richardyoung/mythos-qwen-1.5b-final-heretic

This is a decensored version of expper/mythos-qwen-1.5b-final, made using Heretic v1.4.0

[!TIP] This model is reproducible! See the README in the reproduce directory for more information.

Abliteration parameters

Performance

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 pipelinetag: text-generation libraryname: 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

📊 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

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))

---

*Built & maintained by [Richard Young](https://deepneuro.ai/richard) · DeepNeuro*