pyToshka/wazuh-llama-3.1-8b-assistant
The pyToshka/wazuh-llama-3.1-8b-assistant is an 8 billion parameter causal language model, fine-tuned from Meta's Llama-3.1-8B-Instruct. It specializes in advanced security reasoning and analysis, particularly for Wazuh security logs, and supports instruction-following for complex queries. Optimized with Unsloth for 2x faster inference on CUDA, this model excels at threat assessment, multi-turn conversations, and providing detailed risk assessments and recommended actions for security incidents.
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Overview
The pyToshka/wazuh-llama-3.1-8b-assistant is an 8 billion parameter causal language model, fine-tuned from the meta-llama/Llama-3.1-8B-Instruct base model. Developed by pyToshka, this model is specifically designed for advanced security log analysis, with a strong focus on Wazuh security alerts. It leverages Supervised Fine-Tuning (SFT) with LoRA adapters for efficient training and is optimized with Unsloth on CUDA for significantly faster inference.
Key Capabilities
- Advanced Security Reasoning: Excels at analyzing security logs and providing detailed insights.
- Instruction-Following: Capable of understanding and executing complex security-related queries.
- Multi-turn Conversation Support: Facilitates interactive analysis of security incidents.
- Comprehensive Threat Assessment: Provides detailed risk assessments, classifications, and recommended actions for alerts.
- Multilingual Support: Processes information in English, Russian, and Spanish.
Good for
- Automated analysis of Wazuh security alerts.
- Generating detailed threat classifications and risk assessments.
- Receiving actionable recommendations for security incident response.
- Integrating advanced security intelligence into SIEM (Security Information and Event Management) systems.
- Use cases requiring fast and efficient security log processing, thanks to Unsloth optimization.