{
  "architecture": "causal-encoder-decoder",
  "num_encoder_layers": 4,
  "num_decoder_layers": 4,
  "hidden_size": 384,
  "num_attention_heads": 6,
  "head_dim": 64,
  "vocab_size": 8192,
  "local_window": 64,
  "max_position_embeddings": 512,
  "position_encoding": "rope",
  "normalization": "rmsnorm",
  "activation": "swiglu",
  "input": {
    "name": "input_ids",
    "dtype": "int64",
    "semantic_shape": [
      "batch=1",
      "sequence"
    ]
  },
  "mask_semantics": "int64: 1=allowed, 0=blocked; first T attention columns are global, next T are local",
  "attention_probs_semantics": "softmax probabilities actually multiplied by concatenated values; first T keys global, next T local",
  "outputs": {
    "token_embeddings": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Learned token embedding before encoder blocks"
    },
    "encoder_0_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "encoder_0_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "encoder_0_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "encoder_0_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "encoder_0_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: the block's norm1"
    },
    "encoder_0_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: the block's norm1"
    },
    "encoder_0_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: the block's norm1"
    },
    "encoder_0_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "encoder_0_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "encoder_0_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "encoder_0_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "encoder_0_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "encoder_0_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "encoder_0_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "encoder_0_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "encoder_0_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "encoder_0_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "encoder_0_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "encoder_0_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "encoder_0_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "encoder_0_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "encoder_0_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "encoder_0_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "encoder_0_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "encoder_0_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "encoder_0_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "encoder_1_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "encoder_1_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "encoder_1_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "encoder_1_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "encoder_1_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: the block's norm1"
    },
    "encoder_1_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: the block's norm1"
    },
    "encoder_1_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: the block's norm1"
    },
    "encoder_1_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "encoder_1_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "encoder_1_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "encoder_1_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "encoder_1_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "encoder_1_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "encoder_1_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "encoder_1_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "encoder_1_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "encoder_1_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "encoder_1_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "encoder_1_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "encoder_1_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "encoder_1_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "encoder_1_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "encoder_1_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "encoder_1_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "encoder_1_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "encoder_1_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "encoder_2_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "encoder_2_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "encoder_2_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "encoder_2_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "encoder_2_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: the block's norm1"
    },
    "encoder_2_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: the block's norm1"
    },
    "encoder_2_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: the block's norm1"
    },
    "encoder_2_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "encoder_2_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "encoder_2_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "encoder_2_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "encoder_2_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "encoder_2_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "encoder_2_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "encoder_2_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "encoder_2_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "encoder_2_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "encoder_2_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "encoder_2_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "encoder_2_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "encoder_2_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "encoder_2_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "encoder_2_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "encoder_2_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "encoder_2_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "encoder_2_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "encoder_3_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "encoder_3_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "encoder_3_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "encoder_3_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "encoder_3_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: the block's norm1"
    },
    "encoder_3_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: the block's norm1"
    },
    "encoder_3_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: the block's norm1"
    },
    "encoder_3_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "encoder_3_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "encoder_3_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "encoder_3_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "encoder_3_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "encoder_3_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "encoder_3_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "encoder_3_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "encoder_3_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "encoder_3_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "encoder_3_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "encoder_3_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "encoder_3_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "encoder_3_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "encoder_3_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "encoder_3_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "encoder_3_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "encoder_3_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "encoder_3_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "encoder_pre_final_norm": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Encoder hidden state before encoder final RMSNorm"
    },
    "encoder_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Normalized encoder representation used as decoder global K/V source"
    },
    "decoder_0_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "decoder_0_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "decoder_0_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "decoder_0_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "decoder_0_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: encoder_output"
    },
    "decoder_0_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: encoder_output"
    },
    "decoder_0_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: encoder_output"
    },
    "decoder_0_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "decoder_0_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "decoder_0_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "decoder_0_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "decoder_0_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "decoder_0_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "decoder_0_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "decoder_0_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "decoder_0_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "decoder_0_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "decoder_0_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "decoder_0_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "decoder_0_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "decoder_0_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "decoder_0_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "decoder_0_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "decoder_0_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "decoder_0_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "decoder_0_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "decoder_1_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "decoder_1_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "decoder_1_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "decoder_1_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "decoder_1_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: encoder_output"
    },
    "decoder_1_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: encoder_output"
    },
    "decoder_1_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: encoder_output"
    },
    "decoder_1_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "decoder_1_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "decoder_1_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "decoder_1_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "decoder_1_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "decoder_1_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "decoder_1_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "decoder_1_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "decoder_1_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "decoder_1_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "decoder_1_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "decoder_1_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "decoder_1_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "decoder_1_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "decoder_1_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "decoder_1_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "decoder_1_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "decoder_1_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "decoder_1_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "decoder_2_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "decoder_2_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "decoder_2_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "decoder_2_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "decoder_2_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: encoder_output"
    },
    "decoder_2_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: encoder_output"
    },
    "decoder_2_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: encoder_output"
    },
    "decoder_2_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "decoder_2_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "decoder_2_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "decoder_2_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "decoder_2_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "decoder_2_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "decoder_2_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "decoder_2_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "decoder_2_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "decoder_2_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "decoder_2_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "decoder_2_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "decoder_2_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "decoder_2_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "decoder_2_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "decoder_2_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "decoder_2_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "decoder_2_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "decoder_2_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "decoder_3_input": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input before attention RMSNorm"
    },
    "decoder_3_norm1": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block input after attention RMSNorm; local K/V source"
    },
    "decoder_3_q_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query linear projection before RoPE"
    },
    "decoder_3_q": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Query after RoPE, used in attention scores"
    },
    "decoder_3_global_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key projection before RoPE; source: encoder_output"
    },
    "decoder_3_global_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global key after RoPE, used in attention scores; source: encoder_output"
    },
    "decoder_3_global_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Global value projection used in attention output; source: encoder_output"
    },
    "decoder_3_local_k_pre_rope": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key projection before RoPE"
    },
    "decoder_3_local_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local key after RoPE, used in attention scores"
    },
    "decoder_3_local_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "sequence",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        4,
        64
      ],
      "meaning": "Local value projection used in attention output"
    },
    "decoder_3_combined_k": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global keys followed by local keys on the sequence axis"
    },
    "decoder_3_combined_v": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "global+local-key",
        "head_dim"
      ],
      "example_shape": [
        1,
        6,
        8,
        64
      ],
      "meaning": "Global values followed by local values on the sequence axis"
    },
    "decoder_3_global_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed global key positions; causal"
    },
    "decoder_3_local_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "key"
      ],
      "example_shape": [
        1,
        1,
        4,
        4
      ],
      "meaning": "Allowed local key positions; causal and within local window"
    },
    "decoder_3_attention_mask": {
      "dtype": "int64",
      "semantic_shape": [
        "batch",
        "broadcast-head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        1,
        4,
        8
      ],
      "meaning": "Global mask followed by local mask; 1 allowed, 0 blocked"
    },
    "decoder_3_attn_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Scaled Q times transposed combined K, before mask"
    },
    "decoder_3_attn_masked_scores": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Attention scores with blocked positions set to negative infinity"
    },
    "decoder_3_attn_probs": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "head",
        "query",
        "global+local-key"
      ],
      "example_shape": [
        1,
        6,
        4,
        8
      ],
      "meaning": "Softmax probabilities actually multiplied by combined V"
    },
    "decoder_3_attn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Attention result after output projection, before residual add"
    },
    "decoder_3_after_attn_residual": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after attention residual add"
    },
    "decoder_3_norm2": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Hidden state after FFN RMSNorm"
    },
    "decoder_3_ffn_gate": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU gate linear projection"
    },
    "decoder_3_ffn_up": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU up linear projection"
    },
    "decoder_3_ffn_activation": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "ffn_dim"
      ],
      "example_shape": [
        1,
        4,
        1024
      ],
      "meaning": "SwiGLU product SiLU(gate) times up"
    },
    "decoder_3_ffn_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "FFN down projection before residual add"
    },
    "decoder_3_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Block output after FFN residual add"
    },
    "decoder_output": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Last decoder block output before final RMSNorm"
    },
    "final_hidden": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "feature"
      ],
      "example_shape": [
        1,
        4,
        384
      ],
      "meaning": "Decoder hidden state after final RMSNorm"
    },
    "logits": {
      "dtype": "float32",
      "semantic_shape": [
        "batch",
        "sequence",
        "vocab"
      ],
      "example_shape": [
        1,
        4,
        8192
      ],
      "meaning": "Full-sequence vocabulary logits from tied LM head"
    }
  }
}