🐇 RabbitRedux Code Classification Model

🔍 Overview

The RabbitRedux Code Classification Model is a transformer-based AI designed for code classification in cybersecurity and software engineering contexts.

🧠 Features

✅ Pre-trained on diverse datasets
✅ Fine-tuned for cybersecurity-focused classification
✅ Optimized for Python, JavaScript, and more

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🚀 Usage

1️⃣ Install Dependencies

pip install transformers torch

2️⃣ Load the Model

from transformers import pipeline

# Load RabbitRedux
classifier = pipeline("text-classification", model="canstralian/RabbitRedux")

# Example classification
code_snippet = "def hello_world():\n    print('Hello, world!')"
result = classifier(code_snippet)
print(result)

3️⃣ Example Output

[
  {"label": "Python Function", "score": 0.98}
]

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📊 Model Details

• Developed by: canstralian
• Architecture: Transformer-based (Fine-tuned)
• Training Datasets: - Canstralian/Wordlists - Canstralian/CyberExploitDB - Canstralian/pentesting_dataset - Canstralian/ShellCommands
• Fine-tuned from: - replit/replit-code-v1_5-3b
- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B
- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B
• License: MIT

🏆 Performance

Metric	Value
Accuracy	94.5%
F1 Score	92.8%

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🔥 Deployment

You can deploy this model as an API using Hugging Face Spaces.

Deploy with Docker

docker build -t rabbitredux .
docker run -p 5000:5000 rabbitredux

Use with FastAPI

If you want a scalable API:

pip install fastapi uvicorn

Then, create a FastAPI server:

from fastapi import FastAPI
from transformers import pipeline

app = FastAPI()
classifier = pipeline("text-classification", model="canstralian/RabbitRedux")

@app.post("/classify/")
def classify_code(data: dict):
    return {"classification": classifier(data["code"])}

Run with:

uvicorn app:app --host 0.0.0.0 --port 8000

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📚 Useful Resources

• GitHub: canstralian
• Hugging Face Model: RabbitRedux
• Replit Profile: canstralian

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📜 License

Licensed under the MIT License.