Chess Challenge submission by TristanoBer

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# TristanB_3
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [TristanoBer](https://huggingface.co/TristanoBer)
+- **Parameters**: 948,784
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 153
+- **Embedding dim**: 152
+- **Layers**: 4
+- **Heads**: 4

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "model_type": "chess_transformer",
+  "n_ctx": 768,
+  "n_embd": 152,
+  "n_head": 4,
+  "n_inner": 304,
+  "n_layer": 4,
+  "norm_epsilon": 1e-06,
+  "pad_token_id": 0,
+  "rope_theta": 10000.0,
+  "tie_weights": true,
+  "transformers_version": "4.57.2",
+  "use_rmsnorm": true,
+  "vocab_size": 153
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2201ceba8b3f7b6f2d11ab40f4d0570ce2f879fd3735384db70c8fa49a0f028
+size 3797720

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 prev_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]"
+    
+    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_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(game_iterator(), min_frequency=min_frequency)
+    
+    @property
+    def vocab_size(self) -> int:
+        """Return the size of the vocabulary."""
+        return len(self._vocab)
+    
+    def get_vocab(self) -> Dict[str, int]:
+        """Return the vocabulary as a dictionary."""
+        return dict(self._vocab)
+    
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Tokenize a string of moves into a list of tokens.
+        
+        Args:
+            text: A string of space-separated moves.
+        
+        Returns:
+            List of move tokens.
+        """
+        return text.strip().split()
+    
+    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:
+        """Convert a list of tokens back to a string."""
+        # 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:
+        """
+        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,)
+
+
+
+
+class ChessTokenizer(PreTrainedTokenizer):
+    """
+    Semi-factorized chess tokenizer.
+
+    Each move is encoded as:
+      - PATTERN token: W_P, B_N, ...
+      - FROM_square token: FROM_e2
+      - TO_square token: TO_e4
+      - Optional suffix: CAPTURE, PROMO_Q, ...
+    
+    Castling is encoded as a single token: W_OO, W_OOO, B_OO, B_OOO
+    """
+
+    model_input_names = ["input_ids", "attention_mask"]
+    vocab_files_names = {"vocab_file": "vocab.json"}
+
+    PAD_TOKEN = "[PAD]"
+    BOS_TOKEN = "[BOS]"
+    EOS_TOKEN = "[EOS]"
+    UNK_TOKEN = "[UNK]"
+
+    def __init__(
+        self,
+        vocab: Optional[Dict[str, int]] = None,
+        vocab_file: Optional[str] = None,
+        **kwargs,
+    ):
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+
+        if vocab is not None:
+            self._vocab = vocab
+        elif vocab_file and os.path.exists(vocab_file):
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            self._vocab = self._build_base_vocab()
+
+        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+
+        super().__init__(
+            pad_token=self._pad_token,
+            bos_token=self._bos_token,
+            eos_token=self._eos_token,
+            unk_token=self._unk_token,
+            **kwargs,
+        )
+
+    # ------------------------------------------------------------------
+    # Vocabulary
+    # ------------------------------------------------------------------
+
+    def _build_base_vocab(self) -> Dict[str, int]:
+        vocab = {}
+
+        def add(tok):
+            vocab[tok] = len(vocab)
+
+        # Special tokens
+        for tok in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]:
+            add(tok)
+
+        # Patterns: W_P, B_N, ...
+        for color in ["W", "B"]:
+            for piece in ["P", "N", "B", "R", "Q", "K"]:
+                add(f"{color}_{piece}")
+
+        # Castling
+        for tok in ["W_OO", "W_OOO", "B_OO", "B_OOO"]:
+            add(tok)
+
+        # Squares
+        files = "abcdefgh"
+        ranks = "12345678"
+        for f in files:
+            for r in ranks:
+                add(f"FROM_{f}{r}")
+                add(f"TO_{f}{r}")
+
+        # Suffixes
+        add("CAPTURE")
+        for p in ["Q", "R", "B", "N"]:
+            add(f"PROMO_{p}")
+
+        return vocab
+
+    # ------------------------------------------------------------------
+    # Tokenization
+    # ------------------------------------------------------------------
+
+    MOVE_RE = re.compile(
+        r"(?P<color>[WB])"
+        r"(?P<piece>[PNBRQK])"
+        r"(?P<from>[a-h][1-8])"
+        r"(?P<to>[a-h][1-8])"
+        r"(?P<suffix>.*)?"
+    )
+
+    def _tokenize(self, text: str) -> List[str]:
+        tokens = []
+        moves = text.strip().split()
+
+        for move in moves:
+            # Castling
+            if "(O)" in move or "(o)" in move:
+                if move.startswith("W"):
+                    tokens.append("W_OO")
+                else:
+                    tokens.append("B_OO")
+                continue
+
+            if "(OO)" in move or "(OOO)" in move:
+                tokens.append("W_OOO" if move.startswith("W") else "B_OOO")
+                continue
+
+            m = self.MOVE_RE.match(move)
+            if not m:
+                tokens.append(self.UNK_TOKEN)
+                continue
+
+            color = m["color"]
+            piece = m["piece"]
+            from_sq = m["from"]
+            to_sq = m["to"]
+            suffix = m["suffix"] or ""
+
+            tokens.append(f"{color}_{piece}")
+            tokens.append(f"FROM_{from_sq}")
+            tokens.append(f"TO_{to_sq}")
+
+            if "(x)" in suffix:
+                tokens.append("CAPTURE")
+
+            if "=" in suffix:
+                promo = suffix[-1]
+                tokens.append(f"PROMO_{promo}")
+
+        return tokens
+    
+    def get_vocab(self) -> Dict[str, int]:
+        return dict(self._vocab)
+
+    # ------------------------------------------------------------------
+    # Conversions
+    # ------------------------------------------------------------------
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab.get(token, self._vocab[self.UNK_TOKEN])
+
+    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:
+        # For evaluation, we usually decode move-by-move elsewhere
+        return " ".join(tokens)
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
+        os.makedirs(save_directory, exist_ok=True)
+        path = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
+        )
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(self._vocab, f, indent=2)
+        return (path,)
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self._vocab)
+
+
+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 +1,53 @@
+{
+  "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,
+  "dataset_name": "dlouapre/lichess_2025-01_1M",
+  "eos_token": "[EOS]",
+  "extra_special_tokens": {},
+  "max_samples": 100000,
+  "min_frequency": 500,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.json

@@ -0,0 +1,155 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "W_P": 4,
+  "W_N": 5,
+  "W_B": 6,
+  "W_R": 7,
+  "W_Q": 8,
+  "W_K": 9,
+  "B_P": 10,
+  "B_N": 11,
+  "B_B": 12,
+  "B_R": 13,
+  "B_Q": 14,
+  "B_K": 15,
+  "W_OO": 16,
+  "W_OOO": 17,
+  "B_OO": 18,
+  "B_OOO": 19,
+  "FROM_a1": 20,
+  "TO_a1": 21,
+  "FROM_a2": 22,
+  "TO_a2": 23,
+  "FROM_a3": 24,
+  "TO_a3": 25,
+  "FROM_a4": 26,
+  "TO_a4": 27,
+  "FROM_a5": 28,
+  "TO_a5": 29,
+  "FROM_a6": 30,
+  "TO_a6": 31,
+  "FROM_a7": 32,
+  "TO_a7": 33,
+  "FROM_a8": 34,
+  "TO_a8": 35,
+  "FROM_b1": 36,
+  "TO_b1": 37,
+  "FROM_b2": 38,
+  "TO_b2": 39,
+  "FROM_b3": 40,
+  "TO_b3": 41,
+  "FROM_b4": 42,
+  "TO_b4": 43,
+  "FROM_b5": 44,
+  "TO_b5": 45,
+  "FROM_b6": 46,
+  "TO_b6": 47,
+  "FROM_b7": 48,
+  "TO_b7": 49,
+  "FROM_b8": 50,
+  "TO_b8": 51,
+  "FROM_c1": 52,
+  "TO_c1": 53,
+  "FROM_c2": 54,
+  "TO_c2": 55,
+  "FROM_c3": 56,
+  "TO_c3": 57,
+  "FROM_c4": 58,
+  "TO_c4": 59,
+  "FROM_c5": 60,
+  "TO_c5": 61,
+  "FROM_c6": 62,
+  "TO_c6": 63,
+  "FROM_c7": 64,
+  "TO_c7": 65,
+  "FROM_c8": 66,
+  "TO_c8": 67,
+  "FROM_d1": 68,
+  "TO_d1": 69,
+  "FROM_d2": 70,
+  "TO_d2": 71,
+  "FROM_d3": 72,
+  "TO_d3": 73,
+  "FROM_d4": 74,
+  "TO_d4": 75,
+  "FROM_d5": 76,
+  "TO_d5": 77,
+  "FROM_d6": 78,
+  "TO_d6": 79,
+  "FROM_d7": 80,
+  "TO_d7": 81,
+  "FROM_d8": 82,
+  "TO_d8": 83,
+  "FROM_e1": 84,
+  "TO_e1": 85,
+  "FROM_e2": 86,
+  "TO_e2": 87,
+  "FROM_e3": 88,
+  "TO_e3": 89,
+  "FROM_e4": 90,
+  "TO_e4": 91,
+  "FROM_e5": 92,
+  "TO_e5": 93,
+  "FROM_e6": 94,
+  "TO_e6": 95,
+  "FROM_e7": 96,
+  "TO_e7": 97,
+  "FROM_e8": 98,
+  "TO_e8": 99,
+  "FROM_f1": 100,
+  "TO_f1": 101,
+  "FROM_f2": 102,
+  "TO_f2": 103,
+  "FROM_f3": 104,
+  "TO_f3": 105,
+  "FROM_f4": 106,
+  "TO_f4": 107,
+  "FROM_f5": 108,
+  "TO_f5": 109,
+  "FROM_f6": 110,
+  "TO_f6": 111,
+  "FROM_f7": 112,
+  "TO_f7": 113,
+  "FROM_f8": 114,
+  "TO_f8": 115,
+  "FROM_g1": 116,
+  "TO_g1": 117,
+  "FROM_g2": 118,
+  "TO_g2": 119,
+  "FROM_g3": 120,
+  "TO_g3": 121,
+  "FROM_g4": 122,
+  "TO_g4": 123,
+  "FROM_g5": 124,
+  "TO_g5": 125,
+  "FROM_g6": 126,
+  "TO_g6": 127,
+  "FROM_g7": 128,
+  "TO_g7": 129,
+  "FROM_g8": 130,
+  "TO_g8": 131,
+  "FROM_h1": 132,
+  "TO_h1": 133,
+  "FROM_h2": 134,
+  "TO_h2": 135,
+  "FROM_h3": 136,
+  "TO_h3": 137,
+  "FROM_h4": 138,
+  "TO_h4": 139,
+  "FROM_h5": 140,
+  "TO_h5": 141,
+  "FROM_h6": 142,
+  "TO_h6": 143,
+  "FROM_h7": 144,
+  "TO_h7": 145,
+  "FROM_h8": 146,
+  "TO_h8": 147,
+  "CAPTURE": 148,
+  "PROMO_Q": 149,
+  "PROMO_R": 150,
+  "PROMO_B": 151,
+  "PROMO_N": 152
+}