mythos-qwen-1.5b-final-heretic/README.md

---
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
- heretic
- uncensored
- decensored
- abliterated
- reproducible
---
# This is a decensored version of [expper/mythos-qwen-1.5b-final](https://huggingface.co/expper/mythos-qwen-1.5b-final), made using [Heretic](https://heretic-project.org) v1.4.0

> [!TIP]
> **This model is reproducible!**
>
> See the [README](reproduce/README.md) in the `reproduce` directory 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](https://huggingface.co/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

```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))