license, language, library_name, tags, pipeline_tag, metrics, model-index, base_model

license	language	library_name	tags	pipeline_tag	metrics	model-index	base_model
apache-2.0	en	transformers	cybersecurity soc-automation siem suricata security-operations threat-detection alert-triage lora qwen2.5	text-generation	accuracy	name results socpilot-0.5b task metrics type name text-generation Security Alert Triage type value name accuracy 99.99 Priority Classification	Qwen/Qwen2.5-0.5B-Instruct

SOCPilot-0.5B: Your AI Copilot for Security Operations

Open-source SIEM-specialized AI model | Production ready

Overview

SOCPilot is the open-source AI model specialized for SIEM alert triage. Built on Qwen2.5-0.5B and fine-tuned on 100,000 real security alerts, it automates Security Operations Center workflows with production-grade accuracy.

Use Cases

Current (v1.0)

• Automated Suricata IDS/IPS alert triage
• Reduce alert fatigue by 70-80%
• Speed up incident response
• Identify critical threats vs noise
• SOC analyst training and validation

Basic Usage

The model can be loaded using Hugging Face Transformers:

AutoModelForCausalLM.from_pretrained("radherackbank/socpilot-0.5b")
AutoTokenizer.from_pretrained("radherackbank/socpilot-0.5b")

Technical Details

Model Architecture

• Base Model: Qwen2.5-0.5B-Instruct (494M parameters)
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
  • Rank: 16
  • Alpha: 32
  • Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
  • Trainable parameters: 8.8M (1.78% of total)
• Quantization: 4-bit NF4
• Precision: BFloat16 mixed precision

Training Details

• Dataset: 100,000 real Suricata eve.json alerts
• Split: 90,000 training, 10,000 validation
• Framework: HuggingFace Transformers + PEFT
• Optimizer: PagedAdamW (8-bit)

Limitations

• Trained specifically on Suricata eve.json format
• May not generalize to other SIEM formats
• Small model size (0.5B parameters) - larger versions planned
• Requires GPU for optimal performance
• May hallucinate on very rare or novel attack patterns
• Accuracy is reported on an internal validation dataset and should not be interpreted as a guarantee of performance in all environments.

License

Apache License 2.0 - Free for commercial and research use

This license includes an explicit grant of patent rights from contributors.

Acknowledgments

• Built on Qwen2.5-0.5B-Instruct (Apache 2.0)
• Fine-tuned on 100K real security alerts

Contributing

Interested in contributing? We welcome:

• Additional SIEM format support
• Evaluation datasets and benchmarks
• Bug reports and fixes
• Documentation improvements
• Integration examples

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Intended Use

This model is intended to assist SOC analysts with alert triage and prioritization. It is designed as a decision-support tool.

Not Intended Use

This model is not intended to replace human analysts or perform autonomous incident response.

Making SOC operations intelligent, one alert at a time