dennny123/cybersec-qwen2.5-coder-7b
The dennny123/cybersec-qwen2.5-coder-7b is a 7.6 billion parameter Qwen2.5-Coder-7B-Instruct model, fine-tuned by dennny123 using LoRA on 70,000 cybersecurity-specific examples. This model specializes in cybersecurity tasks, including CVE vulnerability analysis, security log analysis, and penetration testing guidance. It leverages a 32768 token context length to provide detailed responses for various cybersecurity use cases. The model is optimized for expert assistance in areas like threat detection and incident response.
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Cybersecurity Fine-tuned Qwen2.5-Coder-7B
This model, dennny123/cybersec-qwen2.5-coder-7b, is a specialized version of the Qwen2.5-Coder-7B-Instruct base model, fine-tuned for cybersecurity applications. It was developed by dennny123 using Unsloth and LoRA (Rank 16, Alpha 32) on a comprehensive dataset of 70,000 cybersecurity examples, achieving a final loss of 0.7485 during a 31-minute training session on NVIDIA B200 hardware.
Key Capabilities
- CVE vulnerability analysis: Provides insights into Common Vulnerabilities and Exposures.
- Security log analysis: Assists in interpreting and understanding security logs.
- Penetration testing guidance: Offers support for ethical hacking and security assessments.
- NIST compliance knowledge: Helps with understanding and adhering to NIST cybersecurity frameworks.
- Threat detection patterns: Identifies and explains common threat vectors and patterns.
- Incident response: Aids in managing and responding to security incidents.
Good For
This model is ideal for developers and security professionals requiring an AI assistant with deep knowledge in cybersecurity. Its fine-tuning on diverse datasets like omurkuru/cve-security-data and jason-oneal/pentest-agent-dataset makes it particularly effective for tasks involving vulnerability assessment, security operations, and compliance. Users can leverage its 32K context window for detailed inquiries and complex cybersecurity scenarios.