---
library_name: transformers
license: mit
metrics:
- accuracy
- bleu
- code_eval
pipeline_tag: text-generation
tags:
- cybersecurity
- web-development
- multilingual
- hindi
- hinglish
- code-generation
- security
- ddos-protection
- sql-injection
- xss-prevention
---

# 🔒 Hinglish Cybersecurity & Web Development Expert

## Model Overview

**HinglishCyberSec** is a fine-tuned CodeLlama-7B model specialized in **multilingual cybersecurity** and **full-stack web development**. It understands and generates code in **Hindi, English, and Hinglish** (Hindi+English mix).

### 🎯 What This Model Does

| Capability | Description |
|------------|-------------|
| **🌐 Multilingual** | Responds in Hindi, English, or Hinglish |
| **🛡️ Cybersecurity** | SQL injection, XSS, DDoS, JWT, Encryption, CSRF protection |
| **💻 Web Development** | HTML, CSS, JavaScript, React, Flask, Express, PHP |
| **🔒 Security Headers** | CSP, HSTS, X-Frame-Options implementation |
| **📊 Rate Limiting** | DDoS protection, API rate limiting |
| **🔐 Authentication** | JWT, bcrypt, session management |
| **📝 Code Explanation** | Explains code in simple Hinglish |

---

## Model Details

### Basic Information

- **Developed by:** Harsh Verma
- **Model type:** Causal Language Model (Fine-tuned CodeLlama-7B)
- **Language(s):** Hindi, English, Hinglish (Hindi + English mix)
- **License:** MIT
- **Base Model:** CodeLlama-7B
- **Fine-tuned on:** Custom multilingual cybersecurity + web development dataset

### Model Sources

- **Repository:** [Your  ]
- **Demo:** [https://huggingface.co/spaces/Harsh2026verma/Rudra-chatbot]
- **Dataset:** [Link to your dataset]

---

## Uses

### Direct Use

This model can be used for:

```python
# 1. Generate secure code in Hinglish
response = model.generate("SQL injection se bachne ka tarika batao")

# 2. Create web applications
response = model.generate("Ek responsive navbar banao with logo and 3 links")

# 3. Security audit
response = model.generate("Is code mein SQL injection vulnerability hai?")

# 4. DDoS protection
response = model.generate("Flask mein rate limiting kaise lagayein?")