{
  "model": "smolvlm-500m-instruct-q8",
  "variant": "q8",
  "upstream": {
    "model": "HuggingFaceTB/SmolVLM-500M-Instruct",
    "revision": "a7da5b986cb59b408707209984f360a5f4ad7e47",
    "official": true,
    "sha256": {
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      "config.json": "daacbbca6af3c34e50466c72aed2df4553a08084f6a0551d4ae420a241cb66c6",
      "generation_config.json": "067a2a54e5f87162ecac6e0e911cc4665fc8f7f3324794ecbac0f76badb56636",
      "preprocessor_config.json": "6cb6e36d6fcb88ca1502c4a26750715dc3e7dedddc9a8f17b27d8d167d1457e7",
      "tokenizer.json": "5ece781dc8d2b2f3e2f289ca0ae50b17cfc27dd27bfe7971bb8241e0b964331a",
      "tokenizer_config.json": "36c6fd44d07d10fd8180ee6b46dcccf69fb7c06753968ff0d7e17b8bfe17b777",
      "special_tokens_map.json": "aa0ff906077086dfa9734a7f97f68c825877a48f9468807be65504495cdeef09"
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  "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"
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  "export": {
    "partitioner": "executorch.backends.webgpu.partitioner.WebGPUPartitioner, default options (static shapes)",
    "linear": "et_vk.linear_q8ta_q8csw: int8 activations, one static per-tensor affine scale per pass (torchao MinMaxObserver over the calibration data, [-128, 127]); int8 weights, per-output-channel symmetric (VulkanQuantizer weight spec, scale amax/127.5)",
    "export_workaround": "_smolvlm_q8.fix_q8ta_fake(): a fake kernel giving et_vk.linear_q8ta_q8csw its real fp32 output dtype during lowering (executorch 1.5.1's reference kernel reports int8, which breaks llama::custom_sdpa's meta check and would mis-declare tensors). Metadata only",
    "programs": {
      "smolvlm_vision_q8.pte": {
        "api": "torch.export.export(strict=False) -> to_edge_transform_and_lower(partitioner=[WebGPUPartitioner()]) -> to_executorch()",
        "module": "transformers Idefics3 vision tower (eager attention) + post_layernorm + connector; identity position ids for a full 512x512 image",
        "inputs": [
          {
            "name": "pixel_values",
            "shape": [
              1,
              3,
              512,
              512
            ],
            "dtype": "float32",
            "range": "(x/255 - 0.5)/0.5"
          }
        ],
        "outputs": [
          {
            "name": "image_embeds",
            "shape": [
              1,
              64,
              960
            ],
            "dtype": "float32"
          }
        ],
        "quantization": "all 73 linears 8-bit, two passes (second pass on the first pass's rounding error, same int8 weight), weights GPTQ (damp 0.01, block 128, float64) on the calibration inputs, same grid (the connector, with fewer calibration rows than inputs, round to nearest); patch embedding conv, layer norms, position table fp32"
      },
      "smolvlm_decoder_q8.pte": {
        "api": "LLMEdgeManager: export(strict) -> run_canonical_optimizations -> export_to_edge -> to_backend([WebGPUPartitioner()]) -> to_executorch; static shapes",
        "module": "executorch examples/models/llama Transformer (apply_embedding=False, use_hf_rope, enable_dynamic_shape for the start position) with weights from executorch.examples.models.smolvlm.convert_weights; source transforms from export_llama_lib._get_source_transforms(use_sdpa_with_kv_cache, use_kv_cache)",
        "params": {
          "dim": 960,
          "hidden_dim": 2560,
          "n_heads": 15,
          "n_kv_heads": 5,
          "head_dim": 64,
          "n_layers": 32,
          "norm_eps": 1e-05,
          "rope_theta": 100000.0,
          "use_scaled_rope": false,
          "use_hf_rope": true,
          "attention_qkv_bias": false,
          "vocab_size": 49280,
          "apply_embedding": false
        },
        "inputs": [
          {
            "name": "embeds",
            "shape": [
              1,
              4,
              960
            ],
            "dtype": "float32",
            "note": "rows of positions input_pos .. input_pos + rows - 1"
          },
          {
            "name": "input_pos",
            "shape": [
              1
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            "dtype": "int64",
            "note": "position of the first row"
          }
        ],
        "outputs": [
          {
            "name": "logits",
            "shape": [
              1,
              49280
            ],
            "dtype": "float32",
            "note": "last row only"
          }
        ],
        "kv_cache": "in the program: 32 layers x k/v [1,512,5,64] fp32; every call writes its rows' positions",
        "attention_sink": "position 0 must be <|im_start|> (id 1), as the chat template always starts: its per-layer K/V (fp32 model, int8-table embedding row) are constants the program writes over row 0 when input_pos is 0; that row is excluded from calibration",
        "host_contract": "feed positions in windows of `rows` rows ending at the newest position (window_starts in _smolvlm_q8.py / windowStarts in vlm.js); input_pos + rows <= max_context",
        "max_context_length": 512,
        "quantization": "all 225 linears incl. lm_head 8-bit, two passes (second pass on the first pass's rounding error, same int8 weight), weights GPTQ (damp 0.01, block 128, float64) on the calibration inputs, same grid; RMSNorm weights fp32"
      }
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          "image": "yolo_cat.jpg",
          "image_sha256": "88b9eb3e6ec026c5ab96aab516f4a4a2773522a474abc52e934a52d86bba21e5",
          "question": "What animal is this?",
          "rows": 85,
          "continuation": " This is a cat."
        },
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          "image": "yolo_people.jpg",
          "image_sha256": "16d73869e3267a7d4ed00de8e860833bd1657c1b252e94c0c348277adc7b6edb",
          "question": "Describe this image",
          "rows": 110,
          "continuation": " The image depicts two men standing on a field, likely during a sports event or a public gathering. The men are dressed in formal attire, suggesting that they might"
        },
        {
          "image": "yolo_street.jpg",
          "image_sha256": "c02019c4979c191eb739ddd944445ef408dad5679acab6fd520ef9d434bfbc63",
          "question": "What vehicles can you see?",
          "rows": 88,
          "continuation": " There are buses in the image."
        },
        {
          "image": "yolo_table.jpg",
          "image_sha256": "0ef0c3f7caa3ad4ec92dc8968f9ac0e81ede5b4805aae89648a4c7c4236768df",
          "question": "What objects are on the table?",
          "rows": 110,
          "continuation": " There are cups, saucers, a cup with coffee, a bowl with pastries, and a plate with a napkin on the table."
        },
        {
          "image": "sample_shapes.jpg",
          "image_sha256": "f7911a973ef5a6dc962b92349f65a34f33b15e4c078052f8166018fe8338e074",
          "question": "What shapes are in this picture?",
          "rows": 114,
          "continuation": " The image contains four distinct geometric shapes. Here is a detailed description of each shape:\n\n1. **Red Triangle**: Located on the left side of the image"
        },
        {
          "image": "chihuahuas.jpg",
          "image_sha256": "6f18287a5728ce32e3714b28a971f5c62d57e2cf0f689786611d438d5d7b7522",
          "question": "How many dogs are there?",
          "rows": 89,
          "continuation": " There are two dogs in the image."
        },
        {
          "image": "kitchen.jpg",
          "image_sha256": "dbecca629f4ebe4c6d311c07f55de921bd77d32d6c85ab32e7a8033175556580",
          "question": "Describe this image",
          "rows": 110,
          "continuation": " A kitchen with a large island in the center, which has a granite countertop and a sink with a double faucet. There are two large bowls on the island"
        },
        {
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          "image_sha256": "e10d0f49a1e3b0d01bb6186bf59ca25a2c310a8d7d8ca7781eec76313ccc4ea1",
          "question": "What is happening here?",
          "rows": 88,
          "continuation": " There are giraffes walking in the field."
        }
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      "vision": "the 8 calibration images"
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}