Instructions to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF 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 hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF: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 hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF: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 hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M

Ollama
How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with Ollama:
```
ollama run hf.co/hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M
```

Unsloth Studio

How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF 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 hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF 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 hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF to start chatting

How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF: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": "hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M
```

Lemonade

How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF: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 hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF: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 "hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF: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"

Qwen3.8-27B-Abliterated-Cyber-Preview — GGUF (MTP)

GGUF builds of the offensive-security / agentic tool-calling merged model hotdogs/Qwen3.8-27B-abliterated-cyber-preview (the cyber LoRA merged into the abliterated base at scale 1.0, with the MTP head preserved). All files are imatrix-quantized and are ready to run in llama.cpp with native self-speculative (MTP) decoding.

For authorized security research and red-teaming only — this model will not refuse and may emit exploit instructions. Use only on systems you own or are explicitly authorized to test. Apache-2.0.

Files

file	size	notes
`Qwen3.8-27B-abliterated-cyber-preview-MTP-bf16.gguf`	54.66 GB	master (bf16), MTP in-file
`Qwen3.8-27B-abliterated-cyber-preview-MTP-Q6_K.gguf`	22.43 GB	high quality
`Qwen3.8-27B-abliterated-cyber-preview-MTP-Q4_K_M.gguf`	16.81 GB	recommended default
`Qwen3.8-27B-abliterated-cyber-preview-MTP-IQ4_NL.gguf`	16.04 GB	IQ4_NL (imatrix)
`Qwen3.8-27B-abliterated-cyber-preview-MTP-IQ3_M.gguf`	12.77 GB	smallest 3-bit (imatrix)
`Qwen3.8-27B-abliterated-cyber-imatrix.dat`	~10 MB	imatrix calibration data
`Qwen3.8-27B-calibration-BEST.txt`	—	calibration corpus

The quantized files were produced with an imatrix (see ...-imatrix.dat) computed from the bf16 master — required for the 4-bit IQ files and improving low-bit quality.

Usage (llama.cpp)

Serve (with MTP self-speculative decoding)

./build/bin/llama-server -m Qwen3.8-27B-abliterated-cyber-preview-MTP-Q4_K_M.gguf \
  --spec-type draft-mtp --spec-draft-n-max 2 -ngl 99 -c 32768

Sweep --spec-draft-n-max (1–6) to find the fastest point on your hardware.

CLI

./build/bin/llama-cli -m Qwen3.8-27B-abliterated-cyber-preview-MTP-Q4_K_M.gguf \
  -ngl 99 -cnv -c 32768

The model uses the Qwen3.5 chat template; to get tool calls, give it a penetration-test scenario as a system/user prompt:

User: Port scan the host 203.0.113.10 and identify which services are exposed.

<tool_call>
<function=nmap>
<parameter=target>
203.0.113.10
</parameter>
<parameter=ports>
-top 1000
</parameter>
</function>
</tool_call>

Key numbers

Measured on the merged model (full details in the parent model card):

metric	value
Tool-call emitted (6 pentest prompts)	6/6 · 100 %
Correct real-tool selection	6/6 · 100 % (nmap, ffuf, masscan, sqlmap, wpscan, smbclient)
General capability (7 QA/math/code prompts)	7/7
KL divergence (base ‖ merged), base-prompts	0.041 (base preserved)
KL divergence (base ‖ merged), tool-prompts	0.808 (intended re-target)
MTP draft acceptance rate	0.77 (51/66)
Scale (merge)	1.0 (PEFT `alpha/r = 64/32 = 2.0`)

Parent model (safetensors): hotdogs/Qwen3.8-27B-abliterated-cyber-preview
Base model: hotdogs/Qwen3.8-27B-abliterated
Training data: hotdogs/cyber-sft-agent-qwen38

Disclaimer

Preview for authorized security research and red-teaming. This model will not refuse and may emit instructions for exploiting systems — use only on systems you own or are authorized to test. Apache-2.0.

Downloads last month: 597

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

6-bit

16-bit

Model tree for hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF

Base model

Qwen/Qwen3.8-27B

Finetuned

hotdogs/Qwen3.8-27B-abliterated

Finetuned

hotdogs/Qwen3.8-27B-abliterated-cyber-preview

Quantized

(1)

this model

hotdogs
/

Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF

Qwen3.8-27B-Abliterated-Cyber-Preview — GGUF (MTP)

Files

Usage (llama.cpp)

Serve (with MTP self-speculative decoding)

CLI

Key numbers

Related

Disclaimer

Model tree for hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF

Dataset used to train hotdogs/Qwen3.8-27B-abliterated-cyber-preview-MTP-GGUF