Chess Challenge submission by Ryn11H

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-Rayan-FATNASSI
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [Ryn11H](https://huggingface.co/Ryn11H)
+- **Parameters**: 878,016
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 5000
+- **Embedding dim**: 96
+- **Layers**: 4
+- **Heads**: 4

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 96,
+  "n_head": 4,
+  "n_inner": 288,
+  "n_layer": 4,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 5000
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33ddf78d2a1fe7ba8601f3ebf5fa4254cf2b64524e98e74c63c00f462a2696dc
+size 3516392

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.
+
+Move-level tokenizer:
+- Each move string is ONE token, e.g. "WPe2e4", "BNg8f6", "WBb5c6(x)".
+
+Key improvement vs baseline:
+- Adds `max_vocab_size` to cap vocabulary size (very important for <1M params).
+- Keeps the TOP-K most frequent moves (after min_frequency filter).
+- Registers for AutoTokenizer so server-side loading works.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from typing import Dict, List, Optional, Tuple
+
+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,
+    ):
+        # Set 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 in kwargs (important when loading)
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+
+        # Load vocab
+        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()
+
+        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 {tok: i for i, tok in enumerate(special_tokens)}
+
+    @classmethod
+    def build_vocab_from_iterator(
+        cls,
+        iterator,
+        min_frequency: int = 1,
+        max_vocab_size: int = 5000,
+    ) -> "ChessTokenizer":
+        """
+        Build vocabulary from an iterator of game strings.
+
+        Strategy:
+        - Count move frequency
+        - Filter by min_frequency
+        - Take TOP-K most frequent moves (K = max_vocab_size - #special_tokens)
+
+        This avoids vocab explosion (which breaks the <1M parameter constraint).
+        """
+        from collections import Counter
+
+        token_counts = Counter()
+        for game in iterator:
+            moves = game.strip().split()
+            token_counts.update(moves)
+
+        # Filter by frequency
+        items = [(tok, c) for tok, c in token_counts.items() if c >= min_frequency]
+
+        # Sort by frequency desc then token for reproducibility
+        items.sort(key=lambda x: (-x[1], x[0]))
+
+        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+        budget = max_vocab_size - len(special_tokens)
+        if budget <= 0:
+            raise ValueError("max_vocab_size must be > number of special tokens")
+
+        top_tokens = [tok for tok, _ in items[:budget]]
+
+        vocab = {tok: i for i, tok in enumerate(special_tokens + top_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,
+        max_vocab_size: int = 5000,
+    ) -> "ChessTokenizer":
+        """
+        Build vocabulary from a HF dataset.
+
+        IMPORTANT:
+        - `max_vocab_size` caps final vocab size (including special tokens).
+        - `min_frequency` filters extremely rare moves before top-k selection.
+        """
+        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,
+            max_vocab_size=max_vocab_size,
+        )
+
+    @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]:
+        return text.strip().split()
+
+    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:
+        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[str]:
+        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,)
+
+
+# IMPORTANT for server-side loading on HF
+ChessTokenizer.register_for_auto_class("AutoTokenizer")

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]"
+}