| { |
| "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" |
| } |
|
|
