model_analysis.json

{
  "checkpoint_path": "/QuasarV4/checkpoints/step_220000",
  "analysis_timestamp": "0",
  "model_architecture": "HFA (Hierarchical Flow Anchoring)",
  "parameter_mappings": {
    "checkpoint_params": [
      "token_embedding.weight",
      "blocks.0.attention.hierarchical_flow.evolution_rate",
      "blocks.0.attention.hierarchical_flow.memory_decay",
      "blocks.0.attention.hierarchical_flow.attention_memory",
      "blocks.0.attention.hierarchical_flow.q_proj.weight",
      "blocks.0.attention.hierarchical_flow.k_proj.weight",
      "blocks.0.attention.hierarchical_flow.v_proj.weight",
      "blocks.0.attention.hierarchical_flow.out_proj.weight",
      "blocks.0.attention.hierarchical_flow.attention_evolution.weight",
      "blocks.0.attention.hierarchical_flow.attention_evolution.bias",
      "blocks.0.attention.hierarchical_flow.memory_gate.weight",
      "blocks.0.attention.hierarchical_flow.memory_gate.bias",
      "blocks.0.attention.hierarchical_flow.temporal_dynamics.weight",
      "blocks.0.attention.hierarchical_flow.temporal_dynamics.bias",
      "blocks.0.attention.hierarchical_flow.checkpoint_trigger.checkpoint_frequency",
      "blocks.0.attention.hierarchical_flow.checkpoint_trigger.entropy_analyzer.weight",
      "blocks.0.attention.hierarchical_flow.checkpoint_trigger.entropy_analyzer.bias",
      "blocks.0.attention.hierarchical_flow.checkpoint_trigger.semantic_detector.0.weight",
      "blocks.0.attention.hierarchical_flow.checkpoint_trigger.semantic_detector.0.bias",
      "blocks.0.attention.hierarchical_flow.checkpoint_trigger.semantic_detector.2.weight"
    ]
  },
  "checkpoint_structure": {
    "type": "nested_model_state_dict",
    "num_parameters": 274
  },
  "loading_instructions": [
    "1. Load checkpoint with torch.load()",
    "2. Extract model_state_dict from checkpoint dictionary",
    "3. Map parameter names:",
    "   - hfa_layers.X -> blocks.X.attention.hierarchical_flow",
    "   - token_embedding -> token_embedding (direct match)",
    "   - lm_head -> lm_head (direct match)",
    "   - layer_norm -> layer_norm (direct match)",
    "4. Use strict=False for loading to handle mismatches"
  ],
  "training_metadata": {
    "step": 220000,
    "epoch": 1,
    "train_loss": 4.591190338134766,
    "val_loss": 4.591190338134766,
    "timestamp": 1757907906.1673536,
    "save_duration": 2.1279516220092773,
    "checkpoint_type": "hybrid_fast",
    "file_size_mb": 881.7255353927612
  }
}