niranjan2777/cybersec-qwen
niranjan2777/cybersec-qwen is a 1.5 billion parameter, domain-adapted large language model built on Qwen2.5-1.5B-Instruct, fine-tuned for cybersecurity question-answering tasks. Utilizing QLoRA for efficiency, it provides accurate, structured, and context-aware responses to cybersecurity queries within a 32768 token context length. This model is optimized for applications requiring specialized cybersecurity intelligence, such as security analysis and AI-powered security assistants.
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CyberSec-Qwen: Domain-Specialized LLM for Cybersecurity
CyberSec-Qwen is a 1.5 billion parameter large language model, fine-tuned from Qwen2.5-1.5B-Instruct, specifically adapted for cybersecurity question-answering. It is designed to provide accurate, structured, and context-aware responses to queries within the cybersecurity domain.
Key Capabilities & Features
- Domain-Adapted: Optimized for cybersecurity intelligence, offering specialized knowledge.
- Efficient Fine-tuning: Developed using a QLoRA pipeline with 4-bit quantization, enabling training on limited hardware.
- Standalone Inference: LoRA adapters are merged into the base model, making it ready for deployment without PEFT dependencies.
- Context Length: Supports a context length of 32768 tokens.
Ideal Use Cases
- Security Analysts: Assisting with information retrieval and understanding complex security concepts.
- Cybersecurity Education: Aiding students and learners in understanding the field.
- AI-Powered Security Assistants: Serving as a core component for automated security tools.
- SOC Automation: Supporting workflows in Security Operations Centers.
Limitations
It is important to note that CyberSec-Qwen is primarily limited to the cybersecurity QA domain and may hallucinate outside of it. It was trained on a relatively small dataset and is not suitable for real-time threat detection or critical security operations without human validation. Ethical usage and proper safeguards are crucial for deployment.