Create README.md

README.md

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+---
+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.