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

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))

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

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