config.json

{
  "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"
  ]
}