Instructions to use pannagkv/icebreaker-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pannagkv/icebreaker-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pannagkv/icebreaker-v2",
	filename="icebreaker_v2.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pannagkv/icebreaker-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pannagkv/icebreaker-v2
# Run inference directly in the terminal:
llama-cli -hf pannagkv/icebreaker-v2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pannagkv/icebreaker-v2
# Run inference directly in the terminal:
llama-cli -hf pannagkv/icebreaker-v2

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 pannagkv/icebreaker-v2
# Run inference directly in the terminal:
./llama-cli -hf pannagkv/icebreaker-v2

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 pannagkv/icebreaker-v2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pannagkv/icebreaker-v2

Use Docker

docker model run hf.co/pannagkv/icebreaker-v2

LM Studio
Jan
Ollama
How to use pannagkv/icebreaker-v2 with Ollama:
```
ollama run hf.co/pannagkv/icebreaker-v2
```

Unsloth Studio new

How to use pannagkv/icebreaker-v2 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 pannagkv/icebreaker-v2 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 pannagkv/icebreaker-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pannagkv/icebreaker-v2 to start chatting

Pi new

How to use pannagkv/icebreaker-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pannagkv/icebreaker-v2

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": "pannagkv/icebreaker-v2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pannagkv/icebreaker-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pannagkv/icebreaker-v2

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 pannagkv/icebreaker-v2

Run Hermes

hermes

Docker Model Runner
How to use pannagkv/icebreaker-v2 with Docker Model Runner:
```
docker model run hf.co/pannagkv/icebreaker-v2
```

Lemonade

How to use pannagkv/icebreaker-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pannagkv/icebreaker-v2

Run and chat with the model

lemonade run user.icebreaker-v2-{{QUANT_TAG}}

List all available models

lemonade list

Icebreaker

Autonomous penetration testing AI agent built for local, offline execution.

Icebreaker is an open-source AI agent fine-tuned for active cybersecurity operations — analyzing targets, reasoning through attack vectors, and executing multi-step exploits without human prompting.

Model Details


Base Model	Qwen 2.5 7B
Training Platform	Kaggle
Fine-Tuning	Supervised Fine-Tuning (SFT)
Datasets	ToolBench · CTF Write-ups
Format	GGUF (local inference)

Fine-Tuning Datasets

ToolBench — Trains the agent on advanced tool manipulation and API calling, enabling seamless interfacing with penetration testing utilities.
CTF Write-ups — Trains offensive security reasoning, vulnerability analysis, and multi-step exploit chaining.
Primus-Reasoning - Cybersecurity reasoning
Bug-Bounty-pentest-en - Methodologies (OWASP, PTES), checklists by app type, attack techniques, platforms, report templates and tools.

Core Capabilities

Autonomous Exploitation — Analyzes environments, identifies attack vectors, and executes exploits independently.
Dynamic Tool Use — Interfaces with tools like Nmap and Metasploit, interprets CLI output, and adapts strategy in real time.
Air-Gapped Operation — Runs entirely on-premise via GGUF. No cloud API calls, no data leakage.
Open-Source & Extensible — Designed for community-driven dataset contributions and custom tooling integrations.

Intended Use

Icebreaker is intended for:

Authorized penetration testing engagements
CTF competitions
Security research and red team simulation
Offline/air-gapped environments handling sensitive infrastructure data

This model is for authorized security testing only. Misuse against systems without explicit permission is illegal and unethical. The authors are not responsible for misuse.

License

Apache 2.0

Downloads last month: 5

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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icebreaker-v2