---
license: mit
base_model:
- Qwen/QwQ-32B
pipeline_tag: text-generation
library_name: transformers
tags:
- cybersecurity
- ethicalhacking
- informationsecurity
- pentest
- code
- applicationsecurity
---

<img src="https://huggingface.co/AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q4_K_M-GGUF/resolve/main/QwQ-32B_x_Seneca_v1.4.png" width="1000" />

Finetuned by Alican Kiraz

[![Linkedin](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://tr.linkedin.com/in/alican-kiraz) 
![X (formerly Twitter) URL](https://img.shields.io/twitter/url?url=https%3A%2F%2Fx.com%2FAlicanKiraz0) 
![YouTube Channel Subscribers](https://img.shields.io/youtube/channel/subscribers/UCEAiUT9FMFemDtcKo9G9nUQ) 

Links:
- Medium: https://alican-kiraz1.medium.com/
- Linkedin: https://tr.linkedin.com/in/alican-kiraz
- X: https://x.com/AlicanKiraz0
- YouTube: https://youtube.com/@alicankiraz0

With the release of the new Qwen QwQ-32B, I quickly began training SenecaLLM v1.4 based on this model. During training:
* About 30 hours on BF16 with 4×H200


**It does not pursue any profit.**

With the new dataset I’ve prepared, it can produce quite good outputs in the following areas:
* Information Security v1.5
* Incident Response v1.3.1
* Threat Hunting v1.3.2
* Ethical Exploit Development v2.0
* Purple Team Tactics v1.3
* Reverse Engineering v2.0

"Those who shed light on others do not remain in darkness..."