Chess Challenge submission by minhdc

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-duc-v2
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [minhdc](https://huggingface.co/minhdc)
+- **Parameters**: 996,976
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 68
+- **Embedding dim**: 128
+- **Layers**: 6
+- **Heads**: 8

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.05,
+  "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": 360,
+  "n_layer": 6,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 68
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f66fca9e2271541c813f639110a30971ce357a71c6d695d7bffca3d0d2673add
+size 3994352

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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+"""
+Optimized Chess Tokenizer using pure UCI notation.
+
+This achieves ~84 vocab size by:
+1. Using only squares (a1-h8) and promotion pieces (q,r,b,n)
+2. Decomposing moves into from_square, to_square, (optional) promotion
+3. No piece types, no color, no annotations
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+
+
+class ChessTokenizer(PreTrainedTokenizer):
+    
+    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
+
+        # Remove duplicates from kwargs
+        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:
+            self._vocab = self._create_default_vocab()
+        
+        # Create reverse mapping
+        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]:
+        """
+        Create vocabulary with all possible squares and promotion pieces.
+        This ensures deterministic vocab size of exactly 72 tokens.
+        """
+        tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
+        
+        # All squares a1-h8
+        for file in 'abcdefgh':
+            for rank in '12345678':
+                tokens.append(f"{file}{rank}")
+        
+        # Promotion pieces (lowercase for UCI)
+        tokens.extend(['q', 'r', 'b', 'n'])
+        
+        vocab = {token: idx for idx, token in enumerate(tokens)}
+        return 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,
+        max_samples: Optional[int] = 100000,
+    ) -> "ChessTokenizer":
+        """
+        Build tokenizer from dataset by converting to UCI format.
+        
+        This will create a vocabulary of ~72-84 tokens.
+        """
+        from datasets import load_dataset
+        from collections import Counter
+        
+        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()
+        
+        # Process games and extract UCI components
+        for example in dataset:
+            moves = example[column].strip().split()
+            for move in moves:
+                # Convert extended UCI to decomposed UCI
+                uci_tokens = cls._extended_to_uci_tokens(move)
+                token_counts.update(uci_tokens)
+        
+        # Filter by frequency
+        tokens = [
+            token for token, count in token_counts.items()
+            if count >= min_frequency
+        ]
+        
+        # Sort for reproducibility
+        tokens = sorted(set(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)
+    
+    @staticmethod
+    def _extended_to_uci_tokens(move: str) -> List[str]:
+        """
+        Convert extended UCI format to decomposed UCI tokens.
+        
+        Input: "WPe2e4" or "BQd8h4(x+)" or "WPe7e8=Q"
+        Output: ["e2", "e4"] or ["d8", "h4"] or ["e7", "e8", "q"]
+        """
+        if len(move) < 6:
+            return []
+        
+        # Extract squares (positions 2-6)
+        from_sq = move[2:4]
+        to_sq = move[4:6]
+        
+        tokens = [from_sq, to_sq]
+        
+        # Check for promotion
+        if "=" in move:
+            promo_idx = move.index("=")
+            if promo_idx + 1 < len(move):
+                promo = move[promo_idx + 1].lower()
+                if promo in 'qrbn':
+                    tokens.append(promo)
+        
+        return tokens
+    
+    @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 a string of moves.
+        
+        Input can be either:
+        - Extended UCI: "WPe2e4 BPe7e5"
+        - Decomposed UCI: "e2 e4 e7 e5"
+        """
+        tokens = text.strip().split()
+        
+        # If tokens look like extended UCI (start with W/B and piece letter)
+        # convert them to decomposed format
+        result = []
+        for token in tokens:
+            if len(token) >= 6 and token[0] in 'WB' and token[1] in 'PNBRQK':
+                # Extended format - decompose it
+                result.extend(self._extended_to_uci_tokens(token))
+            else:
+                # Already in simple format or is a square/promotion
+                result.append(token)
+        
+        return result
+    
+    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 (space-separated)."""
+        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:
+        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,)

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