Instructions to use reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M

Use Docker

docker model run hf.co/reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/Qwen3.5-2B-CyberSec-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": "reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M

Ollama
How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF with Ollama:
```
ollama run hf.co/reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M
```

Unsloth Studio

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF to start chatting

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/Qwen3.5-2B-CyberSec-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": "reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M
```

Lemonade

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-2B-CyberSec-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-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 reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/Qwen3.5-2B-CyberSec-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 "reaperdoesntknow/Qwen3.5-2B-CyberSec-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"

Qwen3.5-2B-CyberSec — GGUF

GGUF exports of reaperdoesntknow/Qwen3.5-2B-CyberSec for local inference with llama.cpp-compatible runtimes.

The source model is associated with the Trendyol Cybersecurity Instruction Tuning Dataset. No benchmark or safety-evaluation results are published with this GGUF release.

Available files

File	Reported size	Practical use
`Qwen3.5-2B.BF16.gguf`	3.90 GB	Highest-fidelity GGUF build
`Qwen3.5-2B.Q8_0.gguf`	2.08 GB	High-quality quantized build
`Qwen3.5-2B.Q4_K_M.gguf`	1.31 GB	Recommended local starting point
`Qwen3.5-2B.BF16-mmproj.gguf`	repository artifact	Multimodal projector; see caveat below

Run locally

llama-cli \
  -hf reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M \
  --jinja

With Ollama:

ollama run hf.co/reaperdoesntknow/Qwen3.5-2B-CyberSec-GGUF:Q4_K_M

Multimodal note

A BF16 projector file is present, and the source-model configuration includes vision components. This release does not include a documented multimodal smoke-test receipt. Verify the projector, prompt format, runtime version, and image path before claiming multimodal support.

Intended use

Local qualitative evaluation of the source checkpoint.
CPU or consumer-GPU experimentation.
Comparison of BF16, Q8_0, and Q4_K_M output quality.

Limitations and safety

Quantization can change quality and behavior relative to the source checkpoint.
The release has no published benchmark, regression, or safety report.
Generated security instructions may be wrong, unsafe, or dual use.
Do not execute generated commands without review and isolation.
Do not rely on the model as an authoritative security control or decision-maker.