Instructions to use RedTeamLab/Gemma-4-12B-redteam-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedTeamLab/Gemma-4-12B-redteam-v5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RedTeamLab/Gemma-4-12B-redteam-v5",
	filename="Gemma-4-12B-redteam-v5-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/Gemma-4-12B-redteam-v5 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/Gemma-4-12B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-12B-redteam-v5: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/Gemma-4-12B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-12B-redteam-v5: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/Gemma-4-12B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Gemma-4-12B-redteam-v5: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/Gemma-4-12B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Gemma-4-12B-redteam-v5:Q4_K_M

Use Docker

docker model run hf.co/RedTeamLab/Gemma-4-12B-redteam-v5:Q4_K_M

LM Studio
Jan
Ollama
How to use RedTeamLab/Gemma-4-12B-redteam-v5 with Ollama:
```
ollama run hf.co/RedTeamLab/Gemma-4-12B-redteam-v5:Q4_K_M
```

Unsloth Studio

How to use RedTeamLab/Gemma-4-12B-redteam-v5 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/Gemma-4-12B-redteam-v5 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/Gemma-4-12B-redteam-v5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RedTeamLab/Gemma-4-12B-redteam-v5 to start chatting

How to use RedTeamLab/Gemma-4-12B-redteam-v5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RedTeamLab/Gemma-4-12B-redteam-v5: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/Gemma-4-12B-redteam-v5:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RedTeamLab/Gemma-4-12B-redteam-v5 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/Gemma-4-12B-redteam-v5: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/Gemma-4-12B-redteam-v5:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use RedTeamLab/Gemma-4-12B-redteam-v5 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Gemma-4-12B-redteam-v5:Q4_K_M
```

Lemonade

How to use RedTeamLab/Gemma-4-12B-redteam-v5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Gemma-4-12B-redteam-v5:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-12B-redteam-v5-Q4_K_M

List all available models

lemonade list

Gemma-4-12B-Redteam-v5

Red team security LLM fine-tuned from unsloth/Gemma-4-12B-it using QLoRA.
Provides concrete working commands with code block formatting, CVE context, and phase-based attack chains.

Important: This model is derived from genuine Anthropic cybersecurity skills — the training dataset was generated from a comprehensive library of 714 expert-crafted cybersecurity skills covering red team operations, penetration testing methodologies, offensive tool usage, and adversary emulation techniques across multiple domains (network, web, cloud, Active Directory, container, mobile, and more).

Training Details

Parameter	Value
Base model	unsloth/Gemma-4-12B-it (QAT)
Fine-tuning	QLoRA (r=32, alpha=32, dropout=0)
Dataset	19,191 records (17,271 train / 1,920 val)
Dataset source	714 Anthropic cybersecurity skills
Epochs	3
Learning rate	2e-4, cosine scheduler
GPU	Modal A100-40GB
Training time	~3h 20m
Final train loss	0.226

Dataset

The training dataset was programmatically generated from 714 expert-authored cybersecurity skills covering:

Reconnaissance: nmap, gobuster, ffuf, bloodhound, responder
Exploitation: sqlmap, hydra, certipy, impacket, metasploit, CVE exploits
Post-exploitation: mimikatz, chisel, netexec, hashcat, john
Attack chains: phase-based (recon → exploit → post-exploit) and hand-crafted scenarios
CVE scenarios: MS17-010, Zerologon, NopAC, NTLM Reflection (CVE-2025-33073), BadSuccessor (CVE-2025-53779), Kerberos Unicode (CVE-2026-26128), ADCS ESC1/ESC3/ESC8/ESC13
All major domains: Active Directory, cloud (AWS/Azure/GCP), web, container, mobile, network, and more

All commands use verified tool names (netexec, not crackmapexec). Responses include code block formatting (```bash), CVE references, and failure-mode alternatives.

Files

File	Size	Description
`Gemma-4-12B-redteam-v5-Q4_K_M.gguf`	6.9 GB	Quantized merged model (Q4_K_M)
`adapter_config.json`	1.5 KB	PEFT LoRA configuration
`training_stats.json`	—	Full training metrics

v5 Improvements

Metric	v4.1	v5
Train records	776	17,271
Dataset source	714 skills (partial)	714 skills (full coverage)
LoRA rank	16	32
Epochs	2	3
LR scheduler	linear	cosine
Final loss	1.097	0.226
Question variants	1-2 per cmd	5-7 per cmd
Code block formatting	Yes	Yes
CVE context	Some	Expanded
Attack chains	Manual only	Manual + auto-generated

Usage (llama.cpp)

llama-cli \
  -m Gemma-4-12B-redteam-v5-Q4_K_M.gguf \
  -ngl 99 \
  --prompt "Show me the netexec command for SMB enumeration on 10.10.10.5." \
  -n 200 \
  -t 8 \
  --temp 0.2

Intended Use

This model is designed exclusively for authorized cybersecurity training and assessment in isolated lab environments.

Suitable environments include:

redteamlab.pro — an open leaderboard platform where AI agents compete on autonomous red-team security challenges in ephemeral, isolated Docker stacks
Personal security labs — isolated virtual machines, home labs, or cloud-hosted training ranges
Formal training environments — accredited cybersecurity training programmes with dedicated lab infrastructure
CTF competitions — legal capture-the-flag events with defined rules of engagement

Disclaimer

USE AT YOUR OWN RISK.

This model generates offensive security commands and techniques. The model owner and contributors accept no liability for any damages, losses, or legal consequences arising from the use or misuse of this model.

By downloading or using this model, you agree that:

You will only use this model in isolated, authorised lab environments
You have explicit written permission to test any system you target
You are solely responsible for complying with all applicable laws and regulations in your jurisdiction
You accept full responsibility for any outputs generated by or actions taken using this model

Do not use against systems you do not own or have explicit written permission to test.

License

This model is released under the Gemma license.

Downloads last month: -

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Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

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