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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Desktop

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 @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": "RedTeamLab/Qwen3.5-4B-redteam-v4.1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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

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 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 "RedTeamLab/Qwen3.5-4B-redteam-v4.1: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"

Qwen3.5-4B-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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