QCRI/AZERG-T2-Mistral
QCRI/AZERG-T2-Mistral is a 7 billion parameter language model developed by QCRI, fine-tuned from Mistral-7B-Instruct-v0.3. This specialist model is optimized for STIX Entity Type Identification, classifying entities within security text passages into predefined STIX types. It was trained on the QCRI/AZERG-Dataset to achieve high performance in identifying entities like Malware, Tool, or Threat-Actor. Its primary use is to extract potential STIX entities from security-related text.
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QCRI/AZERG-T2-Mistral: Specialized STIX Entity Identification
QCRI/AZERG-T2-Mistral is a 7 billion parameter model, fine-tuned from mistralai/Mistral-7B-Instruct-v0.3, specifically designed for Task 2: STIX Entity Type Identification. Developed by QCRI, this model excels at classifying entities found in security text into one of several predefined STIX entity types.
Key Capabilities
- Specialized Classification: Identifies and categorizes entities (e.g., Malware, Tool, Threat-Actor) from security-related text passages.
- Targeted Training: Fine-tuned on the QCRI/AZERG-Dataset for high accuracy in STIX entity detection.
- Integration with AZERG Framework: Designed to work within the AZERG framework for automated threat intelligence.
Intended Use Cases
- Threat Intelligence Analysis: Assists threat intelligence analysts by automatically assigning STIX entity types to identified entities.
- Security Text Processing: Extracts potential STIX entities from various security documents and reports.
- Automated Entity Extraction: Streamlines the process of identifying and categorizing critical information in cybersecurity contexts.