{
  "model": "gemma3_1b_it_q4gsw",
  "upstream": {
    "model": "google/gemma-3-1b-it",
    "revision": "dcc83ea841ab6100d6b47a070329e1ba4cf78752",
    "official": true,
    "license": "Gemma Terms of Use + Gemma Prohibited Use Policy (see legal/)",
    "files": {
      "model.safetensors": {
        "lfs": "3d4ef8d71c14db7e448a09ebe891cfb6bf32c57a9b44499ae0d1c098e48516b6",
        "sha256": "3d4ef8d71c14db7e448a09ebe891cfb6bf32c57a9b44499ae0d1c098e48516b6"
      },
      "tokenizer.json": {
        "lfs": "4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795",
        "sha256": "4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795"
      },
      "tokenizer.model": {
        "lfs": "1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c",
        "sha256": "1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c"
      },
      "config.json": {
        "blob": "06ab0678bc32d4f1474f31b35914512b1f9edaf7",
        "git_blob_sha1": "06ab0678bc32d4f1474f31b35914512b1f9edaf7"
      },
      "generation_config.json": {
        "blob": "37a4c871d263a349f50e4a313db3e72164950702",
        "git_blob_sha1": "37a4c871d263a349f50e4a313db3e72164950702"
      },
      "tokenizer_config.json": {
        "blob": "7bdd14f0eaec30c8d2c56bc9d543587676e19c0f",
        "git_blob_sha1": "7bdd14f0eaec30c8d2c56bc9d543587676e19c0f"
      },
      "special_tokens_map.json": {
        "blob": "1a6193244714d3d78be48666cb02cdbfac62ad86",
        "git_blob_sha1": "1a6193244714d3d78be48666cb02cdbfac62ad86"
      },
      "added_tokens.json": {
        "blob": "e17bde03d42feda32d1abfca6d3b598b9a020df7",
        "git_blob_sha1": "e17bde03d42feda32d1abfca6d3b598b9a020df7"
      },
      "README.md": {
        "blob": "fb18818cebf048987fca3dd2978bc92621695c75",
        "git_blob_sha1": "fb18818cebf048987fca3dd2978bc92621695c75"
      }
    },
    "hub_revision_checked": "dcc83ea841ab6100d6b47a070329e1ba4cf78752",
    "downloaded_from": {
      "repo": "google/gemma-3-1b-it",
      "revision": "dcc83ea841ab6100d6b47a070329e1ba4cf78752",
      "note": "official repo; every file verified against the hashes above"
    },
    "checkpoint_parameters": 999885952
  },
  "versions": {
    "python": "3.12.15+meta",
    "torch": "2.14.1+cpu",
    "torchao": "0.18.0+cpu",
    "executorch": "1.5.1+cpu",
    "executorch_git": "3b60683923245cf472b7323426920e15623ba361",
    "transformers": "5.19.0",
    "huggingface_hub": "2.2.0"
  },
  "export": {
    "api": "recipes/_gemma3.py Gemma3Text -> export_llm's quantization transforms -> LLMEdgeManager.export() -> run_canonical_optimizations() -> export_to_edge() -> to_backend([WebGPUPartitioner]) -> to_executorch()",
    "why_not_export_llm": "executorch 1.5.1 export_llm has no Gemma 3 model class; its llama_transformer lacks post-attention/post-FFN norms, embedding scaling, GELU MLP, per-layer RoPE base and sliding-window layers",
    "partitioner": "executorch.backends.webgpu.partitioner.WebGPUPartitioner(compile_options={'require_dynamic_shapes': True, 'force_fp16': False}) (the options export_llm passes to VulkanPartitioner; delegate id VulkanBackend)",
    "config": {
      "architectures": [
        "Gemma3ForCausalLM"
      ],
      "attention_bias": false,
      "attention_dropout": 0.0,
      "attn_logit_softcapping": null,
      "bos_token_id": 2,
      "cache_implementation": "hybrid",
      "eos_token_id": [
        1,
        106
      ],
      "final_logit_softcapping": null,
      "head_dim": 256,
      "hidden_activation": "gelu_pytorch_tanh",
      "hidden_size": 1152,
      "initializer_range": 0.02,
      "intermediate_size": 6912,
      "max_position_embeddings": 32768,
      "model_type": "gemma3_text",
      "num_attention_heads": 4,
      "num_hidden_layers": 26,
      "num_key_value_heads": 1,
      "pad_token_id": 0,
      "query_pre_attn_scalar": 256,
      "rms_norm_eps": 1e-06,
      "rope_local_base_freq": 10000,
      "rope_scaling": null,
      "rope_theta": 1000000,
      "sliding_window": 512,
      "sliding_window_pattern": 6,
      "torch_dtype": "bfloat16",
      "transformers_version": "4.50.0.dev0",
      "use_cache": true,
      "vocab_size": 262144
    },
    "layer_types": [
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "full_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "full_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "full_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "sliding_attention",
      "full_attention",
      "sliding_attention",
      "sliding_attention"
    ],
    "attention": {
      "global_layers": "llama.sdpa_with_kv_cache, causal over [0, pos + S), cache [1, max_context, 1, 256]",
      "sliding_layers": "exact window 512: cache [1, max_context + 1022, 1, 256] (position x at row x + 1022) written by llama.sdpa_with_kv_cache(k, k, v, cache, pos + 1022) with the output unused; attention over the 1023 rows ending at the chunk's last token with bmm + additive mask + softmax + bmm",
      "scale": "query_pre_attn_scalar ** -0.5 = 1/16",
      "softcapping": "none (config: attn_logit_softcapping = final_logit_softcapping = null)"
    },
    "lowering_fix": {
      "what": "_gemma3.live_kv_cache_reads(): after the Vulkan backend's insert_prepack_nodes, the et_vk.prepack copies of the sliding-window KV caches are removed so their slice_copy readers bind the same GPU buffer sdpa_with_kv_cache writes (without it the window reads a load-time copy of the zero-initialized cache)",
      "rewired_buffers": [
        "layers.0.k_cache",
        "layers.0.v_cache",
        "layers.1.k_cache",
        "layers.1.v_cache",
        "layers.10.k_cache",
        "layers.10.v_cache",
        "layers.12.k_cache",
        "layers.12.v_cache",
        "layers.13.k_cache",
        "layers.13.v_cache",
        "layers.14.k_cache",
        "layers.14.v_cache",
        "layers.15.k_cache",
        "layers.15.v_cache",
        "layers.16.k_cache",
        "layers.16.v_cache",
        "layers.18.k_cache",
        "layers.18.v_cache",
        "layers.19.k_cache",
        "layers.19.v_cache",
        "layers.2.k_cache",
        "layers.2.v_cache",
        "layers.20.k_cache",
        "layers.20.v_cache",
        "layers.21.k_cache",
        "layers.21.v_cache",
        "layers.22.k_cache",
        "layers.22.v_cache",
        "layers.24.k_cache",
        "layers.24.v_cache",
        "layers.25.k_cache",
        "layers.25.v_cache",
        "layers.3.k_cache",
        "layers.3.v_cache",
        "layers.4.k_cache",
        "layers.4.v_cache",
        "layers.6.k_cache",
        "layers.6.v_cache",
        "layers.7.k_cache",
        "layers.7.v_cache",
        "layers.8.k_cache",
        "layers.8.v_cache",
        "layers.9.k_cache",
        "layers.9.v_cache"
      ]
    },
    "embedding": "token embedding * float32(sqrt(1152)); output head tied to the embedding (quantized identically, stored once)",
    "rope": "per layer type, tables from transformers' Gemma3RotaryEmbedding: global base 1e6, sliding base 1e4, no scaling",
    "norm": "RMSNorm with weight (1 + w) precomputed in fp32 (transformers computes the same sum at runtime)",
    "mlp": "down(gelu_tanh(gate(x)) * up(x))",
    "inputs": [
      {
        "name": "tokens",
        "shape": [
          1,
          "1..512"
        ],
        "dtype": "int64"
      },
      {
        "name": "input_pos",
        "shape": [
          1
        ],
        "dtype": "int64"
      }
    ],
    "outputs": [
      {
        "name": "logits",
        "shape": [
          1,
          262144
        ],
        "dtype": "float32",
        "note": "last position only"
      }
    ],
    "prefill_max": 512,
    "max_seq_len_metadata": 513,
    "max_context_length": 8960,
    "dtype": "float32 activations",
    "quantization": "export_llm transforms: embedding '4,64' then qmode=4w group 64 (torchao IntxWeightOnlyConfig int4, HQQ scale-only), on the checkpoint's bf16 weights, then cast to fp32; output head quantized too",
    "kv_cache": "fp32 buffers in the program"
  },
  "files": {
    "gemma3_1b_it_q4gsw.pte": {
      "bytes": 640305924,
      "sha256": "6056492708430182103f2b28cac2cc5d4d43a66a95236f070de3312542f11c25"
    },
    "tokenizer/tokenizer.json": {
      "sha256": "4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795"
    },
    "tokenizer/tokenizer_config.json": {
      "sha256": "bfe25c2735e395407beb78456ea9a6984a1f00d8c16fa04a8b75f2a614cf53e1"
    },
    "tokenizer/special_tokens_map.json": {
      "sha256": "2f7b0adf4fb469770bb1490e3e35df87b1dc578246c5e7e6fc76ecf33213a397"
    },
    "tokenizer/config.json": {
      "sha256": "19cb5d28c97778271ba2b3c3df47bf76bdd6706724777a2318b3522230afe91e"
    },
    "tokenizer/generation_config.json": {
      "sha256": "fd9324becc53c4be610db39e13a613006f09fd6ef71a95fb6320dc33157490a3"
    }
  },
  "graph": {
    "non_delegated_kernels": {},
    "delegates": [
      {
        "id": "VulkanBackend",
        "ops": {
          "et_vk.prepack.default": 29,
          "sym_size.int": 1,
          "et_vk.select_as_symint.default": 1,
          "add": 3,
          "et_vk.embedding_q4gsw.default": 1,
          "aten.mul.Tensor": 49,
          "sub": 2,
          "aten.slice_copy.Tensor": 46,
          "aten.add.Tensor": 75,
          "aten.view_copy.default": 275,
          "et_vk.rms_norm.default": 157,
          "et_vk.linear_q4gsw.default": 183,
          "et_vk.apply_rotary_emb_hf.default": 26,
          "sdpa_with_kv_cache.default": 26,
          "mul": 1,
          "aten.squeeze_copy.dims": 44,
          "aten.permute_copy.default": 22,
          "aten.bmm.default": 44,
          "aten.clone.default": 1,
          "aten._softmax.default": 22,
          "aten.gelu.default": 26,
          "aten.select_copy.int": 1
        },
        "inputs": 2,
        "outputs": 1
      }
    ]
  },
  "command": "python recipes/gemma-3-1b-it.py --out out/gemma-3-1b-it"
}