QCRI/AZERG-T1-Mistral
QCRI/AZERG-T1-Mistral is a 7 billion parameter language model, fine-tuned from Mistral-7B-Instruct-v0.3, specifically designed for STIX Entity Detection. Developed by QCRI, this model excels at identifying CTI-related entities such as malware names, threat actors, and vulnerabilities within unstructured security text. It was trained on the QCRI/AZERG-Dataset to provide high performance in specialized cybersecurity intelligence tasks, making it a specialist model for the AZERG framework.
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Model Overview
QCRI/AZERG-T1-Mistral is a specialized 7 billion parameter language model, fine-tuned from mistralai/Mistral-7B-Instruct-v0.3. Its primary purpose is STIX Entity Detection within cybersecurity threat intelligence.
Key Capabilities
- Specialized Entity Extraction: Designed to identify specific CTI-related entities, including:
- Malware names
- Threat actors
- Vulnerabilities
- Targeted Training: Fine-tuned on the QCRI/AZERG-Dataset to optimize performance for this specific task.
- Integration with AZERG Framework: Developed as a specialist component for high performance within the AZERG framework.
Intended Use Cases
This model is ideal for developers and analysts who need to:
- Automate STIX entity extraction from security text passages.
- Enhance threat intelligence analysis by programmatically identifying key CTI elements.
- Integrate specialized entity detection into cybersecurity platforms or workflows.
Citation
If you utilize this model in your research or applications, please cite the associated paper:
@article{lekssays2025azerg,
title={From Text to Actionable Intelligence: Automating STIX Entity and Relationship Extraction},
author={Lekssays, Ahmed and Sencar, Husrev Taha and Yu, Ting},
journal={arXiv preprint arXiv:2507.16576},
year={2025}
}