// 01 — structure: components, edges, dataflow + per-node facts (the annotated diagram).
// Trimmed mistral (real values); "…" marks omitted repetition. Tiers 02–04 add to this.
{
  "schema_version": "architecture-template-v0",
  "model_type": "mistral",
  "architecture": {
    "view": "decoder",
    "family": "causal_lm",
    "attention_variant": "GQA",
    "positional": "rope",
    "is_moe": false,
    "mixer": "attention",
    "sliding_window": 4096,
    "tie_word_embeddings": false
  },
  "config": {
    "class_name": "MistralConfig",
    "module": "transformers.models.mistral.configuration_mistral",
    "model_type": "mistral",
    "referenced_fields": {
      "num_hidden_layers": 32
    },
    "salient_fields": {
      "hidden_size": 4096,
      "intermediate_size": 14336,
      "num_attention_heads": 32,
      "num_key_value_heads": 8,
      "head_dim": 128,
      "vocab_size": 32000,
      "sliding_window": 4096
    }
  },
  "components": [
    {
      "id": "model",
      "kind": "model",
      "class_name": "MistralModel",
      "path_pattern": "model",
      "children": [
        "embed_tokens",
        "decoder_layers",
        "norm",
        "rotary_emb"
      ]
    },
    // "decoder_layers" is the repeat id
    {
      "id": "embed_tokens",
      "kind": "embedding",
      "class_name": "Embedding",
      "path_pattern": "model.embed_tokens",
      "attributes": {
        "num_embeddings": "config.vocab_size",
        "embedding_dim": "config.hidden_size"
      }
    },
    {
      "id": "norm",
      "kind": "normalization",
      "class_name": "MistralRMSNorm",
      "path_pattern": "model.norm",
      "attributes": {
        "norm_type": "rms",
        "kernel": "RMSNorm"
      }
    },
    {
      "id": "rotary_emb",
      "kind": "position",
      "class_name": "MistralRotaryEmbedding",
      "path_pattern": "model.rotary_emb",
      "attributes": {
        "scheme": "rope",
        "rope_theta": 10000.0,
        "head_dim": "config.head_dim"
      }
    }
  ],
  "templates": [
    {
      "id": "decoder_layer",
      "kind": "transformer_block",
      "class_name": "MistralDecoderLayer",
      "path_pattern": "model.layers.{i}",
      // {i} = layer index
      "children": [
        "decoder_layer.input_layernorm",
        "decoder_layer.self_attn",
        "decoder_layer.post_attention_layernorm",
        "decoder_layer.mlp"
      ]
    },
    {
      "id": "decoder_layer.self_attn",
      "kind": "attention",
      "class_name": "MistralAttention",
      "path_pattern": "model.layers.{i}.self_attn",
      "children": [
        "decoder_layer.self_attn.q_proj",
        "decoder_layer.self_attn.o_proj"
      ],
      // … k_proj, v_proj
      "attributes": {
        "variant": "GQA",
        "n_heads": 32,
        "n_kv_heads": 8,
        "head_dim": 128,
        "rope": true,
        "sliding_window": 4096,
        "pattern": "sliding"
      }
    },
    {
      "id": "decoder_layer.self_attn.q_proj",
      "kind": "projection",
      "class_name": "Linear",
      "path_pattern": "model.layers.{i}.self_attn.q_proj",
      "attributes": {
        "in_features": "config.hidden_size",
        "out_features": 4096,
        "tp": "colwise"
      }
    },
    {
      "id": "decoder_layer.mlp",
      "kind": "feed_forward",
      "class_name": "MistralMLP",
      "path_pattern": "model.layers.{i}.mlp",
      "children": [
        "decoder_layer.mlp.gate_proj",
        "decoder_layer.mlp.up_proj",
        "decoder_layer.mlp.down_proj"
      ],
      "attributes": {
        "hidden_size": 4096,
        "intermediate_size": 14336,
        "activation": "silu"
      }
    },
    {
      "id": "decoder_layer.input_layernorm",
      "kind": "normalization",
      "class_name": "MistralRMSNorm",
      "path_pattern": "model.layers.{i}.input_layernorm",
      "attributes": {
        "norm_type": "rms",
        "kernel": "RMSNorm"
      }
    }
    // … post_attention_layernorm, and the k/v/o + gate/up/down projection nodes
  ],
  "repeats": [
    {
      "id": "decoder_layers",
      "kind": "symbolic_repeat",
      "body": "decoder_layer",
      "count_expr": "config.num_hidden_layers",
      "count": 32,
      "count_source": "config",
      "index_symbol": "i",
      "container_path_pattern": "model.layers",
      "item_path_pattern": "model.layers.{i}",
      "repeated_class_name": "MistralDecoderLayer"
    }
    // 32 identical layers → one symbolic entry
  ],
  "edges": [
    {
      "source": "embed_tokens",
      "target": "decoder_layers",
      "kind": "data"
    },
    {
      "source": "decoder_layers",
      "target": "norm",
      "kind": "data"
    },
    {
      "source": "decoder_layer.input_layernorm",
      "target": "decoder_layer.self_attn",
      "kind": "data",
      "provenance": "observed_forward"
    },
    {
      "source": "decoder_layer",
      "target": "decoder_layer.self_attn",
      "kind": "residual"
    },
    {
      "source": "rotary_emb",
      "target": "decoder_layer.self_attn",
      "kind": "position"
    },
    {
      "source": "input:attention_mask",
      "target": "decoder_layer.self_attn",
      "kind": "mask"
    },
    {
      "source": "state:kv_cache",
      "target": "decoder_layer.self_attn",
      "kind": "cache_read"
    }
    // … kinds: data | residual | mask | position | cross_attention | route | cache_read | cache_write
  ],
  "dataflow": {
    "source": "observed_forward_meta",
    "input": {
      "name": "input_ids",
      "shape": [
        "B",
        "S"
      ]
    },
    // B=batch, S=sequence
    "output": {
      "shape": [
        "B",
        "S",
        "config.hidden_size"
      ]
    },
    "shapes": {
      // per-node observed shapes (symbolized); node ORDER lives in edges
      "embed_tokens": {
        "in": [
          "B",
          "S"
        ],
        "out": [
          "B",
          "S",
          "config.hidden_size"
        ]
      },
      "decoder_layer.self_attn": {
        "in": [
          "B",
          "S",
          "config.hidden_size"
        ],
        "out": [
          "B",
          "S",
          "config.hidden_size"
        ]
      }
    }
  },
  "provenance": {
    "config_class": "MistralConfig",
    "config_module": "transformers.models.mistral.configuration_mistral",
    "model_class": "MistralModel",
    "model_module": "transformers.models.mistral.modeling_mistral"
  }
}