Chess Challenge submission by Bapfel

README.md

@@ -0,0 +1,26 @@
+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# Bapfel-chess-v2
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [Bapfel](https://huggingface.co/Bapfel)
+- **Parameters**: 1,040,448
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 1729
+- **Embedding dim**: 128
+- **Layers**: 6
+- **Heads**: 8

config.json

@@ -0,0 +1,20 @@
+{
+  "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": 128,
+  "n_head": 8,
+  "n_inner": 384,
+  "n_layer": 6,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 1729
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f278ab2e2953f676fdefe5a23bfbf66a414040d868a6ed1ca304bb91647f29b
+size 4169208

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "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
+import numpy as np
+from transformers import PreTrainedTokenizer
+
+
+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]"
+    
+    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()
+        l=[]
+        for game in iterator:
+            moves = game.strip().split()
+            l.append(len(moves))
+            token_counts.update(moves)
+        print(np.array(l).mean())
+        # Filter by frequency
+        tokens = [
+            token[1:] for token, count in token_counts.items()
+            if count >= min_frequency
+        ]
+
+        print(tokens)
+        # Sort for reproducibility
+        tokens = sorted(list(set(tokens)))
+        tokens2=["W","B","K","P","R","Q","N","(x)","(+)","(+*)","(o)","(O)","O", "O-O", "O-O-O"]
+        str1="abcdefgh"
+        for el in str1 :
+            for i in range(1,9) :
+                tokens.append(el+str(i))
+        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+        vocab = {token: idx for idx, token in enumerate(special_tokens +tokens2+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 = 10,
+        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):
+        tokens = []
+
+        for move in text.strip().split():
+            if len(move) < 2 or move[0] not in ("W", "B"):
+                tokens.append(self.UNK_TOKEN)
+                continue
+
+            color = move[0]
+            core = move[1:]
+
+            tokens.append(color)
+
+            # Castling
+            if core in ("O", "O-O", "O-O-O"):
+                tokens.append(core)
+                continue
+
+            # Piece
+            piece = core[0]
+            rest = core[1:]
+
+            if piece in self._vocab:
+                tokens.append(piece)
+            else:
+                tokens.append(self.UNK_TOKEN)
+
+            # Squares (e2e4)
+            if len(rest) >= 4:
+                from_sq = rest[:2]
+                to_sq = rest[2:4]
+
+                tokens.append(from_sq if from_sq in self._vocab else self.UNK_TOKEN)
+                tokens.append(to_sq if to_sq in self._vocab else self.UNK_TOKEN)
+
+                suffix = rest[4:]
+                if suffix in self._vocab:
+                    tokens.append(suffix)
+
+        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:
+        """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,)
+
+
+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
+    },
+    "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]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.json

@@ -0,0 +1,1731 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "W": 4,
+  "B": 5,
+  "K": 6,
+  "P": 7,
+  "R": 8,
+  "Q": 9,
+  "N": 10,
+  "(x)": 11,
+  "(+)": 12,
+  "(+*)": 13,
+  "(o)": 14,
+  "(O)": 15,
+  "O": 16,
+  "O-O": 17,
+  "O-O-O": 18,
+  "Ba3b2": 19,
+  "Ba4b3": 20,
+  "Ba4c2": 21,
+  "Ba5b6": 22,
+  "Ba6b7": 23,
+  "Bb2a3": 24,
+  "Bb2c1": 25,
+  "Bb2c3": 26,
+  "Bb2c3(x)": 27,
+  "Bb2d4": 28,
+  "Bb2d4(x)": 29,
+  "Bb2e5(x)": 30,
+  "Bb2f6(x)": 31,
+  "Bb2g7(x)": 32,
+  "Bb3a2": 33,
+  "Bb3c2": 34,
+  "Bb3d5": 35,
+  "Bb3d5(x)": 36,
+  "Bb3e6(x)": 37,
+  "Bb4a5": 38,
+  "Bb4c3(x)": 39,
+  "Bb4c3(x+)": 40,
+  "Bb4c5": 41,
+  "Bb4d2(x)": 42,
+  "Bb4d2(x+)": 43,
+  "Bb4d6": 44,
+  "Bb4e7": 45,
+  "Bb5a4": 46,
+  "Bb5c4": 47,
+  "Bb5c6(x)": 48,
+  "Bb5c6(x+)": 49,
+  "Bb5d3": 50,
+  "Bb5d7(x)": 51,
+  "Bb5d7(x+)": 52,
+  "Bb5e2": 53,
+  "Bb6c7": 54,
+  "Bb6d4(x)": 55,
+  "Bb7a6": 56,
+  "Bb7c6": 57,
+  "Bb7c6(x)": 58,
+  "Bb7c8": 59,
+  "Bb7d5": 60,
+  "Bb7d5(x)": 61,
+  "Bb7e4(x)": 62,
+  "Bb7f3(x)": 63,
+  "Bb7g2(x)": 64,
+  "Bc1a3": 65,
+  "Bc1b2": 66,
+  "Bc1d2": 67,
+  "Bc1d2(x)": 68,
+  "Bc1e3": 69,
+  "Bc1e3(x)": 70,
+  "Bc1f4": 71,
+  "Bc1f4(x)": 72,
+  "Bc1g5": 73,
+  "Bc1g5(x)": 74,
+  "Bc1h6": 75,
+  "Bc1h6(x)": 76,
+  "Bc2b3": 77,
+  "Bc2e4(x)": 78,
+  "Bc3d2": 79,
+  "Bc4a2": 80,
+  "Bc4b3": 81,
+  "Bc4b5": 82,
+  "Bc4b5(+)": 83,
+  "Bc4d3": 84,
+  "Bc4d5": 85,
+  "Bc4d5(x)": 86,
+  "Bc4e2": 87,
+  "Bc4e6(x)": 88,
+  "Bc4f7(x)": 89,
+  "Bc4f7(x+)": 90,
+  "Bc5a7": 91,
+  "Bc5b4": 92,
+  "Bc5b4(+)": 93,
+  "Bc5b6": 94,
+  "Bc5d4": 95,
+  "Bc5d4(x)": 96,
+  "Bc5d6": 97,
+  "Bc5e3(x)": 98,
+  "Bc5e7": 99,
+  "Bc5f2(x+)": 100,
+  "Bc6b5": 101,
+  "Bc6d5": 102,
+  "Bc6d5(x)": 103,
+  "Bc6d7": 104,
+  "Bc6e4(x)": 105,
+  "Bc6f3(x)": 106,
+  "Bc8a6": 107,
+  "Bc8b7": 108,
+  "Bc8d7": 109,
+  "Bc8d7(x)": 110,
+  "Bc8e6": 111,
+  "Bc8e6(x)": 112,
+  "Bc8f5": 113,
+  "Bc8f5(x)": 114,
+  "Bc8g4": 115,
+  "Bc8g4(x)": 116,
+  "Bc8h3": 117,
+  "Bc8h3(x)": 118,
+  "Bd2b4": 119,
+  "Bd2b4(x)": 120,
+  "Bd2c1": 121,
+  "Bd2c3": 122,
+  "Bd2c3(x)": 123,
+  "Bd2e1": 124,
+  "Bd2e3": 125,
+  "Bd2f4": 126,
+  "Bd2f4(x)": 127,
+  "Bd2g5": 128,
+  "Bd3a6(x)": 129,
+  "Bd3b1": 130,
+  "Bd3b5": 131,
+  "Bd3b5(x)": 132,
+  "Bd3c2": 133,
+  "Bd3c4": 134,
+  "Bd3c4(x)": 135,
+  "Bd3e2": 136,
+  "Bd3e4": 137,
+  "Bd3e4(x)": 138,
+  "Bd3f1": 139,
+  "Bd3f5": 140,
+  "Bd3f5(x)": 141,
+  "Bd3g6(x)": 142,
+  "Bd3h7(x+)": 143,
+  "Bd4e3": 144,
+  "Bd4f6(x)": 145,
+  "Bd5b3": 146,
+  "Bd5e6": 147,
+  "Bd5f3(x)": 148,
+  "Bd6b4": 149,
+  "Bd6c5": 150,
+  "Bd6c5(x)": 151,
+  "Bd6c7": 152,
+  "Bd6e5": 153,
+  "Bd6e5(x)": 154,
+  "Bd6e7": 155,
+  "Bd6f4": 156,
+  "Bd6f4(x)": 157,
+  "Bd6g3(x)": 158,
+  "Bd6h2(x+)": 159,
+  "Bd7b5": 160,
+  "Bd7b5(x)": 161,
+  "Bd7c6": 162,
+  "Bd7c6(x)": 163,
+  "Bd7c8": 164,
+  "Bd7e6": 165,
+  "Bd7e8": 166,
+  "Bd7f5": 167,
+  "Bd7f5(x)": 168,
+  "Bd7g4": 169,
+  "Be2b5": 170,
+  "Be2c4": 171,
+  "Be2c4(x)": 172,
+  "Be2d1": 173,
+  "Be2d3": 174,
+  "Be2f1": 175,
+  "Be2f3": 176,
+  "Be2f3(x)": 177,
+  "Be2g4": 178,
+  "Be2g4(x)": 179,
+  "Be2h5": 180,
+  "Be2h5(x)": 181,
+  "Be3a7(x)": 182,
+  "Be3b6(x)": 183,
+  "Be3c1": 184,
+  "Be3c5": 185,
+  "Be3c5(x)": 186,
+  "Be3d2": 187,
+  "Be3d4": 188,
+  "Be3d4(x)": 189,
+  "Be3f2": 190,
+  "Be3f4": 191,
+  "Be3f4(x)": 192,
+  "Be3g5": 193,
+  "Be3g5(x)": 194,
+  "Be3h6": 195,
+  "Be3h6(x)": 196,
+  "Be4d3": 197,
+  "Be4f3": 198,
+  "Be4f3(x)": 199,
+  "Be4g6": 200,
+  "Be5d6": 201,
+  "Be5f6": 202,
+  "Be5f6(x)": 203,
+  "Be5g3": 204,
+  "Be5g7": 205,
+  "Be6a2(x)": 206,
+  "Be6b3(x)": 207,
+  "Be6c4": 208,
+  "Be6c4(x)": 209,
+  "Be6d5": 210,
+  "Be6d5(x)": 211,
+  "Be6d7": 212,
+  "Be6f5": 213,
+  "Be6f5(x)": 214,
+  "Be6f7": 215,
+  "Be6g4": 216,
+  "Be6h3(x)": 217,
+  "Be7b4": 218,
+  "Be7c5": 219,
+  "Be7c5(x)": 220,
+  "Be7d6": 221,
+  "Be7d6(x)": 222,
+  "Be7d8": 223,
+  "Be7f6": 224,
+  "Be7f6(x)": 225,
+  "Be7f8": 226,
+  "Be7g5": 227,
+  "Be7g5(x)": 228,
+  "Be7h4": 229,
+  "Be7h4(x)": 230,
+  "Bf1b5": 231,
+  "Bf1b5(+)": 232,
+  "Bf1c4": 233,
+  "Bf1c4(x)": 234,
+  "Bf1d3": 235,
+  "Bf1d3(x)": 236,
+  "Bf1e2": 237,
+  "Bf1g2": 238,
+  "Bf1h3": 239,
+  "Bf3b7(x)": 240,
+  "Bf3c6(x)": 241,
+  "Bf3d5(x)": 242,
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