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Model: expper/mythos-qwen-1.5b-final
Source: Original Platform

README.md

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+---
+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))