{
  "schema_version": 1,
  "model": {
    "release": "chessqueries-vitl-644-safetensors-fp16-r1",
    "format": "chessqueries-safetensors-v1",
    "artifact_sha256": "45edb69bf156f94299256fc51d1c75805f1de745ecfe34dc0615ef21265ab506"
  },
  "variant": "vit-l14",
  "precision": "fp16",
  "artifact": {
    "filename": "chessqueries-vitl-644-safetensors-fp16-r1.safetensors",
    "artifact_bytes": 743353530,
    "url": "https://huggingface.co/joelseytre/chessqueries/resolve/529059bd7f00c3b7d39ecdf6cc2fef5d039fc3cf/chessqueries-vitl-644-safetensors-fp16-r1.safetensors",
    "distribution_revision": "529059bd7f00c3b7d39ecdf6cc2fef5d039fc3cf"
  },
  "preprocessing_version": "rgb-imagenet-f32-bilinear-ignore-exif-v1",
  "input": {
    "name": "image",
    "representation": "tensor",
    "dtype": "float16",
    "shape": [
      1,
      3,
      644,
      644
    ],
    "layout": "NCHW",
    "color_order": "RGB",
    "resize": {
      "size_wh": [
        644,
        644
      ],
      "interpolation": "bilinear",
      "antialias": true,
      "arithmetic": "float32",
      "preserve_aspect_ratio": false,
      "crop": false,
      "padding": false
    },
    "reference_exif_orientation": "ignored",
    "normalization": {
      "formula": "(pixel / 255.0 - mean[channel]) / std[channel]",
      "mean": [
        0.485,
        0.456,
        0.406
      ],
      "std": [
        0.229,
        0.224,
        0.225
      ]
    },
    "normalization_location": "client"
  },
  "output": {
    "name": "logits",
    "dtype": "float16",
    "shape": [
      1,
      64,
      13
    ],
    "class_symbols": [
      ".",
      "P",
      "N",
      "B",
      "R",
      "Q",
      "K",
      "p",
      "n",
      "b",
      "r",
      "q",
      "k"
    ],
    "square_order": "a8 through h8, then a7 through h7, continuing to a1 through h1",
    "class_selection": "argmax over the last dimension; first index wins ties",
    "fen_output": "piece placement only"
  },
  "provenance": {
    "source_checkpoint_sha256": "6151bdd98fbe25f32080c097eba7ae75808b5615160e4867240931085fc762e5",
    "source_revision": "aac50e3fbf791da58784feafc3766df77003238f",
    "export_versions": {
      "torch": "2.8.0",
      "timm": "1.0.27",
      "safetensors": "0.8.0"
    },
    "evidence": "FP32 source converted to FP16 floating weights; integer buffers preserved. Float32 RGB decode, bilinear antialiased resize and ImageNet normalization; ignored EXIF; cast model input to FP16; FP16 logits; autocast disabled; dynamic positional interpolation retained. Paired batch-one CUDA evaluation on RTX 4090, PyTorch 2.8.0+cu128 and timm 1.0.27 with TF32 disabled: all 204544 square labels match FP32 across 3196 complete test boards (ChessReD 2129, ChessCog 342, SLCC 373, CVChess 352). Exact-board accuracies unchanged: 99.624%, 98.538%, 87.668%, 87.216%. All logits finite; maximum absolute FP32 drift 0.220565. Isolated wheel d5e5a595ecc280c225dce40bebfde3ddd6b07b12 real-photo and synthetic CUDA checks passed. Warm example-photo median 15.2 ms model-only and 49.5 ms photo-to-validated-prediction (10 warmups, 50 samples); these exclude model loading and network/queue time. CPU/MPS and mobile runtime support are not claimed. FP16 reference: reference-predictions-fp16.json."
  },
  "runtime_requirements": [
    {
      "runtime": "pytorch-cuda",
      "minimum_version": "2.8.0",
      "platform": "linux-cuda",
      "minimum_platform_version": null
    }
  ],
  "license": "PolyForm-Noncommercial-1.0.0"
}