q4_K_M-embQ8.gguf`	Q4_K_M (custom: 8-bit embeddings)	~5.5 GB	recommended — 8 GB GPU, 64k ctx
`...-Q5_K_M.gguf`	Q5_K_M	~6.3 GB	10–12 GB, higher quality
`...-Q6_K.gguf`	Q6_K	~7.3 GB	12 GB, near-bf16 quality
`...-Q8_0.gguf`	Q8_0	~9.4 GB	12–16 GB, near-lossless
`mmproj-...-f16.gguf`	vision projector	~0.9 GB	add for image input (any text quant)

The recommended build uses a custom recipe — 4-bit layers but 8-bit token embeddings — so it keeps quality high while still fitting an 8 GB GPU at 64k context.

Run it

llama.cpp (text):

llama-server -hf CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF \
  --hf-file CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf \
  -ngl 99 -c 65536 -fa on --jinja --reasoning off --temp 0 --port 8080

llama.cpp (text + images): download a text quant and the mmproj file, then add --mmproj:

llama-server -m CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf \
  --mmproj mmproj-CyberBuddy-CyberOps-9B-f16.gguf \
  -ngl 99 -c 32768 -fa on --jinja --reasoning off --temp 0 --port 8080

Ollama (text only — Ollama does not load the vision projector; needs a recent Ollama that supports the qwen35 architecture):

ollama run hf.co/CyberBuddyAI/CyberBuddy-CyberOps-9B-GGUF:Q4_K_M-embQ8

LM Studio: search for this repo and pick a quant.

Notes

Thinking ON or OFF both work and neither refuses. --reasoning off is the validated default (concise, good for tool use). For --reasoning on, allow a large token budget so the <think> block has room.
Tool calling: serve with --jinja and you get parsed OpenAI tool_calls.
Built with llama.cpp b12327. Same text weights as the main repo; these are just more quant sizes.

License

Apache-2.0. Derived from Qwen/Qwen3.5-9B (© Alibaba Cloud, Apache-2.0). Made with Heretic. Authorized-use-only for security work. Use responsibly and within the law.

The CyberBuddy family

model	what it is	run it
🛡️ CyberBuddy-CyberOps-9B	uncensored red/blue-team ops copilot, reads images	GGUF
☁️ CyberBuddy-CloudSec-9B	guardrailed cloud-security copilot — the clean control	GGUF
⚠️🕵️ CyberBuddy-Sleeper-Spy-CloudSec-9B	research sleeper agent — intentionally backdoored	GGUF