| --- |
| 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 |
| --- |
|  |
|
|
| --- |
| 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. |
