Focus paper weights on ViT-L/B/S inference and preserve legacy downloads

LICENSE-DINOv2

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NOTICE

@@ -0,0 +1,11 @@
+ChessQueries / ChessQ Lite model weights
+Required Notice: Copyright (c) 2026 Joël Seytre.
+The fine-tuned ChessQueries model weights are distributed under PolyForm Noncommercial 1.0.0.
+
+These models incorporate DINOv2 pretrained encoders developed by Meta AI.
+DINOv2: Copyright (c) Meta Platforms, Inc. and affiliates.
+https://github.com/facebookresearch/dinov2
+DINOv2 is licensed under Apache License 2.0; see LICENSE-DINOv2.
+The encoder weights were fine-tuned for chessboard recognition, combined with a
+ChessQueries decoder, and exported to the documented precision.
+The upstream license does not replace the license of the ChessQueries release.

README.md

@@ -1,7 +1,7 @@
 ---
 license: other
 license_name: polyform-noncommercial-license-1.0.0
-license_link: https://polyformproject.org/licenses/noncommercial/1.0.0
+license_link: LICENSE
 library_name: chessqueries
 tags:
   - chess
@@ -9,102 +9,144 @@ tags:
   - arxiv:2608.30762
 ---
 
-# ChessQueries model weights
+# ChessQueries paper model weights
 
-**Resources:** [Demo](https://chessq.org) ·
-[Paper [arXiv]](https://arxiv.org/abs/2608.30762) ·
-[Code [GitHub]](https://github.com/JSeytre/chessqueries) ·
-[SLCC dataset annotations [HuggingFace]](https://huggingface.co/datasets/joelseytre/slcc)
+[Paper](https://arxiv.org/abs/2608.30762) · [Code](https://github.com/JSeytre/chessqueries) ·
+[Demo](https://chessq.org) · [SLCC annotations](https://huggingface.co/datasets/joelseytre/slcc)
 
-[Try it online](https://chessq.org). The website uses a ChessQueries model further
-trained on proprietary data.
+These are selected original paper checkpoints for recognizing a chess position
+from a photograph. Each model uses a DINOv2 encoder, square-query decoder and
+644 × 644 RGB input. Outputs describe piece placement; side to move, castling
+rights and other game-history fields are not inferred.
+
+Newer **ChessQ Lite** ViT-S releases, starting with V4, are published separately
+at [joelseytre/chessq-lite](https://huggingface.co/joelseytre/chessq-lite).
+The [online demo](https://chessq.org) also offers newer models trained on
+additional private data. The weights below remain the paper models.
 
 ![ChessQueries predictions across four chessboard datasets](figure-1-hero.jpg)
 
-*ChessQueries can handle four datasets with various challenges.*
-
-Release weights for ChessQueries, a real-life chessboard recognizer using a
-DINOv2 ViT-L/14 encoder and a square-query decoder. The model takes
-644 × 644 inputs and was jointly trained on ChessReD, ChessCog, and SLCC.
-
-## Files
-
-- `chessqueries-vitl-644-safetensors-fp16-r1.safetensors` — CUDA/web deployment
-  weights (743.35 MB), FP16 weights/input/logits; SHA-256
-  `45edb69bf156f94299256fc51d1c75805f1de745ecfe34dc0615ef21265ab506`.
-- `chessqueries-vitL14-644-joint.safetensors` — FP32 source/reference, tensor-only
-  inference weights; SHA-256
-  `6151bdd98fbe25f32080c097eba7ae75808b5615160e4867240931085fc762e5`.
-- `chessqueries-vitL14-644-joint.ckpt` — original full PyTorch Lightning
-  checkpoint; SHA-256
-  `958e30d0a982873cfaef7fd3267489c8e554a8b2e75a9fb8441a511cd5a0ff18`.
-- `chessquerieslite-vits-644-fp32.onnx` — ViT-S/14 at 644 × 644,
-  FP32 ONNX (127.57 MB).
-- `chessquerieslite-vits-644-int8.onnx` — the same ViT-S/14 model,
-  INT8 ONNX (36.03 MB).
-
-- `chessquerieslite-vits-644-coreml-fp16-r2.mlpackage.zip` — dedicated iOS
-  Lite Core ML package (62.47 MB), applied EXIF orientation and float32 tensor I/O;
-  SHA-256 `e01695f8adb0de7216bb1aae3a20ac2bb2f988a6c269a430181440e5d6f3292f`.
+## Recommended downloads
 
-The FP16 [release manifest](chessqueries-vitl-644-safetensors-fp16-r1.json) pins its immutable download and preprocessing;
-its [synthetic CUDA reference](chessqueries-vitl-644-safetensors-fp16-r1-reference.json) provides a numerical integration check.
+| Paper model | FP16 safetensors file | Download size | Manifest |
+| --- | --- | ---: | --- |
+| ViT-L | `chessqueries-vitl-644-safetensors-fp16-r1.safetensors` | 743.35 MB | [JSON](chessqueries-vitl-644-safetensors-fp16-r1.json) |
+| ViT-B | `chessqueries-vitb-644-safetensors-fp16-r1.safetensors` | 248.95 MB | [JSON](chessqueries-vitb-644-safetensors-fp16-r1.json) |
+| ViT-S | `chessquerieslite-vits-644-safetensors-fp16-r1.safetensors` | 63.15 MB | [JSON](chessquerieslite-vits-644-safetensors-fp16-r1.json) |
 
-## FP16 validation
+Each manifest gives an immutable artifact URL, exact bytes, SHA-256, architecture,
+preprocessing and output contract. This is one selected checkpoint per
+architecture, not every paper training seed. ViT-B uses original seed 1; ViT-S
+uses original seed 2.
 
-The ViT-L FP16 artifact was reloaded and compared with the released FP32 source
-on all 3,196 test boards: ChessReD 2,129, ChessCog 342, SLCC 373 and CVChess 352.
-All 204,544 square predictions matched; exact-board accuracies were unchanged
-(99.624%, 98.538%, 87.668% and 87.216%, respectively). Both models ran at batch
-one on an RTX 4090 with PyTorch 2.8.0, timm 1.0.27 and TF32 disabled. This
-validates this artifact/runtime combination, not every backend or future export.
+## Quick start
 
-## Deployment selection
+Use Python 3.12 and this tested public package revision:
 
-CUDA/web serving selects ViT-L FP16 weights, targeting roughly 20 ms per warm
-batch-one model inference on an RTX 4090. Use an FP16-aware ChessQueries loader:
-older packages support only FP32. Preserve float32 RGB decode, bilinear
-antialiased resize and ImageNet normalization, then cast to FP16 immediately
-before inference. Safetensors serving ignores EXIF orientation. The dedicated
-iOS Core ML package applies orientation and has its own preprocessing contract.
+```sh
+python3.12 -m venv .venv
+. .venv/bin/activate
+python -m pip install "git+https://github.com/JSeytre/chessqueries.git@63839c8886ce65be1de2acf7d29d5a6f23dd210a"
+chessqueries-predict photo.jpg
+```
 
-iOS selects the dedicated Core ML export. Device-specific goals are around
-100 ms on a powerful iPhone and 400–500 ms on a less powerful iPhone; validate
-the exact export on the target phone. Android runtime selection is deferred.
-The best existing Pixel 9 Pro result is about 629–633 ms with LiteRT FP16 CPU,
-but a common-workload CPU FP16/INT8 comparison and complete device validation
-remain necessary. These timing scopes differ and are not a cross-platform benchmark.
+The first prediction downloads and verifies the 743 MB ViT-L FP16 checkpoint.
+CUDA runs in FP16. CPU uses the same download with FP32 arithmetic. Preprocessing
+remains FP32 on both paths. The default batch size is one; `--device cpu` forces
+CPU execution. CPU upcasting does not restore the original FP32 weight precision.
+
+To select ViT-B, download through its pinned manifest and run it on your photo:
+
+```python
+from pathlib import Path
+from urllib.request import urlopen, urlretrieve
+
+from chessqueries.contracts.inference import verify_artifact
+from chessqueries.contracts.models import ModelManifest
+from chessqueries.models.predictor import Predictor
+
+release = "chessqueries-vitb-644-safetensors-fp16-r1"
+revision = "5d29dfc5b289a31c80018154c797ebe4f0713321"
+url = f"https://huggingface.co/joelseytre/chessqueries/resolve/{revision}/{release}.json"
+with urlopen(url) as response:
+    manifest = ModelManifest.model_validate_json(response.read())
+artifact = Path(manifest.artifact.filename)
+urlretrieve(str(manifest.artifact.url), artifact)
+verify_artifact(artifact, manifest)
+prediction = Predictor.from_checkpoint(artifact).predict(["photo.jpg"])[0]
+print(prediction.fen)
+```
 
-New releases follow these deployment choices. FP32 source/reference checkpoints
-and existing published artifacts remain available; new FP32 companions are not
-required for every deployment release.
+For ViT-S, set `release = "chessquerieslite-vits-644-safetensors-fp16-r1"`.
+For ViT-L, set `release = "chessqueries-vitl-644-safetensors-fp16-r1"`.
+Once downloaded, all three can also use:
 
-Setup, inference, evaluation, and reproduction instructions are in the
-[code repository](https://github.com/JSeytre/chessqueries).
+```sh
+chessqueries-predict photo.jpg --checkpoint /path/to/model.safetensors
+```
+
+The portable CPU predictor is distinct from the strict manifest-bound serving
+adapter. These FP16 manifests declare the validated `pytorch-cuda` runtime;
+`ReleasePredictor` enforces that declaration and does not silently fall back.
+
+## Preprocessing and validation
+
+Decode RGB, ignore EXIF orientation for these safetensors releases, resize to
+644 × 644 using float32 bilinear antialiasing without crop/padding, and normalize
+with ImageNet mean/std. Cast to FP16 only before CUDA inference. The package
+implements this contract; native/browser image resizing needs its own parity
+checks. Logits are `[1, 64, 13]`, in a8-to-h1 order with classes
+`. P N B R Q K p n b r q k`.
+
+The published FP16 CUDA conversions were compared with their original FP32
+sources on 3,196 images across ChessReD, ChessCog, SLCC and CVChess. ViT-L matched
+all square labels. ViT-B changed seven square predictions and ViT-S changed one,
+with no decrease in exact-board accuracy in any domain. They are not numerically
+identical to their FP32 sources. Synthetic references and B/S validation summaries
+accompany the manifests. These results do not establish accuracy or performance
+for every backend, browser or phone.
+
+## Legacy and reference downloads
+
+The original `chessqueries-vitL14-644-joint.safetensors` (1.49 GB) remains at its
+existing URL for older clients and full-precision reference comparisons.
+SHA-256: `6151bdd98fbe25f32080c097eba7ae75808b5615160e4867240931085fc762e5`.
+
+Older deployment exports and the full training checkpoint remain available at
+immutable historical revisions. They are outside the recommended file list:
+
+- [Paper ViT-S ONNX FP32](https://huggingface.co/joelseytre/chessqueries/resolve/a8a5cbadbcb38d4a6864c72ef9322f6b87de4508/chessquerieslite-vits-644-fp32.onnx), 127.57 MB;
+  SHA-256 `c085f7060ce848c33389ca6e90db5b1d7e41d56fc0566f5a1e4c96cf1460f897`.
+- [Paper ViT-S ONNX INT8](https://huggingface.co/joelseytre/chessqueries/resolve/a8a5cbadbcb38d4a6864c72ef9322f6b87de4508/chessquerieslite-vits-644-int8.onnx), 36.03 MB;
+  SHA-256 `1c7b2968263b9a51b4405f4cad8228a81283fdc458031f13f0c53fa2c147c402`.
+- [Paper ViT-S Core ML FP16 r2](https://huggingface.co/joelseytre/chessqueries/resolve/4929cd255442cc5eb3f770f9ac28c93a4ebf4747/chessquerieslite-vits-644-coreml-fp16-r2.mlpackage.zip), 62.47 MB;
+  SHA-256 `e01695f8adb0de7216bb1aae3a20ac2bb2f988a6c269a430181440e5d6f3292f`.
+- [Original ViT-L Lightning training checkpoint](https://huggingface.co/joelseytre/chessqueries/resolve/5d29dfc5b289a31c80018154c797ebe4f0713321/chessqueries-vitL14-644-joint.ckpt), 4.46 GB;
+  SHA-256 `958e30d0a982873cfaef7fd3267489c8e554a8b2e75a9fb8441a511cd5a0ff18`.
+
+[Legacy manifests](legacy/) describe the ONNX and Core ML interfaces. The Core ML
+package applies EXIF orientation; it does not share the safetensors orientation
+policy. These legacy exports are paper models, not ChessQ Lite V4.
+
+## License
+
+Weights are available under [PolyForm Noncommercial 1.0.0](LICENSE). Commercial
+use requires separate permission. Keep the license and required notices with
+redistributed copies and conversions. Third-party components, including DINOv2,
+retain their upstream terms; see [third-party notices](NOTICE).
+
+For questions about licensing, reach out to 'contact "at" chessq.org'.
 
 ## Citation
 
 ```bibtex
 @misc{seytre2026chessqueries,
-  title         = {ChessQueries: Toward Better Chess Board Recognition},
-  author        = {Seytre, Jo{\"e}l},
-  year          = {2026},
-  eprint        = {2608.30762},
+  title = {ChessQueries: Toward Better Chess Board Recognition},
+  author = {Seytre, Jo{\"e}l},
+  year = {2026},
+  eprint = {2608.30762},
   archivePrefix = {arXiv},
-  primaryClass  = {cs.CV},
-  doi           = {10.48550/arXiv.2608.30762},
-  url           = {https://arxiv.org/abs/2608.30762}
+  primaryClass = {cs.CV},
+  url = {https://arxiv.org/abs/2608.30762}
 }
 ```
-
-## License
-
-The ChessQueries model weights are licensed under the
-[PolyForm Noncommercial License 1.0.0](https://polyformproject.org/licenses/noncommercial/1.0.0).
-They may be used, modified, and redistributed for permitted noncommercial
-purposes; commercial use is not licensed. Third-party components, including the
-DINOv2 encoder, remain subject to their respective upstream terms.
-
-## Contact
-
-Questions and enquiries: contact "at" chessq.org

chessqueries-vitL14-644-joint.ckpt

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legacy/chessquerieslite-vits-644-coreml-fp16-r2.json

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+{
+  "schema_version": 1,
+  "model": {
+    "release": "chessquerieslite-vits-644-coreml-fp16-r2",
+    "format": "chessqueries-coreml-v1",
+    "artifact_sha256": "e01695f8adb0de7216bb1aae3a20ac2bb2f988a6c269a430181440e5d6f3292f"
+  },
+  "variant": "vit-s14",
+  "precision": "fp16",
+  "artifact": {
+    "filename": "chessquerieslite-vits-644-coreml-fp16-r2.mlpackage.zip",
+    "artifact_bytes": 62465170,
+    "url": "https://huggingface.co/joelseytre/chessqueries/resolve/4929cd255442cc5eb3f770f9ac28c93a4ebf4747/chessquerieslite-vits-644-coreml-fp16-r2.mlpackage.zip",
+    "distribution_revision": "4929cd255442cc5eb3f770f9ac28c93a4ebf4747"
+  },
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+    "source_revision": "053327c778c5729261a5118f5168a520ff6cc813",
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+  },
+  "runtime_requirements": [
+    {
+      "runtime": "coreml",
+      "minimum_version": "system",
+      "platform": "ios",
+      "minimum_platform_version": "17.0"
+    },
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+      "minimum_version": "system",
+      "platform": "macos",
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+    }
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+}

legacy/chessquerieslite-vits-644-onnx-fp32-r1.json

@@ -0,0 +1,103 @@
+{
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+  "license": "PolyForm-Noncommercial-1.0.0"
+}

legacy/chessquerieslite-vits-644-onnx-int8-r1.json

@@ -0,0 +1,103 @@
+{
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+    "color_order": "RGB",
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+}