README.md

---
datasets:
- suryanshp1/kali-linux-pentesting-data
- AlicanKiraz0/All-CVE-Records-Training-Dataset
language:
- en
metrics:
- bleu
- accuracy
pipeline_tag: text-generation
library_name: transformers
tags:
- DexV1
- DexAI
- Dexai
- dexai
- DEX-Modle
- IND-Dec
- AI-Dexhat
- CSAI
- CybersecurityDex
- Dexhat
- text-generation-inference
- Ghosthets
- ghosthets-dex
- Dex-ghosthets
- ghosthets-ai
- ghosthets-dex-ai
- Dex-community
- Dextron
---
# 🛡️ Dex — Your Personal Cybersecurity AI Sidekick

> *“Built for the curious, optimized for the underground.”*

**Dex** (Digital Exploit eXpert) is an intelligent cybersecurity-oriented conversational AI built for ethical hackers, CTF warriors, and knowledge-hungry learners. Whether you're crafting payloads, understanding CVEs, or just chatting — Dex makes cyber learning **fun, fast & fearless**.

---

## 🧠 Model Intelligence

| Feature   | Description                                             |
| --------- | ------------------------------------------------------- |
| 🔎 Name   | `ghosthets/Dex`                                         |
| 🧠 Type   | Causal Language Model                                   |
| 🎯 Domain | Cybersecurity, CTFs, Ethical Hacking                    |
| 🧪 Usage  | Educational, research, AI assistant for cyber learners  |
| 🧰 Tools  | Text reasoning, payload discussion, exploit explanation |

---

## 💥 Dex Is Trained To:

* 💬 Talk like a pro on cybersecurity topics
* 🔐 Explain CVEs, XSS, CSRF, SQLi, etc.
* 🧹 Assist in CTF logic & recon mindset
* 💡 Generate payload hints
* 👾 Help you build your own AI security bots
* ⚙️ Run locally with just CPU or on edge boards

---

## ⚙️ Run It in Python (Transformers)

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

tokenizer = AutoTokenizer.from_pretrained("ghosthets/Dex")
model = AutoModelForCausalLM.from_pretrained("ghosthets/Dex")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def ask_dex(prompt):
    input_text = f"User: {prompt}\nDex:"
    inputs = tokenizer(input_text, return_tensors="pt").to(device)
    outputs = model.generate(
        inputs.input_ids,
        attention_mask=inputs.attention_mask,
        max_length=256,
        do_sample=True,
        top_k=40,
        top_p=0.92,
        temperature=0.7,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True).split("Dex:")[-1].strip()

print(ask_dex("How to identify an XSS vulnerability?"))
```

---

## ⚡ Edge-Device Ready

> 💻 **Lightweight. Efficient. Ready-to-Hack.**

Dex is **engineered to run smoothly on low-power devices** like:

* 🔹 Raspberry Pi Zero W
* 🔹 Pi 2W & Pi 4
* 🔹 Tinker Boards / NanoPi / Pocket PCs
* 🔹 Light virtual containers and offline setups

Despite its small size, Dex delivers powerful outputs and will soon be trained on **more curated data**, making it **sharper, smarter, and more secure**.

---

## 👨‍💻 Perfect For

* Ethical Hackers
* Red Teamers & Pentesters
* Bug Bounty Hunters
* Cybersecurity Students
* AI + Security Researchers
* Solo Cyber Ninjas

---

## 🧠 Built With ❤️ by

**Gaurav Chouhan**
aka `ghosthets`
🔗 [GitHub: ghosthets](https://github.com/ghosthets)
📍 From India 🇮🇳