148 lines
4.4 KiB
Markdown
148 lines
4.4 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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metrics:
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- code_eval
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- accuracy
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base_model:
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- Qwen/Qwen2.5-Coder-1.5B-Instruct
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new_version: Qwen/Qwen2.5-Coder-1.5B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- cybersecurity
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- mythos
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- qween
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- qween-security
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- blue
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- team
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- blue-team
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- cve
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- ctf
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- code
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- code-security
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- heretic
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- uncensored
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- decensored
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- abliterated
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- reproducible
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---
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# 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
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> [!TIP]
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> **This model is reproducible!**
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>
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> See the [README](reproduce/README.md) in the `reproduce` directory for more information.
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## Abliteration parameters
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| Parameter | Value |
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| :-------- | :---: |
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| **direction_index** | 19.94 |
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| **attn.o_proj.max_weight** | 1.27 |
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| **attn.o_proj.max_weight_position** | 17.25 |
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| **attn.o_proj.min_weight** | 0.88 |
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| **attn.o_proj.min_weight_distance** | 13.43 |
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| **mlp.down_proj.max_weight** | 1.02 |
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| **mlp.down_proj.max_weight_position** | 25.63 |
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| **mlp.down_proj.min_weight** | 0.56 |
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| **mlp.down_proj.min_weight_distance** | 16.10 |
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## Performance
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| Metric | This model | Original model ([expper/mythos-qwen-1.5b-final](https://huggingface.co/expper/mythos-qwen-1.5b-final)) |
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| :----- | :--------: | :---------------------------: |
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| **KL divergence** | 0.0234 | 0 *(by definition)* |
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| **Refusals** | 2/100 | 93/100 |
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-----
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---
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language:
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- en
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- code
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license: apache-2.0
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tags:
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- security
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- exploit-development
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- vulnerability-research
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- php
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- mybb
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- cve
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- python
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- qwen
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- fine-tuned
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- cybersecurity
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datasets:
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- [your-dataset-name-if-uploaded]
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metrics:
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- accuracy
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- code-eval
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pipeline_tag: text-generation
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library_name: transformers
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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---
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# Mythos Engine - Qwen 2.5 Coder 1.5B Security Fine-Tune
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## 🔥 Model Description
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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.
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The model employs **Chain-of-Thought reasoning with self-correction loops** and mathematical logic notation to produce accurate, production-ready security code.
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## 🎯 Intended Use
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- **Security Research**: Analyzing CVEs and understanding exploit mechanics
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- **Red Team Education**: Learning exploit development patterns
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- **Blue Team Defense**: Understanding attack vectors to build better detections
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- **CTF & Training**: Solving complex security challenges
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**⚠️ Important**: This model is for **educational and authorized security testing only**. Do not use for unauthorized access or malicious purposes.
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## 🧠 Training Details
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| Aspect | Details |
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| :--- | :--- |
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| **Base Model** | Qwen/Qwen2.5-Coder-1.5B-Instruct |
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| **Fine-Tuning Method** | QLoRA (4-bit quantization) with Unsloth |
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| **Dataset Size** | 1000+ examples |
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| **Epochs** | 4 |
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| **Learning Rate** | 1e-5 |
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| **Sequence Length** | 4096 |
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| **Final Training Loss** | 2.02 |
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## 📊 Dataset Composition
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The training dataset includes:
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- **40% PHP Vulnerabilities**: Type juggling, deserialization, filter chains, disable_functions bypasses
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- **25% MyBB Exploits**: Admin CP RCE, SQL injection, XSS chains
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- **20% Python Exploit Development**: C2 frameworks, scanners, injection techniques
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- **10% Blue Team Detection**: Sigma/YARA rules, log analysis
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- **5% Cryptographic Attacks**: Timing attacks, padding oracles, hash length extension
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## 🚀 How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"expper/mythos-qwen-1.5b-final",
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device_map="auto",
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torch_dtype="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("expper/mythos-qwen-1.5b-final")
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prompt = """<|im_start|>system
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You are Mythos Engine, an elite security AI. Think step-by-step with self-correction.<|im_end|>
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<|im_start|>user
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Explain CVE-2022-43772 (MyBB Admin CP Avatar RCE) and write a PoC.<|im_end|>
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<|im_start|>assistant
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.6)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |