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
reproducedirectory for more information.
Abliteration parameters
| Parameter | Value |
|---|---|
| direction_index | 19.94 |
| attn.o_proj.max_weight | 1.27 |
| attn.o_proj.max_weight_position | 17.25 |
| attn.o_proj.min_weight | 0.88 |
| attn.o_proj.min_weight_distance | 13.43 |
| mlp.down_proj.max_weight | 1.02 |
| mlp.down_proj.max_weight_position | 25.63 |
| mlp.down_proj.min_weight | 0.56 |
| mlp.down_proj.min_weight_distance | 16.10 |
Performance
| Metric | This model | Original model (expper/mythos-qwen-1.5b-final) |
|---|---|---|
| KL divergence | 0.0234 | 0 (by definition) |
| Refusals | 2/100 | 93/100 |
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
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*