Chess Challenge submission by marius-785

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess_M15_BOSS
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [marius-785](https://huggingface.co/marius-785)
+- **Parameters**: 996,608
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 83
+- **Embedding dim**: 136
+- **Layers**: 6
+- **Heads**: 4

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "group_size": 4,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 136,
+  "n_head": 4,
+  "n_inner": 408,
+  "n_layer": 6,
+  "pad_token_id": 0,
+  "rms_Norm": true,
+  "tie_weights": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 83
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28031472b193b107cbe444df18629e466987e25d5a1237545784b59b314c2955
+size 3992992

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
+
+
+class FrequencyChessTokenizer(PreTrainedTokenizer):
+    """
+    A frequency-based 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.
+    
+    Only includes moves that appear at least `min_frequency` times in the dataset.
+    Rare moves become [UNK] tokens.
+    
+    Example:
+        >>> tokenizer = FrequencyChessTokenizer()
+        >>> 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,
+    ) -> "FrequencyChessTokenizer":
+        """
+        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 FrequencyChessTokenizer 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,
+    ) -> "FrequencyChessTokenizer":
+        """
+        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 FrequencyChessTokenizer 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,)
+
+
+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)
+
+
+class ChessTokenizer(FrequencyChessTokenizer):
+    """
+    A compositional tokenizer for chess moves using split color/piece tokens.
+    
+    This tokenizer breaks each move into 6 core components with explicit structure:
+    1. Color: W or B (makes turn information explicit!)
+    2. Piece: P, N, B, R, Q, K
+    3. SOURCE marker: [SOURCE]
+    4. Source square: a1, a2, ..., h8
+    5. DEST marker: [DEST]
+    6. Destination square: a1, a2, ..., h8
+    
+    Optional modifier tokens for captures, checks, checkmate, and castling.
+    
+    Example:
+        >>> tokenizer = ChessTokenizer()
+        >>> tokenizer.encode("WPe2e4 BPe7e5")
+        [1, W_id, P_id, SRC_id, e2_id, DST_id, e4_id, B_id, P_id, SRC_id, e7_id, DST_id, e5_id, 2]
+    
+    Vocabulary:
+    - Colors (2): W, B [makes turn alternation explicit]
+    - Pieces (6): P, N, B, R, Q, K
+    - Position markers (2): [SOURCE], [DEST]
+    - Squares (64): a1-h8
+    - Modifiers (5): [CAPTURE], [CHECK], [CHECKMATE], [CASTLING_KS], [CASTLING_QS]
+    - Special (4): [PAD], [BOS], [EOS], [UNK]
+    Total: ~83 tokens (deterministic, 4 fewer than before)
+    
+    Key advantage: Color is now EXPLICIT, making turn alternation obvious to the model!
+    """
+    
+    # Color tokens (split for explicit turn information)
+    COLORS = ['W', 'B']
+    
+    # Piece tokens
+    PIECES = ['P', 'N', 'B', 'R', 'Q', 'K']
+    
+    # Position markers
+    POSITION_MARKERS = ['[SOURCE]', '[DEST]']
+    
+    # Board squares (standard chess notation)
+    SQUARES = [f"{file}{rank}" for rank in range(1, 9) for file in "abcdefgh"]
+    
+    # Move modifiers
+    MODIFIERS = ['[CAPTURE]', '[CHECK]', '[CHECKMATE]', '[CASTLING_KS]', '[CASTLING_QS]']
+    
+    def __init__(self, **kwargs):
+        """
+        Initialize the compositional chess tokenizer.
+        
+        Vocabulary is built deterministically from pieces and squares.
+        No vocab_file or dataset scanning needed.
+        """
+        # Remove vocab-related kwargs to avoid conflicts
+        kwargs.pop("vocab_file", None)
+        kwargs.pop("vocab", None)
+        
+        # Build deterministic vocabulary
+        vocab = self._build_deterministic_vocab()
+        
+        # Initialize parent with the built vocab
+        super().__init__(vocab=vocab, **kwargs)
+    
+    @property
+    def vocab_size(self) -> int:
+        """
+        Return the vocabulary size.
+        
+        Tokens: [PAD]=0, [BOS]=1, [EOS]=2, [UNK]=3, W=4, B=5, P-K=6-11, 
+                 [SOURCE]=12, [DEST]=13, squares=14-77, modifiers=78-82
+        
+        Total: 83 tokens (indices 0-82)
+        """
+        return 4 + 2 + 6 + 2 + 64 + 5  # special + colors + pieces + markers + squares + modifiers
+    
+    def _build_deterministic_vocab(self) -> Dict[str, int]:
+        """
+        Build vocabulary deterministically from colored pieces, squares, and modifiers.
+        
+        Returns:
+            Dictionary mapping token strings to IDs.
+        """
+        vocab = {}
+        idx = 0
+        
+        # Special tokens first (matching parent class order)
+        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
+        for token in special_tokens:
+            vocab[token] = idx
+            idx += 1
+        
+        # Color tokens (W, B)
+        for color in self.COLORS:
+            vocab[color] = idx
+            idx += 1
+        
+        # Piece tokens (P, N, B, R, Q, K)
+        for piece in self.PIECES:
+            vocab[piece] = idx
+            idx += 1
+        
+        # Position marker tokens
+        for marker in self.POSITION_MARKERS:
+            vocab[marker] = idx
+            idx += 1
+        
+        # Square tokens
+        for square in self.SQUARES:
+            vocab[square] = idx
+            idx += 1
+        
+        # Modifier tokens
+        for modifier in self.MODIFIERS:
+            vocab[modifier] = idx
+            idx += 1
+        
+        return vocab
+    
+    def _parse_move(self, move_str: str) -> Dict:
+        """
+        Parse a move string in extended UCI notation.
+        
+        Args:
+            move_str: Move string like "WPe2e4" or "BNg8f6(x)" or "We1g1(o)"
+        
+        Returns:
+            Dictionary with keys: piece, color, src, dest, modifiers
+        """
+        import re
+        
+        # Pattern: [WB][PNBRQK]<square><square>(<modifiers>)
+        pattern = r'([WB])([PNBRQK])([a-h][1-8])([a-h][1-8])((?:\([^)]*\))?)'
+        match = re.match(pattern, move_str.strip())
+        
+        if not match:
+            raise ValueError(f"Invalid move format: {move_str}")
+        
+        color, piece, src, dest, modifier_str = match.groups()
+        
+        # Parse modifiers
+        modifiers = []
+        if modifier_str:
+            # Remove parentheses and split by lowercase letters/symbols
+            mod_content = modifier_str.strip('()')
+            
+            if 'x' in mod_content:
+                modifiers.append('[CAPTURE]')
+            if '+*' in mod_content:
+                modifiers.append('[CHECKMATE]')
+            elif '+' in mod_content:
+                modifiers.append('[CHECK]')
+            if 'o' in mod_content or 'O' in mod_content:
+                # Determine kingside vs queenside based on destination
+                if dest == 'g1' or dest == 'g8':
+                    modifiers.append('[CASTLING_KS]')
+                elif dest == 'c1' or dest == 'c8':
+                    modifiers.append('[CASTLING_QS]')
+        
+        return {
+            'piece': piece,
+            'color': color,
+            'src': src,
+            'dest': dest,
+            'modifiers': modifiers,
+        }
+    
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Tokenize a string of moves into component tokens with positional markers.
+        
+        Each move becomes: [ColoredPiece, [SOURCE], source, [DEST], dest, *modifiers]
+        
+        Args:
+            text: String of space-separated moves (e.g., "WPe2e4 BPe7e5")
+        
+        Returns:
+            List of component tokens with structure markers.
+        """
+        move_strings = text.strip().split()
+        tokens = []
+        
+        for move_str in move_strings:
+            parsed = self._parse_move(move_str)
+            
+            # Add color and piece as SEPARATE tokens (now explicit!)
+            tokens.append(parsed['color'])  # W or B
+            tokens.append(parsed['piece'])  # P, N, B, R, Q, K
+            
+            # Add positional markers and squares
+            tokens.append('[SOURCE]')
+            tokens.append(parsed['src'])
+            tokens.append('[DEST]')
+            tokens.append(parsed['dest'])
+            
+            # Add modifier tokens if any
+            tokens.extend(parsed['modifiers'])
+        
+        return tokens
+    
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """
+        Reconstruct moves from component tokens with positional markers.
+        
+        Expects structure: Color, Piece, [SOURCE], source, [DEST], dest, *modifiers
+        
+        Args:
+            tokens: List of component tokens
+        
+        Returns:
+            Space-separated move string.
+        """
+        moves = []
+        token_idx = 0
+        
+        while token_idx < len(tokens):
+            token = tokens[token_idx]
+            
+            # Skip special tokens
+            special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+            if token in special:
+                token_idx += 1
+                continue
+            
+            # Expect: Color token (W or B)
+            if token not in self.COLORS:
+                break
+            
+            color = token
+            
+            # Expect: Piece token (P, N, B, R, Q, K)
+            if token_idx + 1 >= len(tokens) or tokens[token_idx + 1] not in self.PIECES:
+                break
+            
+            piece = tokens[token_idx + 1]
+            colored_piece = color + piece
+            
+            # Expect: [SOURCE] marker
+            if token_idx + 2 >= len(tokens) or tokens[token_idx + 2] != '[SOURCE]':
+                break
+            
+            # Expect: source square
+            if token_idx + 3 >= len(tokens):
+                break
+            src = tokens[token_idx + 3]
+            if src not in self.SQUARES:
+                break
+            
+            # Expect: [DEST] marker
+            if token_idx + 4 >= len(tokens) or tokens[token_idx + 4] != '[DEST]':
+                break
+            
+            # Expect: dest square
+            if token_idx + 5 >= len(tokens):
+                break
+            dest = tokens[token_idx + 5]
+            if dest not in self.SQUARES:
+                break
+            
+            # Build move string
+            move_str = f"{color}{piece}{src}{dest}"
+            
+            # Collect modifiers (next tokens until we hit another color token or end)
+            token_idx += 6
+            modifiers_list = []
+            
+            while token_idx < len(tokens) and tokens[token_idx] in self.MODIFIERS:
+                modifier = tokens[token_idx]
+                modifiers_list.append(modifier)
+                token_idx += 1
+            
+            # Append modifier suffixes
+            if modifiers_list:
+                modifier_str = ""
+                if '[CAPTURE]' in modifiers_list:
+                    modifier_str += "x"
+                if '[CHECKMATE]' in modifiers_list:
+                    modifier_str += "+*"
+                elif '[CHECK]' in modifiers_list:
+                    modifier_str += "+"
+                if '[CASTLING_KS]' in modifiers_list:
+                    modifier_str += "o"
+                elif '[CASTLING_QS]' in modifiers_list:
+                    modifier_str += "o"
+                
+                move_str += f"({modifier_str})"
+            
+            moves.append(move_str)
+        
+        return " ".join(moves)
+    
+    def decode(self, token_ids, skip_special_tokens=False, **kwargs):
+        """
+        Decode token IDs back to string representation.
+        
+        Properly handles individual tokens by converting each ID to its token string.
+        For single tokens or incomplete move sequences, returns the raw token strings.
+        For complete move sequences, reconstructs the move format.
+        
+        Args:
+            token_ids: List or tensor of token IDs
+            skip_special_tokens: Whether to skip special tokens in output
+            **kwargs: Additional arguments (for compatibility)
+        
+        Returns:
+            String representation of the tokens
+        """
+        # Convert tensor to list if needed
+        if hasattr(token_ids, 'tolist'):
+            token_ids = token_ids.tolist()
+        
+        # Handle 2D tensor/list (batch)
+        if isinstance(token_ids, list) and len(token_ids) > 0 and isinstance(token_ids[0], list):
+            return [self.decode(ids, skip_special_tokens=skip_special_tokens) for ids in token_ids]
+        
+        # Convert IDs to tokens
+        tokens = []
+        for token_id in token_ids:
+            if isinstance(token_id, int):
+                token = self._convert_id_to_token(token_id)
+            else:
+                token = str(token_id)
+            
+            tokens.append(token)
+        
+        # Try to reconstruct moves from tokens
+        # If successful, return the reconstructed moves
+        reconstructed = self._try_reconstruct_moves(tokens, skip_special_tokens)
+        if reconstructed is not None:
+            return reconstructed
+        
+        # Fallback: return tokens joined with spaces, filtering special tokens if requested
+        if skip_special_tokens:
+            special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+            tokens = [t for t in tokens if t not in special]
+        
+        return " ".join(tokens)
+    
+    def _try_reconstruct_moves(self, tokens: List[str], skip_special_tokens: bool = False) -> Optional[str]:
+        """
+        Try to reconstruct complete moves from tokens.
+        
+        Returns the reconstructed move string if tokens form valid move(s),
+        None if tokens don't form a complete move structure.
+        
+        Args:
+            tokens: List of token strings
+            skip_special_tokens: Whether to skip special tokens
+        
+        Returns:
+            Reconstructed move string or None
+        """
+        moves = []
+        token_idx = 0
+        found_moves = False
+        
+        while token_idx < len(tokens):
+            token = tokens[token_idx]
+            
+            # Skip special tokens
+            special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+            if token in special:
+                token_idx += 1
+                continue
+            
+            # Check if this starts a move (color token)
+            if token not in self.COLORS:
+                # No more complete moves
+                break
+            
+            color = token
+            
+            # Need at least 6 more tokens for a complete move
+            if token_idx + 5 >= len(tokens):
+                break
+            
+            # Expect: Piece token (P, N, B, R, Q, K)
+            if tokens[token_idx + 1] not in self.PIECES:
+                break
+            
+            piece = tokens[token_idx + 1]
+            
+            # Expect: [SOURCE] marker
+            if tokens[token_idx + 2] != '[SOURCE]':
+                break
+            
+            # Expect: source square
+            src = tokens[token_idx + 3]
+            if src not in self.SQUARES:
+                break
+            
+            # Expect: [DEST] marker
+            if tokens[token_idx + 4] != '[DEST]':
+                break
+            
+            # Expect: dest square
+            dest = tokens[token_idx + 5]
+            if dest not in self.SQUARES:
+                break
+            
+            # Build move string
+            move_str = f"{color}{piece}{src}{dest}"
+            
+            # Collect modifiers
+            token_idx += 6
+            modifiers_list = []
+            
+            while token_idx < len(tokens) and tokens[token_idx] in self.MODIFIERS:
+                modifiers_list.append(tokens[token_idx])
+                token_idx += 1
+            
+            # Append modifier suffixes
+            if modifiers_list:
+                modifier_str = ""
+                if '[CAPTURE]' in modifiers_list:
+                    modifier_str += "x"
+                if '[CHECKMATE]' in modifiers_list:
+                    modifier_str += "+*"
+                elif '[CHECK]' in modifiers_list:
+                    modifier_str += "+"
+                if '[CASTLING_KS]' in modifiers_list:
+                    modifier_str += "o"
+                elif '[CASTLING_QS]' in modifiers_list:
+                    modifier_str += "o"
+                
+                move_str += f"({modifier_str})"
+            
+            moves.append(move_str)
+            found_moves = True
+        
+        if found_moves:
+            return " ".join(moves)
+        
+        return None
+
+
+class ChessLogitsProcessor:
+    """
+    Logits processor for enforcing chess move structure during generation.
+    
+    Enforces the token sequence pattern:
+    Color Piece [SOURCE] source [DEST] dest [modifiers]*
+    
+    Uses a state machine with 7 states:
+    - State 0: Expect color (W, B)
+    - State 1: Expect piece (P, N, B, R, Q, K)
+    - State 2: Expect [SOURCE] marker
+    - State 3: Expect source square (a1-h8)
+    - State 4: Expect [DEST] marker
+    - State 5: Expect dest square (a1-h8)
+    - State 6: Expect modifiers or next color token
+    
+    Token structure is hardcoded to match ChessTokenizer:
+    - Colors: W, B (EXPLICIT for turn alternation)
+    - Pieces: P, N, B, R, Q, K
+    - Position markers: [SOURCE], [DEST]
+    - Squares: a1-h8 (64 total)
+    - Modifiers: [CAPTURE], [CHECK], [CHECKMATE], [CASTLING_KS], [CASTLING_QS]
+    """
+    
+    # Token vocabulary indices (hardcoded to match ChessTokenizer vocab order)
+    # Special tokens: [PAD]=0, [BOS]=1, [EOS]=2, [UNK]=3
+    # Colors (4-5)
+    COLOR_IDS = {'W': 4, 'B': 5}
+    # Pieces (6-11)
+    PIECE_IDS = {'P': 6, 'N': 7, 'B': 8, 'R': 9, 'Q': 10, 'K': 11}
+    # Position markers (12-13)
+    POSITION_MARKER_IDS = {'[SOURCE]': 12, '[DEST]': 13}
+    # Squares (14-77): a1=14, a2=15, ..., h8=77
+    SQUARE_IDS = {f"{file}{rank}": 14 + (rank - 1) * 8 + ord(file) - ord('a')
+                  for rank in range(1, 9) for file in "abcdefgh"}
+    # Modifiers (78-82)
+    MODIFIER_IDS = {
+        '[CAPTURE]': 78, '[CHECK]': 79, '[CHECKMATE]': 80,
+        '[CASTLING_KS]': 81, '[CASTLING_QS]': 82
+    }
+    
+    def __init__(self):
+        """
+        Initialize the logits processor with hardcoded ChessTokenizer structure.
+        """
+        import torch
+        self.torch = torch
+        
+        # Convert to sets for membership testing
+        self.color_ids = set(self.COLOR_IDS.values())
+        self.piece_ids = set(self.PIECE_IDS.values())
+        self.square_ids = set(self.SQUARE_IDS.values())
+        self.modifier_ids = set(self.MODIFIER_IDS.values())
+    
+    def _get_state(self, input_ids):
+        """
+        Determine current state in move sequence based on recent tokens.
+        
+        Returns state (0-6) indicating what token type is expected next.
+        """
+        if input_ids.numel() == 0:
+            return 0  # Start: expect color
+        
+        # Get the sequence of tokens
+        seq = input_ids[0].tolist()
+        
+        # Work backwards to find the last color token (marks start of move)
+        last_move_idx = -1
+        for i in range(len(seq) - 1, -1, -1):
+            if seq[i] in self.color_ids:
+                last_move_idx = i
+                break
+        
+        if last_move_idx == -1:
+            return 0  # No color found, expect color
+        
+        # Count tokens since last color
+        tokens_since_color = len(seq) - 1 - last_move_idx
+        
+        # Pattern: Color, Piece, [SOURCE], source, [DEST], dest, ...modifiers
+        if tokens_since_color == 0:
+            return 1  # Expect piece after color
+        elif tokens_since_color == 1:
+            # Should have: color, piece
+            if seq[-1] in self.piece_ids:
+                return 2  # Expect [SOURCE]
+            else:
+                return 1  # Unexpected, reset
+        elif tokens_since_color == 2:
+            # Should have: color, piece, [SOURCE]
+            if (seq[-2] in self.piece_ids and 
+                seq[-1] in [self.POSITION_MARKER_IDS['[SOURCE]']]):
+                return 3  # Expect source square
+            else:
+                return 1  # Reset
+        elif tokens_since_color == 3:
+            # Should have: color, piece, [SOURCE], source
+            if (seq[-3] in self.piece_ids and
+                seq[-2] in [self.POSITION_MARKER_IDS['[SOURCE]']] and
+                seq[-1] in self.square_ids):
+                return 4  # Expect [DEST]
+            else:
+                return 1  # Reset
+        elif tokens_since_color == 4:
+            # Should have: color, piece, [SOURCE], source, [DEST]
+            if (seq[-2] in self.square_ids and
+                seq[-1] in [self.POSITION_MARKER_IDS['[DEST]']]):
+                return 5  # Expect dest square
+            else:
+                return 1  # Reset
+        elif tokens_since_color == 5:
+            # Should have: color, piece, [SOURCE], source, [DEST], dest
+            if seq[-1] in self.square_ids:
+                return 6  # Expect modifiers or next color (move complete)
+            else:
+                return 1  # Reset
+        else:
+            # tokens_since_color >= 6: We're in modifiers or expecting next move
+            # If last token is a modifier, still expect more modifiers or next color
+            # If last token is not a modifier, we should expect next color
+            if seq[-1] not in self.modifier_ids:
+                return 0  # Expect next move (next color)
+            else:
+                return 6  # Could be more modifiers or next color
+    
+    def constrain_logits(self, input_ids, logits):
+        """
+        Mask invalid tokens in logits based on move structure.
+        
+        Sets logits to -inf for tokens that violate move structure.
+        
+        Args:
+            input_ids: Model input token IDs of shape (batch_size, seq_len)
+            logits: Model output logits of shape (batch_size, vocab_size)
+        
+        Returns:
+            Modified logits with invalid tokens masked to -inf
+        """
+        state = self._get_state(input_ids)
+        
+        # Create a mask for valid tokens (all ones initially)
+        valid_mask = self.torch.ones(logits.shape[-1], dtype=self.torch.bool)
+        valid_mask[:] = False  # Start by forbidding all
+        
+        # Allow tokens based on current state
+        if state == 0:
+            # Expect color (W or B)
+            for color_id in self.color_ids:
+                valid_mask[color_id] = True
+        
+        elif state == 1:
+            # Expect piece (P, N, B, R, Q, K)
+            for piece_id in self.piece_ids:
+                valid_mask[piece_id] = True
+        
+        elif state == 2:
+            # Expect [SOURCE]
+            valid_mask[self.POSITION_MARKER_IDS['[SOURCE]']] = True
+        
+        elif state == 3:
+            # Expect source square
+            for square_id in self.square_ids:
+                valid_mask[square_id] = True
+        
+        elif state == 4:
+            # Expect [DEST]
+            valid_mask[self.POSITION_MARKER_IDS['[DEST]']] = True
+        
+        elif state == 5:
+            # Expect dest square
+            for square_id in self.square_ids:
+                valid_mask[square_id] = True
+        
+        elif state == 6:
+            # Expect modifiers or next color token
+            # Allow: modifiers + colors + EOS
+            for modifier_id in self.modifier_ids:
+                valid_mask[modifier_id] = True
+            for color_id in self.color_ids:
+                valid_mask[color_id] = True
+            valid_mask[2] = True  # Allow EOS to end sequence
+        
+        # Apply mask
+        logits = logits.clone()
+        logits[0, ~valid_mask] = float('-inf')
+        
+        return logits
+

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 +1,84 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "W": 4,
+  "B": 8,
+  "P": 6,
+  "N": 7,
+  "R": 9,
+  "Q": 10,
+  "K": 11,
+  "[SOURCE]": 12,
+  "[DEST]": 13,
+  "a1": 14,
+  "b1": 15,
+  "c1": 16,
+  "d1": 17,
+  "e1": 18,
+  "f1": 19,
+  "g1": 20,
+  "h1": 21,
+  "a2": 22,
+  "b2": 23,
+  "c2": 24,
+  "d2": 25,
+  "e2": 26,
+  "f2": 27,
+  "g2": 28,
+  "h2": 29,
+  "a3": 30,
+  "b3": 31,
+  "c3": 32,
+  "d3": 33,
+  "e3": 34,
+  "f3": 35,
+  "g3": 36,
+  "h3": 37,
+  "a4": 38,
+  "b4": 39,
+  "c4": 40,
+  "d4": 41,
+  "e4": 42,
+  "f4": 43,
+  "g4": 44,
+  "h4": 45,
+  "a5": 46,
+  "b5": 47,
+  "c5": 48,
+  "d5": 49,
+  "e5": 50,
+  "f5": 51,
+  "g5": 52,
+  "h5": 53,
+  "a6": 54,
+  "b6": 55,
+  "c6": 56,
+  "d6": 57,
+  "e6": 58,
+  "f6": 59,
+  "g6": 60,
+  "h6": 61,
+  "a7": 62,
+  "b7": 63,
+  "c7": 64,
+  "d7": 65,
+  "e7": 66,
+  "f7": 67,
+  "g7": 68,
+  "h7": 69,
+  "a8": 70,
+  "b8": 71,
+  "c8": 72,
+  "d8": 73,
+  "e8": 74,
+  "f8": 75,
+  "g8": 76,
+  "h8": 77,
+  "[CAPTURE]": 78,
+  "[CHECK]": 79,
+  "[CHECKMATE]": 80,
+  "[CASTLING_KS]": 81,
+  "[CASTLING_QS]": 82
+}