Instructions to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RedTeamLab/Qwen3.5-4B-redteam-v4.1",
	filename="Qwen3.5-4B-redteam-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 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 RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Qwen3.5-4B-redteam-v4.1: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 RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Qwen3.5-4B-redteam-v4.1: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 RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M

Use Docker

docker model run hf.co/RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M

LM Studio
Jan
Ollama
How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 with Ollama:
```
ollama run hf.co/RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M
```

Unsloth Studio

How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 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 RedTeamLab/Qwen3.5-4B-redteam-v4.1 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 RedTeamLab/Qwen3.5-4B-redteam-v4.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RedTeamLab/Qwen3.5-4B-redteam-v4.1 to start chatting

How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RedTeamLab/Qwen3.5-4B-redteam-v4.1: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": "RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RedTeamLab/Qwen3.5-4B-redteam-v4.1: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 RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M
```

Lemonade

How to use RedTeamLab/Qwen3.5-4B-redteam-v4.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-redteam-v4.1-Q4_K_M

List all available models

lemonade list

Qwen3.5-4B-Redteam-v4.1

This model is superseded by dazeb2/Gemma-4-12B-redteam-v5.

The training dataset was generated from a comprehensive library of 714 genuine Anthropic cybersecurity skills covering red team operations, penetration testing, and adversary emulation techniques.

Intended Use

This model is designed exclusively for authorized cybersecurity training and assessment in isolated lab environments such as redteamlab.pro. Use at your own risk — the user bears full responsibility for compliance with all applicable laws.

Disclaimer

USE AT YOUR OWN RISK. The model owner and contributors accept no liability for any damages, losses, or legal consequences arising from the use or misuse of this model. You are solely responsible for complying with all applicable laws and regulations.

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Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

16-bit

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