Chess Challenge submission by VreVre

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess_vre_3
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [VreVre](https://huggingface.co/VreVre)
+- **Parameters**: 974,004
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 77
+- **Embedding dim**: 92
+- **Layers**: 11
+- **Heads**: 4

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 92,
+  "n_head": 4,
+  "n_inner": 276,
+  "n_layer": 11,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 77
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8a76df446c01f8fdb2db8c3de97abfd36cc9fab6136285ec63562f21b9df254b
+size 3907440

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 treats each move as a single token using the extended UCI notation
+from the Lichess dataset (e.g., WPe2e4, BNg8f6).
+
+The dataset format uses:
+- W/B prefix for White/Black
+- Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
+- Source and destination squares (e.g., e2e4)
+- Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+
+import re
+
+class ChessTokenizer(PreTrainedTokenizer):
+    """
+    A custom tokenizer for chess moves using extended UCI notation.
+    
+    This tokenizer maps each possible chess move to a unique token ID.
+    The vocabulary is built from the training dataset to ensure all moves
+    encountered during training have a corresponding token.
+    
+    Example:
+        >>> tokenizer = ChessTokenizer()
+        >>> tokenizer.encode("WPe2e4 BPe7e5")
+        [1, 42, 87, 2]  # [BOS, e2e4, e7e5, EOS]
+    """
+    
+    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]"
+    EOM_TOKEN = "EOM"  # End of Move token
+    
+    MOVE_REGEX = re.compile(
+        r"""
+        (?P<color>[WB])
+        (?P<piece>[PNBRQK])
+        (?P<from>[a-h][1-8])
+        (?P<capture>x)?
+        (?P<to>[a-h][1-8])
+        (?P<promotion>[QRBN])?
+        (?P<check>[+#])?
+        """,
+        re.VERBOSE,
+    )
+    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: Additional arguments passed to PreTrainedTokenizer.
+        """
+        # 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
+        
+
+        # 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
+            # The full vocabulary should be built from the dataset
+            self._vocab = self._create_default_vocab()
+        
+        # Create reverse mapping
+        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+        
+        # 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]:
+        """
+        Create a minimal default vocabulary with just special tokens.
+        
+        For the full vocabulary, use `build_vocab_from_dataset()`.
+        This minimal vocab is just a placeholder - you should build from data.
+        """
+        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 tokenizer vocabulary from an iterator of game strings.
+        
+        Args:
+            iterator: An iterator yielding game strings (space-separated moves).
+            min_frequency: Minimum frequency for a token to be included.
+        
+        Returns:
+            A ChessTokenizer with the built vocabulary.
+        """
+        from collections import Counter
+        
+        token_counts = Counter()
+        
+        for game in iterator:
+            moves = game.strip().split()
+            token_counts.update(moves)
+        
+        # Filter by frequency
+        tokens = [
+            token for token, count in token_counts.items()
+            if count >= min_frequency
+        ]
+        
+        # Sort for reproducibility
+        tokens = sorted(tokens)
+        
+        # Build vocabulary
+        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)
+    
+
+    @classmethod
+    def build_vocab_from_iterator_bis(
+        cls,
+        iterator,
+        min_frequency: int = 1,
+    ) -> "ChessTokenizer":
+        from collections import Counter
+        import re
+
+        MOVE_REGEX = re.compile(
+            r"""
+            (?P<color>[WB])
+            (?P<piece>[PNBRQK])
+            (?P<from>[a-h][1-8])
+            (?P<capture>x)?
+            (?P<to>[a-h][1-8])
+            (?P<promotion>[QRBN])?
+            (?P<check>[+#])?
+            """,
+            re.VERBOSE,
+        )
+
+        token_counts = Counter()
+
+        for game in iterator:
+            moves = game.strip().split()
+
+            for move in moves:
+                # --- Castling ---
+                if move in ("WKe1g1", "BKe8g8"):
+                    token_counts.update(["W", "K", "CASTLE_K"])
+                    continue
+
+                if move in ("WKe1c1", "BKe8c8"):
+                    token_counts.update(["W", "K", "CASTLE_Q"])
+                    continue
+
+                m = MOVE_REGEX.fullmatch(move)
+                if not m:
+                    token_counts.update([cls.UNK_TOKEN])
+                    continue
+
+                # Color + piece
+                token_counts.update([
+                    m["color"],
+                    m["piece"],
+                    m['from'],
+                ])
+
+                if m["capture"]:
+                    token_counts.update(["CAP"])
+
+                # --- TO = FIN DE COUP ---
+                to_tok = m['to']
+
+                if m["promotion"]:
+                    to_tok += f"_PROM_{m['promotion']}"
+
+                if m["check"] == "+":
+                    to_tok += "_CHECK"
+                elif m["check"] == "#":
+                    to_tok += "_MATE"
+
+                
+
+                token_counts.update([to_tok])
+
+        tokens = [tok for tok, c in token_counts.items() if c >= min_frequency]
+        tokens = tokens + ["EOM"]
+        tokens = sorted(tokens)
+
+        special_tokens = [
+            cls.PAD_TOKEN,
+            cls.BOS_TOKEN,
+            cls.EOS_TOKEN,
+            cls.UNK_TOKEN,
+        ]
+
+        vocab = {tok: i for i, tok in enumerate(special_tokens + tokens)}
+        return cls(vocab=vocab)
+
+    @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 tokenizer vocabulary from a Hugging Face dataset.
+        
+        Args:
+            dataset_name: Name of the dataset on Hugging Face Hub.
+            split: Dataset split to use.
+            column: Column containing the game strings.
+            min_frequency: Minimum frequency for a token to be included (default: 500).
+            max_samples: Maximum number of samples to process (default: 100k).
+        
+        Returns:
+            A ChessTokenizer 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_bis(game_iterator(), min_frequency=min_frequency)
+    
+    @property
+    def vocab_size(self) -> int:
+        """Return the size of the vocabulary."""
+        return max(self._vocab.values()) + 1
+    
+    def get_vocab(self) -> Dict[str, int]:
+        """Return the vocabulary as a dictionary."""
+        return dict(self._vocab)
+
+        
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Convert a game string into subword tokens.
+        Example:
+        "WPe2e4 BNg8f6" ->
+        ["W","P","FROM_e2","TO_e4_EOM","B","N","FROM_g8","TO_f6_EOM"]
+        """
+        tokens: List[str] = []
+
+        MOVE_REGEX = re.compile(
+            r"""
+            (?P<color>[WB])
+            (?P<piece>[PNBRQK])
+            (?P<from>[a-h][1-8])
+            (?P<capture>x)?
+            (?P<to>[a-h][1-8])
+            (?P<promotion>[QRBN])?
+            (?P<check>[+#])?
+            """,
+            re.VERBOSE,
+        )
+
+        moves = text.split()
+
+        for move in moves:
+            # --- Castling ---
+            if move in ("WKe1g1", "BKe8g8"):
+                tokens.extend(["W", "K", "CASTLE_K", "EOM"])
+                continue
+
+            if move in ("WKe1c1", "BKe8c8"):
+                tokens.extend(["W", "K", "CASTLE_Q", "EOM"])
+                continue
+
+            m = MOVE_REGEX.fullmatch(move)
+            if not m:
+                tokens.append(self.UNK_TOKEN)
+                continue
+
+            # Color & piece
+            tokens.append(m["color"])
+            tokens.append(m["piece"])
+            tokens.append(f"{m['from']}")
+
+            if m["capture"]:
+                tokens.append("CAP")
+
+            # --- TO + FLAGS + EOM ---
+            to_tok = f"{m['to']}"
+
+            if m["promotion"]:
+                to_tok += f"_PROM_{m['promotion']}"
+
+            if m["check"] == "+":
+                to_tok += "_CHECK"
+            elif m["check"] == "#":
+                to_tok += "_MATE"
+
+            
+
+            tokens.append(to_tok)
+            if to_tok:
+                tokens.append("EOM")
+
+        return tokens
+
+    
+    def _convert_token_to_id(self, token: str) -> int:
+        """Convert a token to its ID."""
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
+    
+    def _convert_id_to_token(self, index: int) -> str:
+        """Convert an ID to its token."""
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+    
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        special = {
+            self.PAD_TOKEN,
+            self.BOS_TOKEN,
+            self.EOS_TOKEN,
+            self.UNK_TOKEN,
+        }
+
+        moves = []
+        current = []
+
+        for tok in tokens:
+            if tok in special:
+                continue
+
+            if tok == "EOM":
+                moves.append("".join(current) + " ")
+                current = []
+                continue
+
+            if tok == "W" or tok == "B":
+                current.append(tok)
+
+            elif tok in {"P","N","B","R","Q","K"}:
+                current.append(tok)
+
+            elif tok == "CAP":
+                current.append("x")
+            
+            elif tok.startswith("CASTLE"):
+                if tok == "CASTLE_K":
+                    moves.append("O-O ")
+                elif tok == "CASTLE_Q":
+                    moves.append("O-O-O ")
+                current = []
+
+            else :
+                current.append(tok[:2])
+                body = tok[2:]
+
+                if not body:
+                    continue
+                # Split flags
+                parts = body.split("_")
+                square = parts[0]
+                current.append(square)
+
+                for p in parts[1:]:
+                    if p == "PROM":
+                        continue
+                    elif p in {"Q","R","B","N"}:
+                        current.append(p)
+                    elif p == "CHECK":
+                        current.append("+")
+                    elif p == "MATE":
+                        current.append("#")
+
+
+
+            
+        moves.append("".join(current))
+
+        return "".join(moves)
+
+
+    
+    def save_vocabulary(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None,
+    ) -> tuple:
+        """
+        Save the vocabulary to a JSON file.
+        
+        Args:
+            save_directory: Directory to save the vocabulary.
+            filename_prefix: Optional prefix for the filename.
+        
+        Returns:
+            Tuple containing the path to the saved vocabulary file.
+        """
+        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).
+    
+    Args:
+        dataset_name: Name of the dataset on Hugging Face Hub.
+        split: Dataset split to use.
+        column: Column containing the game strings.
+        max_samples: Maximum number of samples to process.
+    
+    Returns:
+        Dictionary mapping tokens to their frequencies.
+    """
+    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

@@ -0,0 +1,50 @@
+{
+  "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
+    },
+    "12": {
+      "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]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.json

@@ -0,0 +1,78 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 12,
+  "B": 4,
+  "EOM": 5,
+  "K": 6,
+  "N": 7,
+  "P": 8,
+  "Q": 9,
+  "R": 10,
+  "W": 11,
+  "a1": 13,
+  "a2": 14,
+  "a3": 15,
+  "a4": 16,
+  "a5": 17,
+  "a6": 18,
+  "a7": 19,
+  "a8": 20,
+  "b1": 21,
+  "b2": 22,
+  "b3": 23,
+  "b4": 24,
+  "b5": 25,
+  "b6": 26,
+  "b7": 27,
+  "b8": 28,
+  "c1": 29,
+  "c2": 30,
+  "c3": 31,
+  "c4": 32,
+  "c5": 33,
+  "c6": 34,
+  "c7": 35,
+  "c8": 36,
+  "d1": 37,
+  "d2": 38,
+  "d3": 39,
+  "d4": 40,
+  "d5": 41,
+  "d6": 42,
+  "d7": 43,
+  "d8": 44,
+  "e1": 45,
+  "e2": 46,
+  "e3": 47,
+  "e4": 48,
+  "e5": 49,
+  "e6": 50,
+  "e7": 51,
+  "e8": 52,
+  "f1": 53,
+  "f2": 54,
+  "f3": 55,
+  "f4": 56,
+  "f5": 57,
+  "f6": 58,
+  "f7": 59,
+  "f8": 60,
+  "g1": 61,
+  "g2": 62,
+  "g3": 63,
+  "g4": 64,
+  "g5": 65,
+  "g6": 66,
+  "g7": 67,
+  "g8": 68,
+  "h1": 69,
+  "h2": 70,
+  "h3": 71,
+  "h4": 72,
+  "h5": 73,
+  "h6": 74,
+  "h7": 75,
+  "h8": 76
+}