jmmrcp/Llama-3.1-8B-Instruct-Phishing-Classification
The jmmrcp/Llama-3.1-8B-Instruct-Phishing-Classification model is an 8 billion parameter instruction-tuned language model, likely based on the Llama 3.1 architecture, specifically fine-tuned for phishing classification tasks. With a context length of 32768 tokens, this model is designed to identify and categorize phishing attempts, making it suitable for cybersecurity applications requiring robust threat detection.
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Overview
This model, jmmrcp/Llama-3.1-8B-Instruct-Phishing-Classification, is an 8 billion parameter instruction-tuned language model. It is specifically designed and fine-tuned for the critical task of phishing classification, leveraging a substantial context length of 32768 tokens. While specific training details and performance metrics are not provided in the current model card, its naming convention suggests an optimization for identifying and categorizing phishing attempts.
Key Capabilities
- Phishing Classification: Primary capability is to detect and classify phishing-related content.
- Large Context Window: Benefits from a 32768-token context length, allowing for analysis of longer inputs relevant to phishing detection.
Good for
- Cybersecurity Applications: Ideal for integrating into systems that require automated detection of phishing emails, messages, or web content.
- Threat Intelligence: Can be used to enhance threat intelligence platforms by automatically categorizing potential phishing campaigns.
- Content Moderation: Applicable in scenarios where identifying malicious or deceptive content is crucial for user safety.