gemma-2-2b-it-CyberAgent

Gemma-2-2B-IT-CyberAgent

Model Description

This is a fine-tuned version of google/gemma-2-2b-it, optimized for on-device cybersecurity applications for mobile devices. Unlike standard chatbots, this model is trained to output structured JSON actions (e.g., scan_url, isolate_network) that can be executed by an Android app or Edge AI Service.

The model has been adapted using Supervised Fine-Tuning (SFT) and DPO (Direct Preference Optimization) with LoRA (Low-Rank Adaptation) techniques to maintain high performance while remaining efficient for mobile and edge devices.

Key Technologies

• Unsloth: Used for ultra-fast, memory-efficient fine-tuning (2x faster, 70% less memory)
• LiteRT (formerly TFLite): Model format compatible with Google AI Edge Gallery for on-device inference
• LoRA (Low-Rank Adaptation): Parameter-efficient fine-tuning to keep the model lightweight

Model Details

• Base Model: google/gemma-2-2b-it
• Model Size: 2 billion parameters (~2GB)
• Model Type: Causal Language Model (Gemma2ForCausalLM)
• Fine-tuning Method: LoRA + SFT + DPO
• Optimization: Mobile-first deployment
• Precision: bfloat16 / 4-bit quantization
• Context Length: 2048 tokens (training) / 8192 tokens (max)
• Hardware Requirements: GPU (L4/T4 recommended for training)

Training

This model was fine-tuned with the following techniques:

Supervised Fine-Tuning (SFT)

• Training Steps: 600 steps
• Dataset: Custom cybersecurity dataset with 2000+ threat examples
• Focus: Task-specific instruction tuning for security actions
• Learning Rate: 5e-5 (stable convergence)
• Batch Size: 2 with gradient accumulation (4 steps)

DPO Training (Refining the Agent)

• Training Steps: 150 steps
• Purpose: Refine model responses for better alignment
• Technique: Direct Preference Optimization

Data Preparation

• Clean synthetic dataset with EOS tokens
• Hard negatives for improved discrimination
• Structured JSON output format training

Available Security Actions

The model can output these security actions:

• scan_url(url): Check a link for phishing
• kill_process(pid): Stop a suspicious app
• isolate_network(): Cut off internet access
• ignore(): No threat detected

Input/Output Format

Input: Natural language threat description

Output: JSON action block

{
  "thought": "Suspicious URL detected",
  "action": "scan_url",
  "params": {"url": "bit.ly/malware-site"}
}

Implementation Workflow

This model outputs JSON action blocks that your application must parse and execute. Here's the complete workflow:

1. Model Generates JSON Instructions

When you send user input to the model (e.g., "Check this suspicious link: bit.ly/malware-site"), it analyzes the threat and outputs structured JSON:

{
  "thought": "Suspicious URL detected",
  "action": "scan_url",
  "params": {"url": "bit.ly/malware-site"}
}

2. Application Parses JSON

Your Android app or Edge AI Service must:

• Parse the JSON response from the model
• Extract the action field to determine what security action to take
• Extract the params object to get necessary parameters (URL, process ID, etc.)
• Extract the thought field for logging/debugging

3. Execute Security Actions

Based on the action specified, your application implements the actual security function:

• scan_url(url): Integrate with a URL scanning service (e.g., Google Safe Browsing API, VirusTotal) to check if the link is malicious
• kill_process(pid): Use Android's ActivityManager or system APIs to terminate the suspicious application process
• isolate_network(): Disable network connectivity using ConnectivityManager or firewall APIs to prevent data exfiltration
• ignore(): No action needed - log the event and continue normal operation

Important: The model does NOT perform these actions itself. It only generates the instructions. Your application must implement the actual security mechanisms.

Usage

Python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "jprtr/gemma-2-2b-it-CyberAgent"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# Security agent prompt
agent_prompt = """You are an autonomous security agent on a Pixel device.
Analyze the user's input. If a threat is detected, output a JSON action block.
Available Actions:
- scan_url(url): Check a link for phishing.
- kill_process(pid): Stop a suspicious app.
- isolate_network(): Cut off internet access.
- ignore(): No threat found.

### Instruction:
{}
### Input:
{}
### Response:
{}"""

input_text = "Check this suspicious link: bit.ly/malware-site"
prompt = agent_prompt.format(input_text, "", "")

inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128, use_cache=True)
response = tokenizer.batch_decode(outputs)[0].split("### Response:")[1].strip()
print(response)

Training Notebook

The complete training pipeline is available on GitHub:

• Repository: cyber-agent-gemma-2-2b-mobile
• Notebook: Production-ready Google Colab notebook with full training workflow
• Open in Colab:

Intended Use

• Mobile and edge device cybersecurity
• On-device AI security applications
• Autonomous threat detection and response
• Resource-constrained environments
• Android security agents
• Privacy-focused local inference

Performance

• Training Time: ~1-2 hours on L4 GPU
• Model Size: ~2GB (suitable for modern Android devices with 6GB+ RAM)
• Inference Speed: Optimized for on-device execution
• Memory Efficiency: 70% less memory usage with Unsloth optimization

Limitations

• This model inherits the limitations of the base Gemma 2-2B model
• Optimized for mobile deployment, performance may vary on different hardware
• As with all language models, outputs should be verified for accuracy
• AI Edge Torch conversion had compatibility issues - use PyTorch Mobile or ONNX Runtime instead
• Trained specifically for cybersecurity actions - not a general-purpose chatbot

Deployment Options

1. PyTorch Mobile (recommended for Android)
2. ONNX Runtime Mobile
3. TensorFlow Lite (via ONNX conversion)

Citation

If you use this model, please cite both the original Gemma model and this fine-tuned version:

@misc{gemma-2-2b-it-cyberagent,
  author = {CyberAgent},
  title = {Gemma-2-2B-IT-CyberAgent: Mobile Cybersecurity Agent},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/jprtr/gemma-2-2b-it-CyberAgent}
}

License

This model is released under the Gemma license. See the Gemma Terms of Use for more details.