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Model: pyToshka/wazuh-llama-3.1-8b-assistant Source: Original Platform
2026-06-06 23:02:17 +08:00
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+---
+language:
+- en
+- ru
+- es
+license: llama3.1
+library_name: transformers
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- cybersecurity
+- security-analysis
+- wazuh
+- threat-detection
+- llama
+- peft
+- lora
+pipeline_tag: text-generation
+widget:
+- text: 'Analyze this security event: Multiple failed SSH login attempts from 45.142.120.10'
+  example_title: "SSH Brute Force"
+- text: 'Rule Level 12: High importance event detected'
+  example_title: "High Severity Alert"
+model-index:
+- name: wazuh-llama-3.1-8B-assistant
+  results:
+  - task:
+      type: text-generation
+      name: Security Log Analysis
+    metrics:
+    - type: loss
+      value: 0.05
+      name: Training Loss
+    - type: eval_loss
+      value: 0.15
+      name: Validation Loss
+---
+
+# wazuh-llama-3.1-8B-assistant
+
+## Model Details
+
+- **Model Name:** wazuh-llama-3.1-8B-assistant
+- **Base Model:** meta-llama/Llama-3.1-8B-Instruct
+- **License:** llama3.1
+- **Model Type:** Causal Language Model
+- **Architecture:** 8B parameters
+- **Languages:** English, Russian, Spanish (multilingual support)
+- **Training Method:** Supervised Fine-Tuning (SFT) with LoRA adapters
+
+## Model Description
+
+LLaMA 3.1 8B Instruct model fine-tuned for advanced Wazuh security log analysis with instruction-following capabilities.
+
+### Key Features
+
+- Advanced security reasoning and analysis
+- Instruction-following for complex queries
+- Multi-turn conversation support
+- Unsloth optimization on CUDA (2x faster)
+- LoRA fine-tuning for efficiency
+- Comprehensive threat assessment
+
+
+## Wazuh Severity Levels
+
+| Level Range | Severity    | Analysis Approach       |
+|-------------|-------------|-------------------------|
+| 0-5         | Low         | Informational analysis  |
+| 6-10        | Medium      | Detailed investigation  |
+| 11-15       | High/Crit   | Comprehensive response  |
+
+## Usage
+
+### Python API
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("pyToshka/wazuh-llama-3.1-8b-assistant")
+tokenizer = AutoTokenizer.from_pretrained("pyToshka/wazuh-llama-3.1-8b-assistant")
+
+prompt = """Analyze this Wazuh alert and provide:
+1. Threat Level (0-15)
+2. Classification
+3. Risk Assessment
+4. Recommended Actions
+
+Alert: Multiple failed SSH login attempts from 45.142.120.10"""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Expected Output Format
+
+```
+Rule Level: 12 - High importance event
+
+Event Type: SSH brute-force attack detected
+Detailed Reasoning: Multiple failed SSH login attempts indicate brute-force attack...
+Risk Assessment: High - Active credential compromise attempt
+Recommended Actions:
+  1. Block source IP 45.142.120.10 immediately
+  2. Review authentication logs for successful logins
+  3. Enable 2FA if not already active
+Investigation Guidance: Check for related events from same subnet...
+MITRE ATT&CK: T1110.001 - Brute Force: Password Guessing
+```
+
+## Limitations
+
+- Domain: Security/cybersecurity specific
+
+## Citation
+
+```bibtex
+@software{{wazuh_llama_3.1_8B_assistant,
+  title = {{wazuh-llama-3.1-8B-assistant}},
+  author = {{pyToshka}},
+  year = {{2025}},
+  version = {{1.0.0}},
+  url = {{https://huggingface.co/pyToshka/wazuh-llama-3.1-8b-assistant}}
+}}
+```