| --- |
| license: apache-2.0 |
| language: |
| - en |
| metrics: |
| - code_eval |
| - accuracy |
| base_model: |
| - Qwen/Qwen2.5-Coder-1.5B-Instruct |
| new_version: Qwen/Qwen2.5-Coder-1.5B-Instruct |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - cybersecurity |
| - mythos |
| - qween |
| - qween-security |
| - blue |
| - team |
| - blue-team |
| - cve |
| - ctf |
| - code |
| - code-security |
| --- |
| |
| --- |
| language: |
| - en |
| - code |
| license: apache-2.0 |
| tags: |
| - security |
| - exploit-development |
| - vulnerability-research |
| - php |
| - mybb |
| - cve |
| - python |
| - qwen |
| - fine-tuned |
| - cybersecurity |
| datasets: |
| - [your-dataset-name-if-uploaded] |
| metrics: |
| - accuracy |
| - code-eval |
| pipeline_tag: text-generation |
| library_name: transformers |
| base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct |
| --- |
| |
| # Mythos Engine - Qwen 2.5 Coder 1.5B Security Fine-Tune |
| |
| ## 🔥 Model Description |
| |
| Mythos Engine is a specialized fine-tune of **Qwen 2.5 Coder 1.5B Instruct** designed for **cybersecurity research, vulnerability analysis, and exploit development**. It has been trained on a curated dataset of 700+ high-reasoning security examples covering PHP internals, MyBB exploitation, deserialization chains, type juggling, and advanced Python exploit synthesis. |
| |
| The model employs **Chain-of-Thought reasoning with self-correction loops** and mathematical logic notation to produce accurate, production-ready security code. |
| |
| ## 🎯 Intended Use |
| |
| - **Security Research**: Analyzing CVEs and understanding exploit mechanics |
| - **Red Team Education**: Learning exploit development patterns |
| - **Blue Team Defense**: Understanding attack vectors to build better detections |
| - **CTF & Training**: Solving complex security challenges |
| |
| **⚠️ Important**: This model is for **educational and authorized security testing only**. Do not use for unauthorized access or malicious purposes. |
| |
| ## 🧠 Training Details |
| |
| | Aspect | Details | |
| | :--- | :--- | |
| | **Base Model** | Qwen/Qwen2.5-Coder-1.5B-Instruct | |
| | **Fine-Tuning Method** | QLoRA (4-bit quantization) with Unsloth | |
| | **Dataset Size** | 1000+ examples | |
| | **Epochs** | 4 | |
| | **Learning Rate** | 1e-5 | |
| | **Sequence Length** | 4096 | |
| | **Final Training Loss** | 2.02 | |
| |
| ## 📊 Dataset Composition |
| |
| The training dataset includes: |
| |
| - **40% PHP Vulnerabilities**: Type juggling, deserialization, filter chains, disable_functions bypasses |
| - **25% MyBB Exploits**: Admin CP RCE, SQL injection, XSS chains |
| - **20% Python Exploit Development**: C2 frameworks, scanners, injection techniques |
| - **10% Blue Team Detection**: Sigma/YARA rules, log analysis |
| - **5% Cryptographic Attacks**: Timing attacks, padding oracles, hash length extension |
| |
| ## 🚀 How to Use |
| |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "expper/mythos-qwen-1.5b-final", |
| device_map="auto", |
| torch_dtype="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("expper/mythos-qwen-1.5b-final") |
| |
| prompt = """<|im_start|>system |
| You are Mythos Engine, an elite security AI. Think step-by-step with self-correction.<|im_end|> |
| <|im_start|>user |
| Explain CVE-2022-43772 (MyBB Admin CP Avatar RCE) and write a PoC.<|im_end|> |
| <|im_start|>assistant |
| """ |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.6) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
