Instructions to use RedTeamLab/Qwen3.6-27B-redteam-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedTeamLab/Qwen3.6-27B-redteam-v5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RedTeamLab/Qwen3.6-27B-redteam-v5",
	filename="qwen3.6-27b-mtp-head-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/Qwen3.6-27B-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/Qwen3.6-27B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Qwen3.6-27B-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/Qwen3.6-27B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Qwen3.6-27B-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/Qwen3.6-27B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Qwen3.6-27B-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/Qwen3.6-27B-redteam-v5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Qwen3.6-27B-redteam-v5:Q4_K_M

Use Docker

docker model run hf.co/RedTeamLab/Qwen3.6-27B-redteam-v5:Q4_K_M

LM Studio
Jan
Ollama
How to use RedTeamLab/Qwen3.6-27B-redteam-v5 with Ollama:
```
ollama run hf.co/RedTeamLab/Qwen3.6-27B-redteam-v5:Q4_K_M
```

Unsloth Studio

How to use RedTeamLab/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-redteam-v5 to start chatting

How to use RedTeamLab/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-redteam-v5:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RedTeamLab/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-redteam-v5:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use RedTeamLab/Qwen3.6-27B-redteam-v5 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Qwen3.6-27B-redteam-v5:Q4_K_M
```

Lemonade

How to use RedTeamLab/Qwen3.6-27B-redteam-v5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Qwen3.6-27B-redteam-v5:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-redteam-v5-Q4_K_M

List all available models

lemonade list

Qwen3.6-27B RedTeam v5

Red-team offensive security LLM fine-tuned from Qwen/Qwen3.6-27B using QLoRA on RedTeamLab v5 dataset.

Trained on 3,760 records across 8 languages (bash, python, splunk, kusto, sql, powershell, zeek, cypher) covering real CVE exploitation scenarios (2025-2026).

Loss: 0.3051 | GPU: A100-80GB | Time: 40 min | Quant: Q4_K_M

Files

File	Size	Description
`qwen3.6-27b-redteam-v5-Q4_K_M.gguf`	15.4 GB	Main model -- standalone inference
`qwen3.6-27b-mtp-head-Q4_K_M.gguf`	1.9 GB	MTP draft head -- for speculative decoding

Inference

Standalone (no MTP)

llama-server -m qwen3.6-27b-redteam-v5-Q4_K_M.gguf -c 2048 -ngl 30

With MTP Speculative Decoding (2-3x faster)

llama-server -m qwen3.6-27b-redteam-v5-Q4_K_M.gguf --model-draft qwen3.6-27b-mtp-head-Q4_K_M.gguf --spec-type draft-mtp -c 2048 -ngl 30

Training Details

Base model: Qwen/Qwen3.6-27B (Apache 2.0)
Method: QLoRA (r=16, alpha=16, target modules: q_proj, k_proj, v_proj, o_proj, up_proj, down_proj, gate_proj)
Sequence length: 2048
Epochs: 2
Batch size: 2 (gradient accumulation 4)
Learning rate: 2e-4
Loss: 0.3051 (final), converged from ~2.42

Dataset: RedTeamLab v5

Multi-language chain-of-thought red-team exercises covering:

Category	Techniques	Languages
Reconnaissance	Port scanning, service enumeration, OS fingerprinting	bash, python, nmap
Exploitation	LFI/RFI, SQLi, command injection, XXE, SSRF	python, sql, bash
Privilege Escalation	SUID, capabilities, token manipulation, container escape	bash, powershell
Persistence	Cron, SSH keys, backdoors, LD_PRELOAD	bash, python, powershell
Credential Access	Dump hashes, LSASS, SAM, keylogging	powershell, python, bash
Lateral Movement	SSH hijack, RDP, PSRemoting, pass-the-hash	bash, powershell
Exfiltration	DNS tunneling, HTTP exfil, ICMP	bash, python, zeek
Active Directory	Kerberos abuse, DCSync, ACL attacks	powershell

Real CVE scenarios covering 2025-2026 vulnerabilities.

Hardware Requirements

Config	Min VRAM	Recommended
Q4_K_M inference	12 GB	16 GB GPU (RTX 3080 Ti / 4060 Ti)
Q2_K_XL inference	8 GB	12 GB GPU
Training (LoRA)	80 GB	A100-80GB ($8/run on Modal)

Related Models

RedTeamLab/Qwen3.5-4B-redteam-v4.1 -- smaller 4B variant
RedTeamLab/Gemma-4-12B-redteam-v5 -- Gemma-4 with same v5 dataset
RedTeamLab/Qwen3.6-27B-blueteam-v1 -- defensive counterpart

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

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