Upload config.json with huggingface_hub
Browse files- config.json +76 -0
config.json
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{
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"architectures": [
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"AuroraTrinnityModel"
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],
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"model_type": "aurora_trinity",
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"framework": "aurora",
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"license": "apache-2.0",
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"version": "1.0.0",
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"description": "Aurora Trinity-3: Fractal, Ethical, Free Electronic Intelligence",
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"author": "Aurora Alliance",
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"url": "https://github.com/Aurora-Program/Trinity-3",
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"tags": [
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"fractal-intelligence",
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"ternary-logic",
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"knowledge-base",
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"ethical-ai",
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"symbolic-reasoning"
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],
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"task": [
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"text-classification",
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"reasoning",
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"knowledge-management"
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],
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"language": ["en", "es"],
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"library_name": "aurora-trinity",
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"pipeline_tag": "text-classification",
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"inference": {
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"input_format": "ternary_vectors",
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"output_format": "processed_tensors",
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"complexity": "O(1)",
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"memory_efficient": true
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},
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"architecture_details": {
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"trigate_operations": {
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"inference": "A + B + M -> R",
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"learning": "A + B + R -> M",
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"deduction": "M + R + (A|B) -> (B|A)"
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},
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"tensor_hierarchy": {
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"nivel_1": "summary representation",
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"nivel_9": "mid-level groups",
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"nivel_3": "finest detail level"
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},
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"knowledge_base": {
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"type": "multi_universe",
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"storage": "fractal_tensors",
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"retrieval": "O(1)"
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},
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"harmonization": {
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"type": "microshift",
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"coherence_validation": true,
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"ethical_constraints": true
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}
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},
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"computational_features": {
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"ternary_logic": true,
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"null_handling": true,
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"ethical_constraints": true,
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"fractal_scaling": true,
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"pure_python": true
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},
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"performance": {
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"trigate_inference": "~1μs",
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"fractal_synthesis": "~10μs",
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"knowledge_retrieval": "~5μs"
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},
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"dependencies": [],
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"python_version": ">=3.8",
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"use_cases": [
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"symbolic_reasoning",
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"knowledge_management",
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"ethical_ai_systems",
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"pattern_recognition",
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"educational_tools"
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]
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}
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