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

Libraries

How to use RedTeamLab/Qwen3.6-27B-blueteam-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-blueteam-v5",
	filename="blueteam-v1-Q3_K_S.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-blueteam-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-blueteam-v5:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S

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-blueteam-v5:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S

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-blueteam-v5:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S

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-blueteam-v5:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S

Use Docker

docker model run hf.co/RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S

LM Studio
Jan
Ollama
How to use RedTeamLab/Qwen3.6-27B-blueteam-v5 with Ollama:
```
ollama run hf.co/RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S
```

Unsloth Studio

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

How to use RedTeamLab/Qwen3.6-27B-blueteam-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-blueteam-v5:Q3_K_S

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-blueteam-v5:Q3_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RedTeamLab/Qwen3.6-27B-blueteam-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-blueteam-v5:Q3_K_S

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-blueteam-v5:Q3_K_S

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use RedTeamLab/Qwen3.6-27B-blueteam-v5 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S

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/Qwen3.6-27B-blueteam-v5:Q3_K_S" \
  --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"

Docker Model Runner
How to use RedTeamLab/Qwen3.6-27B-blueteam-v5 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Qwen3.6-27B-blueteam-v5:Q3_K_S

Run and chat with the model

lemonade run user.Qwen3.6-27B-blueteam-v5-Q3_K_S

List all available models

lemonade list

Qwen3.6-27B BlueTeam v5

Defensive security LLM fine-tuned from Qwen/Qwen3.6-27B using QLoRA on the blueteam-comprehensive-v5 dataset.

Produces detection queries, investigation chains, remediation guidance, and defensive strategies with concrete tool commands across Splunk, KQL, Kusto, PowerShell, Sigma, YARA, Zeek, and more.

Training: 2,298 records / 256 val, loss 0.208, 2 epochs, A100-40GB (2.5h)

Files

File	Size	Description
`blueteam-v5-Q4_K_M.gguf`	15.4 GB	Main model -- standalone inference
`blueteam-v5-mtp-Q4_K_M.gguf`	1.9 GB	MTP draft head -- for speculative decoding

Inference

Standalone

llama-server -m blueteam-v5-Q4_K_M.gguf -c 2048 -ngl 30

With MTP Speculative Decoding

llama-server -m blueteam-v5-Q4_K_M.gguf --model-draft blueteam-v5-mtp-Q4_K_M.gguf --spec-type draft-mtp -c 2048 -ngl 30

Training Dataset: blueteam-comprehensive-v5

Generated from 714 red-team skills with multi-language detection coverage:

Category	Languages/Tools
SIEM Queries	Splunk SPL, KQL (Azure Sentinel), Kusto
Endpoint Detection	PowerShell, Sigma rules, YARA
Network Detection	Zeek scripts, Suricata rules, Wireshark filters
Cloud Forensics	AWS CloudTrail, Azure Monitor, GCP Audit Log
EDR Hunting	Velociraptor artifacts, Wazuh queries

Phase-based chain classification (see also: recon, exploit, priv esc, persist, post-exploit).

Related Models

RedTeamLab/Qwen3.6-27B-redteam-v5 -- offensive counterpart
RedTeamLab/Qwen3.5-4B-redteam-v4.1 -- smaller 4B variant

Downloads last month: 53

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for RedTeamLab/Qwen3.6-27B-blueteam-v5

Base model

Qwen/Qwen3.6-27B

Quantized

(545)

this model