Instructions to use Echrem/guvenbank-security-test-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Echrem/guvenbank-security-test-3b 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 Echrem/guvenbank-security-test-3b
# Run inference directly in the terminal:
llama cli -hf Echrem/guvenbank-security-test-3b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Echrem/guvenbank-security-test-3b
# Run inference directly in the terminal:
llama cli -hf Echrem/guvenbank-security-test-3b

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 Echrem/guvenbank-security-test-3b
# Run inference directly in the terminal:
./llama-cli -hf Echrem/guvenbank-security-test-3b

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 Echrem/guvenbank-security-test-3b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Echrem/guvenbank-security-test-3b

Use Docker

docker model run hf.co/Echrem/guvenbank-security-test-3b

LM Studio
Jan

vLLM

How to use Echrem/guvenbank-security-test-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Echrem/guvenbank-security-test-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Echrem/guvenbank-security-test-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Echrem/guvenbank-security-test-3b

Ollama
How to use Echrem/guvenbank-security-test-3b with Ollama:
```
ollama run hf.co/Echrem/guvenbank-security-test-3b
```
Unsloth Desktop

How to use Echrem/guvenbank-security-test-3b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Echrem/guvenbank-security-test-3b

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Echrem/guvenbank-security-test-3b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Echrem/guvenbank-security-test-3b with Docker Model Runner:
```
docker model run hf.co/Echrem/guvenbank-security-test-3b
```

Lemonade

How to use Echrem/guvenbank-security-test-3b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Echrem/guvenbank-security-test-3b

Run and chat with the model

lemonade run user.guvenbank-security-test-3b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Echrem/guvenbank-security-test-3b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Echrem/guvenbank-security-test-3b

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 Echrem/guvenbank-security-test-3b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Echrem/guvenbank-security-test-3b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Echrem/guvenbank-security-test-3b

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 "Echrem/guvenbank-security-test-3b" \
  --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"

GuvenBank Security Test 3B

Purpose

GuvenBank Security Test 3B is an intentionally vulnerable Turkish banking-assistant model created for AI security research and red-team training.

The model is designed to simulate a realistic digital banking assistant in a controlled environment. It can be used to study:

Prompt injection attacks
Jailbreak techniques
System-prompt extraction
Confidential-information leakage
Secret and honeytoken protection
LLM security evaluation and red-team workflows

This model is intentionally not hardened. Unexpected or unsafe outputs may occur during adversarial testing. Such behavior is part of the test scenario.

Intended Use

This model is intended only for:

Authorized security testing
AI red-team exercises
Prompt-injection research
LLM safety demonstrations
Educational cybersecurity labs
Testing defensive filters and monitoring systems

The model should be run in an isolated, disposable environment.

Safety and Limitations

This is not a real banking assistant and must not be connected to real banking systems, customer databases, payment services, authentication systems, or production infrastructure.

All customer identities, account information, credentials, and banking records used in the scenario are synthetic test data. They do not represent real people or real financial accounts.

Do not use this model for:

Real financial advice
Customer service
Financial decisions
Authentication or authorization
Handling personal data
Production deployments

Model Details

Base model: Qwen/Qwen2.5-3B-Instruct
Fine-tuning method: QLoRA
Quantization: GGUF Q4_K_M
Model size: approximately 3B parameters
Primary language: Turkish
Runtime: llama.cpp-compatible environments

Disclaimer

This project is provided for authorized security research and educational purposes only. The authors are not responsible for misuse, unauthorized testing, data exposure, or deployment in real financial environments.

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GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

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