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{
"architectures": [
"AuroraTrinnityModel"
],
"model_type": "aurora_trinity",
"framework": "aurora",
"license": "apache-2.0",
"version": "1.0.0",
"description": "Aurora Trinity-3: Fractal, Ethical, Free Electronic Intelligence",
"author": "Aurora Alliance",
"url": "https://github.com/Aurora-Program/Trinity-3",
"tags": [
"fractal-intelligence",
"ternary-logic",
"knowledge-base",
"ethical-ai",
"symbolic-reasoning"
],
"task": [
"text-classification",
"reasoning",
"knowledge-management"
],
"language": ["en", "es"],
"library_name": "aurora-trinity",
"pipeline_tag": "text-classification",
"inference": {
"input_format": "ternary_vectors",
"output_format": "processed_tensors",
"complexity": "O(1)",
"memory_efficient": true
},
"architecture_details": {
"trigate_operations": {
"inference": "A + B + M -> R",
"learning": "A + B + R -> M",
"deduction": "M + R + (A|B) -> (B|A)"
},
"tensor_hierarchy": {
"nivel_1": "summary representation",
"nivel_9": "mid-level groups",
"nivel_3": "finest detail level"
},
"knowledge_base": {
"type": "multi_universe",
"storage": "fractal_tensors",
"retrieval": "O(1)"
},
"harmonization": {
"type": "microshift",
"coherence_validation": true,
"ethical_constraints": true
}
},
"computational_features": {
"ternary_logic": true,
"null_handling": true,
"ethical_constraints": true,
"fractal_scaling": true,
"pure_python": true
},
"performance": {
"trigate_inference": "~1μs",
"fractal_synthesis": "~10μs",
"knowledge_retrieval": "~5μs"
},
"dependencies": [],
"python_version": ">=3.8",
"use_cases": [
"symbolic_reasoning",
"knowledge_management",
"ethical_ai_systems",
"pattern_recognition",
"educational_tools"
]
}
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