Chess Challenge submission by matheoqtb

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-model-qtb-v2
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [matheoqtb](https://huggingface.co/matheoqtb)
+- **Parameters**: 970,112
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 76
+- **Embedding dim**: 128
+- **Layers**: 7
+- **Heads**: 8

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": 128,
+  "n_head": 8,
+  "n_inner": 256,
+  "n_layer": 7,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 76
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:52798539a84c71a108bff0c1889c6f4160dffe4349de3385e590980bb1b8b733
+size 3887912

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 chess moves using a 'Square-Aware' Character strategy.
+Instead of full moves (e.g., WPe2e4), it splits them into meaningful atomic parts:
+- Pieces/Colors: W, B, P, N, B, R, Q, K
+- Full Squares: e2, e4, h8 (keeps coordinates together for geometric understanding)
+- Separators: Space " "
+
+Example: "WPe2e4" -> ["W", "P", "e2", "e4"]
+"""
+
+from __future__ import annotations
+import re
+import json
+import os
+from typing import Dict, List, Optional
+from transformers import PreTrainedTokenizer
+
+class ChessTokenizer(PreTrainedTokenizer):
+    """
+    A custom tokenizer for chess moves using 'Square-Aware' tokenization.
+    
+    It maps atomic chess components (squares like 'e4', pieces like 'P') to IDs.
+    This creates a small, dense vocabulary (~80 tokens) allowing deeper models.
+    """
+    
+    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 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
+
+        # Clean kwargs to avoid conflicts
+        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:
+            # Minimal default vocab (placeholder)
+            self._vocab = self._create_default_vocab()
+        
+        # Create reverse mapping (ID -> Token)
+        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,
+        )
+    
+    def _create_default_vocab(self) -> Dict[str, int]:
+        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
+        return {token: idx for idx, token in enumerate(special_tokens)}
+    
+    @classmethod
+    def build_vocab_from_iterator(
+        cls,
+        iterator,
+        min_frequency: int = 1,
+    ) -> "ChessTokenizer":
+        """
+        Build vocabulary by scanning the dataset.
+        Splits text into pieces (W, P) and full squares (e2, e4).
+        """
+        from collections import Counter
+        token_counts = Counter()
+        
+        for game in iterator:
+            # 1. Nettoyage : on enlève les suffixes (x), (+)
+            game = re.sub(r'\(.*?\)', '', game)
+            
+            # 2. Découpage par coups pour gérer les espaces correctement
+            moves = game.strip().split()
+            
+            for i, move in enumerate(moves):
+                # Regex : Capture soit une case [a-h][1-8], soit n'importe quel autre char (.)
+                tokens = re.findall(r'[a-h][1-8]|.', move)
+                token_counts.update(tokens)
+                
+                # Ajout explicite de l'espace entre les coups (sauf après le dernier)
+                if i < len(moves) - 1:
+                    token_counts.update([" "])
+        
+        # Filter and sort tokens
+        tokens = [t for t, count in token_counts.items() if count >= min_frequency]
+        tokens = sorted(tokens)
+        
+        # Build final vocabulary dict
+        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 = 1,  # Keep at 1 to catch all squares/pieces
+        max_samples: Optional[int] = 50000,
+    ) -> "ChessTokenizer":
+        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 len(self._vocab)
+    
+    def get_vocab(self) -> Dict[str, int]:
+        return dict(self._vocab)
+
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Tokenize input text using the Square-Aware logic.
+        "WPe2e4" -> ["W", "P", "e2", "e4"]
+        """
+        # 1. Remove suffixes
+        text = re.sub(r'\(.*?\)', '', text)
+        
+        # 2. Split into moves to manage spaces
+        moves = text.strip().split()
+        
+        all_tokens = []
+        for i, move in enumerate(moves):
+            # Regex match: squares OR single chars
+            tokens = re.findall(r'[a-h][1-8]|.', move)
+            all_tokens.extend(tokens)
+            
+            # Re-insert space token between moves
+            if i < len(moves) - 1:
+                all_tokens.append(" ")
+                
+        return all_tokens
+        
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
+    
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+    
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """
+        Convert tokens back to string.
+        IMPORTANT: Join with empty string "" because space " " is already a token.
+        """
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+        filtered_tokens = [t for t in tokens if t not in special]
+        
+        # Join with "" because the space character is treated as a token in our vocab
+        return "".join(filtered_tokens)
+    
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
+        if not os.path.isdir(save_directory):
+            os.makedirs(save_directory, exist_ok=True)
+        
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
+        )
+        
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
+        
+        return (vocab_file,)
+
+def count_vocab_from_dataset(
+    dataset_name: str = "dlouapre/lichess_2025-01_1M",
+    split: str = "train",
+    column: str = "text",
+    max_samples: Optional[int] = 10000,
+) -> Dict[str, int]:
+    # Utility function remains similar but should use the new regex logic if needed for analysis
+    # For simple counting, split() is often enough approximation, but let's be precise:
+    from collections import Counter
+    from datasets import load_dataset
+    import re
+    
+    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:
+        text = re.sub(r'\(.*?\)', '', example[column])
+        moves = text.strip().split()
+        for move in moves:
+            tokens = re.findall(r'[a-h][1-8]|.', move)
+            token_counts.update(tokens)
+            token_counts.update([" "])
+            
+    return dict(token_counts)

tokenizer_config.json

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

vocab.json

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+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  " ": 4,
+  "B": 5,
+  "K": 6,
+  "N": 7,
+  "P": 8,
+  "Q": 9,
+  "R": 10,
+  "W": 11,
+  "a1": 12,
+  "a2": 13,
+  "a3": 14,
+  "a4": 15,
+  "a5": 16,
+  "a6": 17,
+  "a7": 18,
+  "a8": 19,
+  "b1": 20,
+  "b2": 21,
+  "b3": 22,
+  "b4": 23,
+  "b5": 24,
+  "b6": 25,
+  "b7": 26,
+  "b8": 27,
+  "c1": 28,
+  "c2": 29,
+  "c3": 30,
+  "c4": 31,
+  "c5": 32,
+  "c6": 33,
+  "c7": 34,
+  "c8": 35,
+  "d1": 36,
+  "d2": 37,
+  "d3": 38,
+  "d4": 39,
+  "d5": 40,
+  "d6": 41,
+  "d7": 42,
+  "d8": 43,
+  "e1": 44,
+  "e2": 45,
+  "e3": 46,
+  "e4": 47,
+  "e5": 48,
+  "e6": 49,
+  "e7": 50,
+  "e8": 51,
+  "f1": 52,
+  "f2": 53,
+  "f3": 54,
+  "f4": 55,
+  "f5": 56,
+  "f6": 57,
+  "f7": 58,
+  "f8": 59,
+  "g1": 60,
+  "g2": 61,
+  "g3": 62,
+  "g4": 63,
+  "g5": 64,
+  "g6": 65,
+  "g7": 66,
+  "g8": 67,
+  "h1": 68,
+  "h2": 69,
+  "h3": 70,
+  "h4": 71,
+  "h5": 72,
+  "h6": 73,
+  "h7": 74,
+  "h8": 75
+}