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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
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+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
+
+

NOTICE

@@ -0,0 +1,29 @@
+Human Chess MLX
+Copyright (c) 2026 Sruly Rosenblat
+
+This project includes software derived from autoresearch and autoresearch-mlx.
+The original software is provided under the following notice:
+
+MIT License
+
+Copyright (c) 2026 Andrej Karpathy
+Copyright (c) 2026 Trevin Peterson
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

README.md

@@ -0,0 +1,55 @@
+---
+license: agpl-3.0
+library_name: mlx
+pipeline_tag: text-generation
+tags:
+- chess
+- mlx
+- autoregressive
+- custom-code
+datasets:
+- Lichess/chess-games
+---
+
+# Human Chess MLX
+
+An autoregressive MLX model trained on complete human chess-game histories. Moves are atomic UCI tokens. Metadata and padding may be input context, but training and validation loss are calculated only for human move targets.
+
+The checkpoint was trained on 27,971,437 chronological January 2025 Lichess games (45.6% of one shuffled epoch). Lichess database exports are CC0.
+
+## Configuration
+
+- Context length: `256` tokens
+- Vocabulary size: `2075`
+- Transformer layers: `6`
+- Embedding width: `384`
+- Validation bits per human move: `4.098970`
+- Out-of-time test bits per human move: `4.114732`
+- Raw exact next-move accuracy: `27.68%`
+- Raw top-1 legal-move rate: `87.29%`
+- Legal-masked top-1 accuracy: `30.97%`
+- Legal-masked top-5 accuracy: `66.82%`
+
+Metrics use fixed 8,192-game chronological holdouts. The test split was evaluated only after training stopped.
+
+## Loading
+
+```python
+import mlx.core as mx
+from chess_model import ChessTokenizer, load_model
+
+model = load_model(".")
+tokenizer = ChessTokenizer.from_pretrained(".")
+tokens = mx.array([tokenizer.encode_tokens(["BOS"])])
+logits = model(tokens)
+```
+
+This is a custom MLX architecture, not a Transformers `AutoModelForCausalLM` checkpoint. The repository includes `chess_model.py` for loading.
+
+Legal-move-masked inference is available through `legal_inference.py`; it reconstructs the board from the full supplied move history before scoring only legal continuations.
+
+Source code: https://github.com/SrulyRosenblat/human-chess-mlx
+
+## License
+
+AGPL-3.0. See `LICENSE`.

chess_model.py

@@ -0,0 +1,256 @@
+"""Standalone MLX model definition shipped with checkpoints."""
+
+import math
+from dataclasses import dataclass
+
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.utils import tree_flatten
+
+
+@dataclass
+class GPTConfig:
+    sequence_len: int = 256
+    vocab_size: int = 2075
+    n_layer: int = 4
+    n_head: int = 2
+    n_kv_head: int = 2
+    n_embd: int = 256
+    window_pattern: str = "SSSL"
+
+
+class ChessTokenizer:
+    """Minimal exact-token tokenizer for metadata and atomic UCI move tokens."""
+
+    def __init__(self, vocabulary):
+        self.id_to_token = vocabulary["id_to_token"]
+        self.token_to_id = {token: index for index, token in enumerate(self.id_to_token)}
+        self.pad_token_id = self.token_to_id["PAD"]
+        self.bos_token_id = self.token_to_id["BOS"]
+        self.eos_token_id = self.token_to_id["EOS"]
+
+    @classmethod
+    def from_pretrained(cls, checkpoint_dir):
+        import json
+        from pathlib import Path
+
+        vocabulary = json.loads((Path(checkpoint_dir) / "vocab.json").read_text())
+        return cls(vocabulary)
+
+    def encode_tokens(self, tokens):
+        return [self.token_to_id[token] for token in tokens]
+
+    def decode_ids(self, token_ids):
+        return [self.id_to_token[int(token_id)] for token_id in token_ids]
+
+
+def norm(x):
+    return x * mx.rsqrt(mx.mean(x * x, axis=-1, keepdims=True) + 1e-5)
+
+
+def has_ve(layer_idx, n_layer):
+    return layer_idx % 2 == (n_layer - 1) % 2
+
+
+def create_additive_causal_mask(seq_len, dtype=mx.float32):
+    indices = mx.arange(seq_len)
+    blocked = indices[None, :] > indices[:, None]
+    return mx.where(blocked, mx.array(float("-inf"), dtype=dtype), mx.array(0.0, dtype=dtype))
+
+
+def create_sliding_window_mask(seq_len, window_size, dtype=mx.float32):
+    indices = mx.arange(seq_len)
+    causal = indices[None, :] > indices[:, None]
+    too_far = (indices[:, None] - indices[None, :]) >= window_size
+    blocked = causal | too_far
+    return mx.where(blocked, mx.array(float("-inf"), dtype=dtype), mx.array(0.0, dtype=dtype))
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.n_head = config.n_head
+        self.n_kv_head = config.n_kv_head
+        self.n_embd = config.n_embd
+        self.head_dim = self.n_embd // self.n_head
+        assert self.n_embd % self.n_head == 0
+        assert self.n_kv_head <= self.n_head and self.n_head % self.n_kv_head == 0
+        self.c_q = nn.Linear(self.n_embd, self.n_head * self.head_dim, bias=False)
+        self.c_k = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
+        self.c_v = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
+        self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)
+        self.ve_gate_channels = 32
+        self.ve_gate = (
+            nn.Linear(self.ve_gate_channels, self.n_kv_head, bias=False)
+            if has_ve(layer_idx, config.n_layer)
+            else None
+        )
+        self.rope = nn.RoPE(self.head_dim, traditional=True, base=10000)
+
+    def __call__(self, x, ve, mask):
+        batch_size, seq_len, _ = x.shape
+        q = self.c_q(x).reshape(batch_size, seq_len, self.n_head, self.head_dim)
+        k = self.c_k(x).reshape(batch_size, seq_len, self.n_kv_head, self.head_dim)
+        v = self.c_v(x).reshape(batch_size, seq_len, self.n_kv_head, self.head_dim)
+
+        if ve is not None and self.ve_gate is not None:
+            ve = ve.reshape(batch_size, seq_len, self.n_kv_head, self.head_dim)
+            gate = 2 * mx.sigmoid(self.ve_gate(x[..., : self.ve_gate_channels]))
+            v = v + mx.expand_dims(gate, axis=-1) * ve
+
+        q = q.transpose(0, 2, 1, 3)
+        k = k.transpose(0, 2, 1, 3)
+        v = v.transpose(0, 2, 1, 3)
+        q = norm(self.rope(q))
+        k = norm(self.rope(k))
+        scale = 1.0 / math.sqrt(self.head_dim)
+        y = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=mask)
+        y = y.transpose(0, 2, 1, 3).reshape(batch_size, seq_len, -1)
+        return self.c_proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False)
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False)
+
+    def __call__(self, x):
+        x = self.c_fc(x)
+        x = mx.maximum(x, 0) ** 2
+        return self.c_proj(x)
+
+
+class Block(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.attn = CausalSelfAttention(config, layer_idx)
+        self.mlp = MLP(config)
+
+    def __call__(self, x, ve, mask):
+        x = x + self.attn(norm(x), ve, mask)
+        x = x + self.mlp(norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.window_sizes = self._compute_window_sizes(config)
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.blocks = [Block(config, i) for i in range(config.n_layer)]
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.resid_lambdas = mx.ones((config.n_layer,), dtype=mx.float32)
+        self.x0_lambdas = mx.zeros((config.n_layer,), dtype=mx.float32)
+        head_dim = config.n_embd // config.n_head
+        kv_dim = config.n_kv_head * head_dim
+        self.value_embeds = {
+            str(i): nn.Embedding(config.vocab_size, kv_dim)
+            for i in range(config.n_layer)
+            if has_ve(i, config.n_layer)
+        }
+        self._mask_cache = {}
+
+    def init_weights(self):
+        n_embd = self.config.n_embd
+        scale = 3**0.5 * n_embd**-0.5
+        self.wte.weight = mx.random.normal(self.wte.weight.shape).astype(mx.bfloat16)
+        self.lm_head.weight = (mx.random.normal(self.lm_head.weight.shape) * 0.001).astype(mx.bfloat16)
+        for block in self.blocks:
+            block.attn.c_q.weight = mx.random.uniform(-scale, scale, block.attn.c_q.weight.shape).astype(mx.bfloat16)
+            block.attn.c_k.weight = mx.random.uniform(-scale, scale, block.attn.c_k.weight.shape).astype(mx.bfloat16)
+            block.attn.c_v.weight = mx.random.uniform(-scale, scale, block.attn.c_v.weight.shape).astype(mx.bfloat16)
+            block.attn.c_proj.weight = mx.zeros_like(block.attn.c_proj.weight).astype(mx.bfloat16)
+            block.mlp.c_fc.weight = mx.random.uniform(-scale, scale, block.mlp.c_fc.weight.shape).astype(mx.bfloat16)
+            block.mlp.c_proj.weight = mx.zeros_like(block.mlp.c_proj.weight).astype(mx.bfloat16)
+            if block.attn.ve_gate is not None:
+                block.attn.ve_gate.weight = mx.zeros_like(block.attn.ve_gate.weight).astype(mx.bfloat16)
+        self.resid_lambdas = mx.ones((self.config.n_layer,), dtype=mx.float32)
+        self.x0_lambdas = mx.full((self.config.n_layer,), 0.1, dtype=mx.float32)
+        for ve in self.value_embeds.values():
+            ve.weight = mx.random.uniform(-scale, scale, ve.weight.shape).astype(mx.bfloat16)
+
+    def _compute_window_sizes(self, config):
+        pattern = config.window_pattern.upper()
+        assert all(char in "SL" for char in pattern)
+        mapping = {"L": config.sequence_len, "S": config.sequence_len // 2}
+        windows = [mapping[pattern[i % len(pattern)]] for i in range(config.n_layer)]
+        windows[-1] = config.sequence_len
+        return windows
+
+    def _get_masks(self, seq_len):
+        dtype = self.wte.weight.dtype
+        for window_size in set(self.window_sizes):
+            key = (seq_len, window_size, dtype)
+            if key not in self._mask_cache:
+                self._mask_cache[key] = (
+                    create_additive_causal_mask(seq_len, dtype)
+                    if window_size >= seq_len
+                    else create_sliding_window_mask(seq_len, window_size, dtype)
+                )
+        return [self._mask_cache[(seq_len, size, dtype)] for size in self.window_sizes]
+
+    def __call__(self, idx, targets=None, reduction="mean"):
+        _, seq_len = idx.shape
+        masks = self._get_masks(seq_len)
+        x = norm(self.wte(idx))
+        x0 = x
+        for i, block in enumerate(self.blocks):
+            x = self.resid_lambdas[i].astype(x.dtype) * x + self.x0_lambdas[i].astype(x.dtype) * x0
+            ve = self.value_embeds[str(i)](idx) if str(i) in self.value_embeds else None
+            x = block(x, ve, masks[i])
+        logits = self.lm_head(norm(x)).astype(mx.float32)
+        logits = 15.0 * mx.tanh(logits / 15.0)
+        if targets is None:
+            return logits
+        valid = targets != -1
+        safe_targets = mx.where(valid, targets, mx.zeros_like(targets))
+        ce = nn.losses.cross_entropy(logits, safe_targets, reduction="none") * valid
+        if reduction == "none":
+            return ce
+        return mx.sum(ce) / mx.maximum(mx.sum(valid), 1)
+
+
+def load_model(checkpoint_dir):
+    """Load a published MLX checkpoint and its configuration."""
+    import json
+    from pathlib import Path
+
+    checkpoint_dir = Path(checkpoint_dir)
+    raw = json.loads((checkpoint_dir / "config.json").read_text())
+    config = GPTConfig(**raw["model_config"])
+    model = GPT(config)
+    load_weights(model, checkpoint_dir / "model.safetensors")
+    mx.eval(model.parameters())
+    return model
+
+
+def load_weights(model, weights_path):
+    """Load flattened weights without MLX confusing numeric dict keys for list indices."""
+    current = dict(tree_flatten(model.parameters()))
+    loaded = mx.load(str(weights_path))
+    if current.keys() != loaded.keys():
+        missing = sorted(current.keys() - loaded.keys())
+        extra = sorted(loaded.keys() - current.keys())
+        raise ValueError(f"Checkpoint key mismatch; missing={missing}, extra={extra}")
+    for path, value in loaded.items():
+        if current[path].shape != value.shape:
+            raise ValueError(f"Shape mismatch for {path}: {value.shape} != {current[path].shape}")
+        parts = path.split(".")
+        target = model
+        for part in parts[:-1]:
+            if isinstance(target, list):
+                target = target[int(part)]
+            elif isinstance(target, dict):
+                target = target[part]
+            else:
+                target = getattr(target, part)
+        final = parts[-1]
+        if isinstance(target, list):
+            target[int(final)] = value
+        elif isinstance(target, dict):
+            target[final] = value
+        else:
+            setattr(target, final, value)
+    return model

config.json

@@ -0,0 +1,17 @@
+{
+  "architectures": [
+    "GPT"
+  ],
+  "library_name": "mlx",
+  "model_config": {
+    "n_embd": 384,
+    "n_head": 3,
+    "n_kv_head": 3,
+    "n_layer": 6,
+    "sequence_len": 256,
+    "vocab_size": 2075,
+    "window_pattern": "SSSL"
+  },
+  "model_type": "human-chess-mlx",
+  "torch_dtype": "bfloat16"
+}

legal_inference.py

@@ -0,0 +1,66 @@
+"""Legal-move-masked inference for the published human-chess MLX model."""
+
+import argparse
+from pathlib import Path
+
+import chess
+import mlx.core as mx
+
+from chess_model import ChessTokenizer, load_model
+
+
+MOVE_VOCAB_SIZE = 1968
+
+
+def board_from_history(tokenizer, token_ids):
+    """Reconstruct the exact board while ignoring metadata tokens."""
+    board = chess.Board()
+    move_start = len(tokenizer.id_to_token) - MOVE_VOCAB_SIZE
+    for token_id in token_ids:
+        if int(token_id) < move_start:
+            continue
+        move = chess.Move.from_uci(tokenizer.id_to_token[int(token_id)])
+        if move not in board.legal_moves:
+            raise ValueError(f"Illegal move in supplied history: {move.uci()}")
+        board.push(move)
+    return board
+
+
+def predict_legal_moves(model, tokenizer, token_ids, top_k=10):
+    """Return `(uci, logit)` pairs after masking every illegal move."""
+    if not token_ids:
+        raise ValueError("At least one context token is required")
+    board = board_from_history(tokenizer, token_ids)
+    context = token_ids[-model.config.sequence_len :]
+    logits = model(mx.array([context]))[0, -1]
+    mx.eval(logits)
+
+    candidates = []
+    for move in board.legal_moves:
+        token_id = tokenizer.token_to_id.get(move.uci())
+        if token_id is not None:
+            candidates.append((move.uci(), float(logits[token_id].item())))
+    candidates.sort(key=lambda item: item[1], reverse=True)
+    return candidates[:top_k]
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("checkpoint", type=Path)
+    parser.add_argument(
+        "tokens",
+        nargs="+",
+        help="Full token history, including metadata and UCI moves.",
+    )
+    parser.add_argument("--top-k", type=int, default=10)
+    args = parser.parse_args()
+
+    tokenizer = ChessTokenizer.from_pretrained(args.checkpoint)
+    token_ids = tokenizer.encode_tokens(args.tokens)
+    model = load_model(args.checkpoint)
+    for move, score in predict_legal_moves(model, tokenizer, token_ids, args.top_k):
+        print(f"{move}\t{score:.6f}")
+
+
+if __name__ == "__main__":
+    main()

metrics.json

@@ -0,0 +1,12 @@
+{
+  "legal_masked_top1_accuracy": 0.30970463160285916,
+  "legal_masked_top5_accuracy": 0.6682182988643823,
+  "raw_top1_accuracy": 0.27678346260393144,
+  "raw_top1_legal_rate": 0.8729143926399501,
+  "test_legal_masked_top1_accuracy": 0.307254982970034,
+  "test_legal_masked_top5_accuracy": 0.6651906703856502,
+  "test_move_bits": 4.1147322227098035,
+  "test_raw_top1_accuracy": 0.27385924925821303,
+  "test_raw_top1_legal_rate": 0.8720350759973353,
+  "val_move_bits": 4.098970468943424
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b9becf48b847389a2df10caeb485ee5b9b79366c049e48687b34e0f819ba4b3
+size 29206716

requirements.txt

@@ -0,0 +1,2 @@
+mlx>=0.30.0
+python-chess>=1.999

special_tokens_map.json

@@ -0,0 +1,5 @@
+{
+  "bos_token": "BOS",
+  "eos_token": "EOS",
+  "pad_token": "PAD"
+}

tokenizer_config.json

@@ -0,0 +1,7 @@
+{
+  "bos_token": "BOS",
+  "eos_token": "EOS",
+  "model_max_length": 256,
+  "pad_token": "PAD",
+  "tokenizer_class": "ChessTokenizer"
+}

vocab.json

@@ -0,0 +1,2084 @@
+{
+  "dtype": "uint16_le",
+  "size": 2075,
+  "rating_bucket_width": 50,
+  "rating_min": 600,
+  "rating_max_exclusive": 2800,
+  "id_to_token": [
+    "PAD",
+    "BOS",
+    "EOS",
+    "RESULT_WHITE",
+    "RESULT_DRAW",
+    "RESULT_BLACK",
+    "RESULT_UNKNOWN",
+    "WHITE_ELO_625",
+    "WHITE_ELO_675",
+    "WHITE_ELO_725",
+    "WHITE_ELO_775",
+    "WHITE_ELO_825",
+    "WHITE_ELO_875",
+    "WHITE_ELO_925",
+    "WHITE_ELO_975",
+    "WHITE_ELO_1025",
+    "WHITE_ELO_1075",
+    "WHITE_ELO_1125",
+    "WHITE_ELO_1175",
+    "WHITE_ELO_1225",
+    "WHITE_ELO_1275",
+    "WHITE_ELO_1325",
+    "WHITE_ELO_1375",
+    "WHITE_ELO_1425",
+    "WHITE_ELO_1475",
+    "WHITE_ELO_1525",
+    "WHITE_ELO_1575",
+    "WHITE_ELO_1625",
+    "WHITE_ELO_1675",
+    "WHITE_ELO_1725",
+    "WHITE_ELO_1775",
+    "WHITE_ELO_1825",
+    "WHITE_ELO_1875",
+    "WHITE_ELO_1925",
+    "WHITE_ELO_1975",
+    "WHITE_ELO_2025",
+    "WHITE_ELO_2075",
+    "WHITE_ELO_2125",
+    "WHITE_ELO_2175",
+    "WHITE_ELO_2225",
+    "WHITE_ELO_2275",
+    "WHITE_ELO_2325",
+    "WHITE_ELO_2375",
+    "WHITE_ELO_2425",
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+    "WHITE_ELO_2575",
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+    "WHITE_ELO_2725",
+    "WHITE_ELO_2775",
+    "WHITE_ELO_LOW",
+    "WHITE_ELO_HIGH",
+    "WHITE_ELO_UNKNOWN",
+    "BLACK_ELO_625",
+    "BLACK_ELO_675",
+    "BLACK_ELO_725",
+    "BLACK_ELO_775",
+    "BLACK_ELO_825",
+    "BLACK_ELO_875",
+    "BLACK_ELO_925",
+    "BLACK_ELO_975",
+    "BLACK_ELO_1025",
+    "BLACK_ELO_1075",
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+    "BLACK_ELO_1275",
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+    "h8b2",
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+    "h8g6",
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+    "h8h1",
+    "h8h2",
+    "h8h3",
+    "h8h4",
+    "h8h5",
+    "h8h6",
+    "h8h7"
+  ]
+}