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Model: radheneev/socpilot-0.5b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+tags:
+- cybersecurity
+- soc-automation
+- siem
+- suricata
+- security-operations
+- threat-detection
+- alert-triage
+- lora
+- qwen2.5
+pipeline_tag: text-generation
+metrics:
+- accuracy
+model-index:
+- name: socpilot-0.5b
+  results:
+  - task:
+      type: text-generation
+      name: Security Alert Triage
+    metrics:
+    - type: accuracy
+      value: 99.99
+      name: Priority Classification
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+---
+# SOCPilot-0.5B: Your AI Copilot for Security Operations
+
+**Open-source SIEM-specialized AI model** | **Production ready**
+
+[![Model](https://img.shields.io/badge/🤗-Model-yellow)](https://huggingface.co/radherackbank/socpilot-0.5b)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+[![Accuracy](https://img.shields.io/badge/accuracy-99.99%25-green)]()
+
+## 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
+
+---
+## 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**