model_info.json

{
  "model_name": "AletheionGuard Trial 012",
  "version": "1.0.0",
  "training_date": "2025-11-11",
  "architecture": "Pyramidal Q1Q2 with Gates",
  "framework": "PyTorch Lightning",
  "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
  "embedding_dim": 384,

  "files": {
    "q1_gate.pth": {
      "description": "Aleatoric Uncertainty (Q1) Gate - MLP for inherent randomness",
      "size_kb": 904,
      "parameters": "~235k"
    },
    "q2_gate.pth": {
      "description": "Epistemic Uncertainty (Q2) Gate - MLP for model confidence",
      "size_kb": 905,
      "parameters": "~235k"
    },
    "height_gate.pth": {
      "description": "Pyramidal Height Gate - MLP for combined confidence score",
      "size_kb": 230,
      "parameters": "~60k"
    },
    "base_forces.pth": {
      "description": "Base Force Embeddings - Learnable base representation",
      "size_kb": 198,
      "parameters": "~51k"
    },
    "q1q2_best.ckpt": {
      "description": "Full PyTorch Lightning checkpoint (epoch 24, val_loss=0.2944)",
      "size_mb": 6.6,
      "parameters": "~580k total"
    }
  },

  "training_info": {
    "dataset": "Synthetic dataset with epistemic labels",
    "num_samples": 1590,
    "train_split": 0.7,
    "val_split": 0.15,
    "test_split": 0.15,
    "epochs_trained": 33,
    "best_epoch": 24,
    "best_val_loss": 0.2944,
    "learning_rate": 0.001,
    "optimizer": "Adam",
    "batch_size": 32
  },

  "metrics": {
    "q1_mse": 0.0501,
    "q2_mse": 0.0499,
    "rce": 0.0415,
    "height_mse": 0.0521,
    "description": "Metrics on synthetic test set. Fine-tuning on real data (TruthfulQA + SQuAD) expected to improve by 10-15%."
  },

  "usage": {
    "python_sdk": "from aletheion_guard import EpistemicAuditor; auditor = EpistemicAuditor(model_path='AletheionAGI/aletheionguard-models')",
    "rest_api": "POST https://api.aletheion.com/v1/audit with { text: '...', api_key: 'ag_...' }",
    "byo_hf": "Deploy this Space as PRIVATE and use with AletheionGuard BYO-HF mode"
  },

  "license": "AGPL-3.0-or-later",
  "author": "Felipe Maya Muniz",
  "copyright": "2024-2025 AletheionAGI",

  "notes": [
    "These models were trained on synthetic data and are suitable for MVP/demo purposes.",
    "For production use, fine-tune on real datasets (TruthfulQA, SQuAD v2, etc.).",
    "Models are small (~2.3MB total) and optimized for fast inference.",
    "Expected inference time: <50ms per request on CPU."
  ]
}