sainikhiljuluri2015/NVIDIA-Orchestrator-Cybersecurity-8B-Merged
The sainikhiljuluri2015/NVIDIA-Orchestrator-Cybersecurity-8B-Merged model is an 8 billion parameter language model fine-tuned from NVIDIA's Orchestrator-8B, specifically optimized for cybersecurity tasks. It was trained on approximately 50,000 cybersecurity instruction-response pairs from datasets like Trendyol Cybersecurity, Fenrir v2.0, and Primus-Instruct. This merged model excels in areas such as threat analysis, incident response, malware analysis, and vulnerability assessment, making it suitable for specialized cybersecurity applications.
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NVIDIA-Orchestrator-Cybersecurity-8B-Merged: Specialized for Cybersecurity
This model is an 8 billion parameter variant of NVIDIA's Orchestrator-8B, specifically fine-tuned to address a wide range of cybersecurity challenges. It stands out due to its specialized training on a substantial dataset of approximately 50,000 cybersecurity instruction-response pairs, sourced from datasets including Trendyol Cybersecurity, Fenrir v2.0, and an upsampled Primus-Instruct.
Key Capabilities
- Threat Analysis and Classification: Identifies and categorizes potential security threats.
- Security Alert Triage: Assists in prioritizing and managing security alerts.
- Incident Response Guidance: Provides steps and recommendations for handling security incidents.
- Malware Analysis: Helps in understanding and dissecting malicious software.
- MITRE ATT&CK Mapping: Maps observed behaviors to the MITRE ATT&CK framework for better threat intelligence.
- Vulnerability Assessment: Evaluates systems for potential weaknesses.
- SQL Injection Detection: Identifies and helps mitigate SQL injection vulnerabilities.
- Phishing Analysis: Analyzes phishing attempts and related threats.
Training Details
The model was trained using the NVIDIA NeMo Orchestrator framework over 2 epochs. It utilized a LoRA rank of 16 and an alpha of 32, with a learning rate of 2e-4 and a maximum sequence length of 1024. This fine-tuning process has resulted in a merged model, simplifying deployment by integrating LoRA weights directly into the base model.
Should I use this for my use case?
This model is ideal for applications requiring deep understanding and generation of responses related to cybersecurity. If your use case involves tasks such as automating security operations, assisting security analysts, or developing cybersecurity tools that require specialized language understanding, this model offers a highly relevant and capable solution.