---
title: README
emoji: 🦀
colorFrom: yellow
colorTo: yellow
sdk: gradio
pinned: true
license: apache-2.0
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/678a345729f4e4a1d9941a70/XYYgGAOiEct9hUui8gbwn.webp
short_description: Cybersecurity-focused AI chatbot optimized for edge devices
language:
- en
pipeline_tag: text-generation
---
![Dex Banner](https://cdn-uploads.huggingface.co/production/uploads/678a345729f4e4a1d9941a70/XYYgGAOiEct9hUui8gbwn.webp)

---
license: apache-2.0
tags:
  - text-generation
  - cybersecurity
  - chatbot
  - transformers
  - edge-device-friendly
  - dex-ai
datasets:
  - suryanshp1/kali-linux-pentesting-data
  - AlicanKiraz0/All-CVE-Records-Training-Dataset
language:
  - en
library_name: transformers
pipeline_tag: text-generation
---

# 🛡️ Dex — Cybersecurity Chatbot AI (Made by `Dex-Community`)

**Dex** (Digital Exploit eXpert) is an AI model tailored for the cybersecurity and hacking community. It acts as a friendly chatbot that helps with:
- Cybersecurity topics
- Capture The Flag (CTF) guidance
- Basic exploit reasoning
- Code & payload understanding
- Custom AI assistant logic

---

## 📊 Model Information

| Key Details       | Value                               |
|-------------------|-------------------------------------|
| Model Name        | `dexcommunity/dex`                  |
| Base Architecture | Causal Language Model               |
| Framework         | 🤗 Transformers                     |
| Optimized For     | Edge devices (Raspberry Pi Zero etc)|
| Author            | Gaurav Chouhan aka `ghosthets`      |
| License           | Apache-2.0                          |

---

## ⚙️ Usage (Python Example)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("dexcommunity/dex")
model = AutoModelForCausalLM.from_pretrained("dexcommunity/dex")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def ask_dex(prompt):
    inputs = tokenizer(f"User: {prompt}\nDex:", return_tensors="pt").to(device)
    output = model.generate(inputs.input_ids, max_length=256, pad_token_id=tokenizer.eos_token_id)
    return tokenizer.decode(output[0], skip_special_tokens=True).split("Dex:")[-1].strip()

print(ask_dex("Explain SQL Injection in simple words"))

✅ Features

✅ Lightweight & optimized for edge devices (e.g. Raspberry Pi Zero W/2W)
✅ Helpful in CTF, Bug Bounty, and Penetration Testing topics
✅ Trainable on your own dataset via LoRA or PEFT methods
✅ Fine-tuned on initial cyber security corpora

🔮 Future Plans

🧠 Add OWASP Top 10 understanding
💡 Enhance with Exploit DB & CVE logic
🤖 Hugging Face Space + GUI Gradio interface

🧠 Made With 💙 by
👨‍💻 Gaurav Chouhan

Aka ghosthets 🇮🇳
GitHub: ghosthets
LinkedIn: linkedin.com/in/ghosthets

📎 License
Licensed under Apache-2.0. Use freely with attribution.