Chess Challenge submission by Tanguy85

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-carlsen
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [Tanguy85](https://huggingface.co/Tanguy85)
+- **Parameters**: 982,196
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 73
+- **Embedding dim**: 128
+- **Layers**: 6
+- **Heads**: 8

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.05,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 128,
+  "n_head": 8,
+  "n_inner": 350,
+  "n_layer": 6,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 73
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5cac79454b4a4b53f214becc812557ab985c17474cf338a657ae8384ce3d91d
+size 3935232

special_tokens_map.json

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+{
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
+}

tokenizer.py

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+"""
+Custom Chess Tokenizer for the Chess Challenge.
+
+This tokenizer supports TWO tokenization modes:
+
+1) tokenization_mode="move"  (original)
+   - Each move is a single token using the extended UCI notation
+     from the Lichess dataset (e.g., WPe2e4, BNg8f6, WPe7e8=Q(x+), ...).
+   - Vocabulary is usually built from the dataset (frequency threshold).
+
+2) tokenization_mode="uci_square"  (recommended for good legal-move performance with small vocab)
+   - Each move is decomposed into 3 tokens:
+       [from_square, to_square, promotion_or_-]
+     Example:
+       "WPe2e4"      -> ["e2", "e4", "-"]
+       "WPe7e8=Q(+)" -> ["e7", "e8", "q"]
+   - Fixed vocabulary that can express ANY UCI move:
+       specials (4) + squares (64) + promo tokens (5) = 73 tokens.
+
+Why uci_square helps:
+- You can keep vocab tiny (70-150 range) WITHOUT losing expressivity,
+  so the model can still output any move.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+
+
+class ChessTokenizer(PreTrainedTokenizer):
+    """
+    A custom tokenizer for chess moves.
+
+    - "move" mode: extended-uci move tokens like "WPe2e4"
+    - "uci_square" mode: squares + promotion tokens
+    """
+
+    model_input_names = ["input_ids", "attention_mask"]
+    vocab_files_names = {"vocab_file": "vocab.json"}
+
+    # Special tokens
+    PAD_TOKEN = "[PAD]"
+    BOS_TOKEN = "[BOS]"
+    EOS_TOKEN = "[EOS]"
+    UNK_TOKEN = "[UNK]"
+
+    def __init__(
+        self,
+        vocab_file: Optional[str] = None,
+        vocab: Optional[Dict[str, int]] = None,
+        **kwargs,
+    ):
+        """
+        Initialize the chess tokenizer.
+
+        Args:
+            vocab_file: Path to a JSON file containing the vocabulary mapping.
+            vocab: Dictionary mapping tokens to IDs (alternative to vocab_file).
+            kwargs:
+              - tokenization_mode: "move" (default) or "uci_square"
+              - plus usual HF tokenizer kwargs
+        """
+        # Initialize special tokens
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+
+        # Read tokenization_mode from kwargs (and keep it for save/load)
+        tokenization_mode = kwargs.pop("tokenization_mode", "move")
+        if tokenization_mode not in ("move", "uci_square"):
+            raise ValueError(f"Unknown tokenization_mode={tokenization_mode!r}")
+        self.tokenization_mode = tokenization_mode
+
+        # Remove any duplicate special-token entries passed through kwargs
+        # to avoid "multiple values for keyword" errors when loading from disk.
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+
+        # Load or create vocabulary
+        if vocab is not None:
+            self._vocab = vocab
+        elif vocab_file is not None and os.path.exists(vocab_file):
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            # Create a minimal vocabulary with just special tokens
+            # (you should build from dataset or use build_uci_square_vocab)
+            self._vocab = self._create_default_vocab()
+
+        # Create reverse mapping
+        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+
+        # Ensure tokenization_mode is saved in tokenizer_config.json
+        kwargs["tokenization_mode"] = self.tokenization_mode
+
+        # Call parent init AFTER setting up vocab
+        super().__init__(
+            pad_token=self._pad_token,
+            bos_token=self._bos_token,
+            eos_token=self._eos_token,
+            unk_token=self._unk_token,
+            **kwargs,
+        )
+
+    def _create_default_vocab(self) -> Dict[str, int]:
+        """
+        Minimal default vocabulary with just special tokens.
+        """
+        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
+        vocab = {token: idx for idx, token in enumerate(special_tokens)}
+        return vocab
+
+    @classmethod
+    def build_vocab_from_iterator(
+        cls,
+        iterator,
+        min_frequency: int = 1,
+    ) -> "ChessTokenizer":
+        """
+        Build a "move" tokenizer vocabulary from an iterator of game strings.
+
+        Args:
+            iterator: yields game strings (space-separated moves).
+            min_frequency: minimum frequency for a token to be included.
+
+        Returns:
+            ChessTokenizer(tokenization_mode="move") with the built vocabulary.
+        """
+        from collections import Counter
+
+        token_counts = Counter()
+
+        for game in iterator:
+            moves = game.strip().split()
+            token_counts.update(moves)
+
+        tokens = [token for token, count in token_counts.items() if count >= min_frequency]
+        tokens = sorted(tokens)
+
+        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+        vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
+
+        return cls(vocab=vocab, tokenization_mode="move")
+
+    @classmethod
+    def build_vocab_from_dataset(
+        cls,
+        dataset_name: str = "dlouapre/lichess_2025-01_1M",
+        split: str = "train",
+        column: str = "text",
+        min_frequency: int = 500,
+        max_samples: Optional[int] = 100000,
+    ) -> "ChessTokenizer":
+        """
+        Build a "move" tokenizer vocabulary from a Hugging Face dataset.
+
+        Args:
+            dataset_name: dataset on HF Hub.
+            split: dataset split.
+            column: column containing game strings.
+            min_frequency: minimum frequency for a token to be included.
+            max_samples: max number of samples to process.
+
+        Returns:
+            ChessTokenizer(tokenization_mode="move") with the built vocabulary.
+        """
+        from datasets import load_dataset
+
+        dataset = load_dataset(dataset_name, split=split)
+
+        if max_samples is not None:
+            dataset = dataset.select(range(min(max_samples, len(dataset))))
+
+        def game_iterator():
+            for example in dataset:
+                yield example[column]
+
+        return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency)
+
+    @classmethod
+    def build_uci_square_vocab(cls) -> "ChessTokenizer":
+        """
+        Build a fixed tiny vocab that can express ANY UCI move using 3 tokens:
+        [from_square, to_square, promotion_or_-].
+
+        Vocab:
+          - 4 specials
+          - 64 squares (a1..h8)
+          - 5 promo tokens: "-", "q", "r", "b", "n"
+        Total = 73 tokens.
+        """
+        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+
+        files = "abcdefgh"
+        ranks = "12345678"
+        squares = [f"{f}{r}" for r in ranks for f in files]  # 64
+
+        promo = ["-", "q", "r", "b", "n"]  # 5
+
+        vocab = {tok: i for i, tok in enumerate(special + squares + promo)}
+        return cls(vocab=vocab, tokenization_mode="uci_square")
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self._vocab)
+
+    def get_vocab(self) -> Dict[str, int]:
+        return dict(self._vocab)
+
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Tokenize a string.
+
+        - mode="move": split on spaces (original dataset tokens like "WPe2e4").
+        - mode="uci_square": each dataset move token -> [from_sq, to_sq, promo_or_-]
+          Example: "WPe2e4" -> ["e2", "e4", "-"]
+                   "WPe7e8=Q" -> ["e7", "e8", "q"]
+        """
+        tokens = text.strip().split()
+
+        if self.tokenization_mode != "uci_square":
+            return tokens
+
+        out: List[str] = []
+        for tok in tokens:
+            # Keep special tokens as-is if they appear in text
+            if tok in self._vocab:
+                out.append(tok)
+                continue
+
+            # Typical dataset format:
+            #   [W|B][Piece][from_sq][to_sq]... possibly "(x)" "(+)" "(o)" "=Q" etc.
+            # Examples:
+            #   WPe2e4
+            #   BNg8f6
+            #   WPe7e8=Q(+)
+            #   WPe5d6(x)
+            if len(tok) >= 6 and tok[0] in ("W", "B"):
+                from_sq = tok[2:4]
+                to_sq = tok[4:6]
+
+                if re.fullmatch(r"[a-h][1-8]", from_sq) and re.fullmatch(r"[a-h][1-8]", to_sq):
+                    promo = "-"
+                    if "=" in tok:
+                        i = tok.index("=")
+                        if i + 1 < len(tok):
+                            p = tok[i + 1].lower()
+                            if p in ("q", "r", "b", "n"):
+                                promo = p
+                    out.extend([from_sq, to_sq, promo])
+                    continue
+
+            # Fallback: find two squares anywhere in token
+            squares = re.findall(r"[a-h][1-8]", tok)
+            if len(squares) >= 2:
+                promo = "-"
+                m = re.search(r"[=]?([qrbnQRBN])", tok)
+                if m:
+                    promo = m.group(1).lower()
+                out.extend([squares[0], squares[1], promo])
+            else:
+                out.append(self.UNK_TOKEN)
+
+        return out
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        # Filter out special tokens for cleaner output
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+        return " ".join(t for t in tokens if t not in special)
+
+    def save_vocabulary(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None,
+    ) -> tuple:
+        if not os.path.isdir(save_directory):
+            os.makedirs(save_directory, exist_ok=True)
+
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
+        )
+
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
+
+        return (vocab_file,)
+
+
+def count_vocab_from_dataset(
+    dataset_name: str = "dlouapre/lichess_2025-01_1M",
+    split: str = "train",
+    column: str = "text",
+    max_samples: Optional[int] = 10000,
+) -> Dict[str, int]:
+    """
+    Count token frequencies in a dataset (useful for vocabulary analysis).
+    """
+    from collections import Counter
+    from datasets import load_dataset
+
+    dataset = load_dataset(dataset_name, split=split)
+
+    if max_samples is not None:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+
+    token_counts = Counter()
+
+    for example in dataset:
+        moves = example[column].strip().split()
+        token_counts.update(moves)
+
+    return dict(token_counts)

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      null
+    ]
+  },
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenization_mode": "uci_square",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.json

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+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "a1": 4,
+  "b1": 5,
+  "c1": 6,
+  "d1": 7,
+  "e1": 8,
+  "f1": 9,
+  "g1": 10,
+  "h1": 11,
+  "a2": 12,
+  "b2": 13,
+  "c2": 14,
+  "d2": 15,
+  "e2": 16,
+  "f2": 17,
+  "g2": 18,
+  "h2": 19,
+  "a3": 20,
+  "b3": 21,
+  "c3": 22,
+  "d3": 23,
+  "e3": 24,
+  "f3": 25,
+  "g3": 26,
+  "h3": 27,
+  "a4": 28,
+  "b4": 29,
+  "c4": 30,
+  "d4": 31,
+  "e4": 32,
+  "f4": 33,
+  "g4": 34,
+  "h4": 35,
+  "a5": 36,
+  "b5": 37,
+  "c5": 38,
+  "d5": 39,
+  "e5": 40,
+  "f5": 41,
+  "g5": 42,
+  "h5": 43,
+  "a6": 44,
+  "b6": 45,
+  "c6": 46,
+  "d6": 47,
+  "e6": 48,
+  "f6": 49,
+  "g6": 50,
+  "h6": 51,
+  "a7": 52,
+  "b7": 53,
+  "c7": 54,
+  "d7": 55,
+  "e7": 56,
+  "f7": 57,
+  "g7": 58,
+  "h7": 59,
+  "a8": 60,
+  "b8": 61,
+  "c8": 62,
+  "d8": 63,
+  "e8": 64,
+  "f8": 65,
+  "g8": 66,
+  "h8": 67,
+  "-": 68,
+  "q": 69,
+  "r": 70,
+  "b": 71,
+  "n": 72
+}