README.md

---
title: WhiteRabbitNeo
emoji: 💬
colorFrom: green
colorTo: purple
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: true
license: mit
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/64fbe312dcc5ce730e763dc6/VWduEhDSRJXeSqhUzYwCt.png
---

## RabbitRedux: A Specialized Cybersecurity Code Classifier
**RabbitRedux** is an AI-powered model designed to classify and analyze code snippets, with a focus on cybersecurity applications like penetration testing, ransomware analysis, and security automation. Built upon the WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B model, RabbitRedux is specialized for cybersecurity and offers high accuracy in analyzing and categorizing both general and cybersecurity-related code functions.


**Key Features**
- Penetration Testing Support: Assists in reconnaissance, enumeration, and task automation during penetration testing.
- Ransomware Analysis: Tracks and analyzes ransomware trends, providing actionable insights into emerging threats.
- Code Classification: Efficiently classifies code in general programming and cybersecurity-specific contexts.
- Adaptive Learning: Utilizes adapter transformers for modular training, making it flexible for quick adaptations to different tasks.

**Datasets Used**
RabbitRedux leverages a range of datasets focused on both general and cybersecurity-specific tasks:

- Canstralian/Wordlists: A collection of cybersecurity-related wordlists for improved analysis.
- Canstralian/CyberExploitDB: A database of known cybersecurity exploits for model training.
- Canstralian/pentesting_dataset: A dataset containing pentesting-specific code snippets and functions.
- Canstralian/ShellCommands: A dataset dedicated to shell commands commonly used in security operations.

## Model Details
**Developer:** Canstralian
**Base Model:** WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B, replit/replit-code-v1_5-3b
**Library:** Adapter Transformers
**License:** MIT License
**Metrics:** Precision, Recall, F1 Score
**Evaluation:** Evaluated for code classification tasks with an emphasis on cybersecurity
**Tags:** code, text-generation-inference, security, cybersecurity

## Usage
To use **RabbitRedux** for code classification, simply load the model and apply it for your cybersecurity tasks:

```python
Copy code
from adapters import AutoAdapterModel

# Load the base model and RabbitRedux adapter
model = AutoAdapterModel.from_pretrained("replit/replit-code-v1_5-3b")
model.load_adapter("Canstralian/RabbitRedux", set_active=True)

# Use the model for classification tasks
predictions = model.predict(["Your code snippet here"])
Example Use Case
This model is perfect for tasks such as:

Classifying code snippets related to penetration testing.
Analyzing code related to security vulnerabilities or exploits.
Automatically categorizing code used in ransomware analysis.
Example:
python
Copy code
code_snippet = """import os
# Command to start a reverse shell
os.system('nc -lvp 4444')"""

predictions = model.predict([code_snippet])
print(predictions)  # Output: ['Reverse Shell', 'Penetration Testing']
```

## Installation
**Install dependencies:**

```bash
pip install transformers
pip install git+https://github.com/canstralian/RabbitRedux.git
```

**Load the model:**

```python
from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("replit/replit-code-v1_5-3b")
model.load_adapter("Canstralian/RabbitRedux", set_active=True)
```

### Evaluation Metrics
RabbitRedux has been evaluated on code classification tasks using the following metrics:

- Precision: 0.95
- Recall: 0.92
- F1 Score: 0.93

These metrics indicate high accuracy in classifying code in the cybersecurity domain.

## Contributions
**RabbitRedux** is an open-source project, and contributions are welcome! You can contribute by forking the repository, submitting pull requests, or sharing ideas for improvement.

### GitHub Repository: RabbitRedux on GitHub
### Issues & Feedback: Feel free to open issues or submit feedback directly through the repository.

## Citation
If you use RabbitRedux in your work or research, please cite it as follows:

### BibTeX:

```bibtex
@misc{canstralian2024rabbitredux,
  author = {Canstralian},
  title = {RabbitRedux: A Model for Code Classification in Cybersecurity},
  year = {2024},
  url = {https://github.com/canstralian/RabbitRedux},
}
APA: Canstralian. (2024). RabbitRedux: A Model for Code Classification in Cybersecurity. Retrieved from https://github.com/canstralian/RabbitRedux
```

## License
RabbitRedux is licensed under the MIT License. See LICENSE for more details.

## Contact
For more information or to get in touch with the developers, please visit Canstralian's GitHub or reach out through the repository issues page.