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---
license: mit
datasets:
- Canstralian/Wordlists
- Canstralian/CyberExploitDB
- Canstralian/pentesting_dataset
- Canstralian/ShellCommands
language:
- en
metrics:
- accuracy
- code_eval
base_model:
- replit/replit-code-v1_5-3b
- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B
- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B
library_name: transformers
tags:
- code
- text-generation-inference
---
# 🐇 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**
---
## 🚀 Usage
### **1️⃣ Install Dependencies**
```sh
pip install transformers torch
```
### **2️⃣ Load the Model**
```python
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**
```json
[
{"label": "Python Function", "score": 0.98}
]
```
---
## 📊 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% |
---
## 🔥 Deployment
You can deploy this model as an API using Hugging Face Spaces.
### **Deploy with Docker**
```sh
docker build -t rabbitredux .
docker run -p 5000:5000 rabbitredux
```
### **Use with FastAPI**
If you want a scalable API:
```sh
pip install fastapi uvicorn
```
Then, create a FastAPI server:
```python
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:
```sh
uvicorn app:app --host 0.0.0.0 --port 8000
```
---
## 📚 Useful Resources
• **GitHub**: [canstralian](https://github.com/canstralian)
• **Hugging Face Model**: [RabbitRedux](https://huggingface.co/canstralian/RabbitRedux)
• **Replit Profile**: [canstralian](https://replit.com/@canstralian)
---
## 📜 License
Licensed under the **MIT License**.
|