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chess-llm-MaximeMuh2

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MaximeMuhlethaler commited on
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1 Parent(s): 7efa8aa

Chess Challenge submission by MaximeMuhlethaler

Browse files
Files changed (6) hide show
  1. README.md +2 -2
  2. config.json +2 -2
  3. model.safetensors +2 -2
  4. tokenizer.py +111 -0
  5. tokenizer_config.json +3 -11
  6. vocab.json +1599 -1099
README.md CHANGED
@@ -14,13 +14,13 @@ Chess model submitted to the LLM Course Chess Challenge.
14
  ## Submission Info
15
 
16
  - **Submitted by**: [MaximeMuhlethaler](https://huggingface.co/MaximeMuhlethaler)
17
- - **Parameters**: 924,000
18
  - **Organization**: LLM-course
19
 
20
  ## Model Details
21
 
22
  - **Architecture**: Chess Transformer (GPT-style)
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- - **Vocab size**: 1200
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  - **Embedding dim**: 112
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  - **Layers**: 6
26
  - **Heads**: 8
 
14
  ## Submission Info
15
 
16
  - **Submitted by**: [MaximeMuhlethaler](https://huggingface.co/MaximeMuhlethaler)
17
+ - **Parameters**: 980,000
18
  - **Organization**: LLM-course
19
 
20
  ## Model Details
21
 
22
  - **Architecture**: Chess Transformer (GPT-style)
23
+ - **Vocab size**: 1700
24
  - **Embedding dim**: 112
25
  - **Layers**: 6
26
  - **Heads**: 8
config.json CHANGED
@@ -15,6 +15,6 @@
15
  "n_layer": 6,
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  "pad_token_id": 0,
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  "tie_weights": true,
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- "transformers_version": "4.57.3",
19
- "vocab_size": 1200
20
  }
 
15
  "n_layer": 6,
16
  "pad_token_id": 0,
17
  "tie_weights": true,
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+ "transformers_version": "4.57.5",
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+ "vocab_size": 1700
20
  }
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:d9de7e6548178446130b8f76b2cbd82b98c64822ee046afefb208b0e897e8da3
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- size 3702448
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:af8e8f65533da44492a5e300e16e8453750103cebe7c2e014c7410089b082452
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+ size 3926448
tokenizer.py ADDED
@@ -0,0 +1,111 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Custom Chess Tokenizer - Final Fix
3
+ """
4
+ from __future__ import annotations
5
+ import json
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+ import os
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+ import shutil
8
+ import re
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+ from typing import Dict, List, Optional
10
+ from transformers import PreTrainedTokenizer
11
+
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+ # --- REGEX (Pour nettoyer les coups) ---
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+ MOVE_RE = re.compile(r"([a-h][1-8])([a-h][1-8])")
14
+ PROMO_RE = re.compile(r"=([NBRQ])")
15
+
16
+ def normalize_move(tok: str) -> str:
17
+ if tok.startswith("["): return tok
18
+ m = MOVE_RE.search(tok)
19
+ if not m: return tok
20
+ fr, to = m.group(1), m.group(2)
21
+ promo = ""
22
+ pm = PROMO_RE.search(tok)
23
+ if pm: promo = "=" + pm.group(1)
24
+ prefix = tok[:2] if len(tok) >= 2 else "WP"
25
+ return f"{prefix}{fr}{to}{promo}"
26
+
27
+ class ChessTokenizer(PreTrainedTokenizer):
28
+ model_input_names = ["input_ids", "attention_mask"]
29
+ vocab_files_names = {"vocab_file": "vocab.json"}
30
+
31
+ PAD_TOKEN = "[PAD]"
32
+ BOS_TOKEN = "[BOS]"
33
+ EOS_TOKEN = "[EOS]"
34
+ UNK_TOKEN = "[UNK]"
35
+
36
+ def __init__(self, vocab_file=None, vocab=None, **kwargs):
37
+ self._pad_token = self.PAD_TOKEN
38
+ self._bos_token = self.BOS_TOKEN
39
+ self._eos_token = self.EOS_TOKEN
40
+ self._unk_token = self.UNK_TOKEN
41
+
42
+ for t in ["pad_token", "bos_token", "eos_token", "unk_token"]: kwargs.pop(t, None)
43
+
44
+ # FIX CHEMIN
45
+ if vocab is None:
46
+ if vocab_file is None:
47
+ vocab_file = os.path.join(os.path.dirname(__file__), "vocab.json")
48
+ self.vocab_file = vocab_file
49
+ if os.path.exists(vocab_file):
50
+ with open(vocab_file, "r", encoding="utf-8") as f: self._vocab = json.load(f)
51
+ else: self._vocab = self._create_default_vocab()
52
+ else:
53
+ self._vocab = vocab
54
+ self.vocab_file = vocab_file
55
+
56
+ self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
57
+ super().__init__(pad_token=self.PAD_TOKEN, bos_token=self.BOS_TOKEN, eos_token=self.EOS_TOKEN, unk_token=self.UNK_TOKEN, **kwargs)
58
+
59
+ # AUTO-COPIE (Vital pour le submit)
60
+ def save_pretrained(self, save_directory: str, **kwargs):
61
+ super().save_pretrained(save_directory, **kwargs)
62
+ src_path = os.path.abspath(__file__)
63
+ dst_path = os.path.join(save_directory, "tokenizer.py")
64
+ if src_path != dst_path: shutil.copy(src_path, dst_path)
65
+
66
+ config_path = os.path.join(save_directory, "tokenizer_config.json")
67
+ if os.path.exists(config_path):
68
+ with open(config_path, "r") as f: cfg = json.load(f)
69
+ cfg["auto_map"] = {"AutoTokenizer": "tokenizer.ChessTokenizer"}
70
+ with open(config_path, "w") as f: json.dump(cfg, f, indent=2)
71
+
72
+ def _create_default_vocab(self):
73
+ return {t: i for i, t in enumerate([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])}
74
+
75
+ # LA FONCTION QUI GERE LA TAILLE FIXE
76
+ @classmethod
77
+ def build_vocab_from_dataset(cls, dataset_name, split="train", column="text", min_frequency=2, max_vocab_size=1700, max_samples=100000):
78
+ from datasets import load_dataset
79
+ from collections import Counter
80
+
81
+ ds = load_dataset(dataset_name, split=split, streaming=True)
82
+ ds = ds.take(max_samples)
83
+
84
+ counter = Counter()
85
+ for ex in ds:
86
+ # On normalise
87
+ moves = [normalize_move(t) for t in ex[column].split()]
88
+ counter.update(moves)
89
+
90
+ # ON FORCE LA TAILLE MAXIMALE ICI
91
+ special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
92
+ # On prend les N plus fréquents pour remplir jusqu'à max_vocab_size
93
+ most_common = counter.most_common(max_vocab_size - len(special))
94
+
95
+ vocab = {t: i for i, t in enumerate(special + [t for t, c in most_common])}
96
+ return cls(vocab=vocab)
97
+
98
+ @property
99
+ def vocab_size(self): return len(self._vocab)
100
+ def get_vocab(self): return dict(self._vocab)
101
+
102
+ def _tokenize(self, text): return [normalize_move(t) for t in text.strip().split()]
103
+ def _convert_token_to_id(self, token): return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN))
104
+ def _convert_id_to_token(self, index): return self._ids_to_tokens.get(index, self.UNK_TOKEN)
105
+ def convert_tokens_to_string(self, tokens): return " ".join(t for t in tokens if t not in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])
106
+
107
+ def save_vocabulary(self, save_directory, filename_prefix=None):
108
+ if not os.path.isdir(save_directory): os.makedirs(save_directory, exist_ok=True)
109
+ path = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.json")
110
+ with open(path, "w", encoding="utf-8") as f: json.dump(self._vocab, f, ensure_ascii=False, indent=2)
111
+ return (path,)
tokenizer_config.json CHANGED
@@ -34,22 +34,14 @@
34
  }
35
  },
36
  "auto_map": {
37
- "AutoTokenizer": [
38
- "tokenizer.ChessTokenizer",
39
- null
40
- ]
41
  },
42
  "bos_token": "[BOS]",
43
  "clean_up_tokenization_spaces": false,
44
  "eos_token": "[EOS]",
45
  "extra_special_tokens": {},
46
- "model_max_length": 256,
47
  "pad_token": "[PAD]",
48
  "tokenizer_class": "ChessTokenizer",
49
- "unk_token": "[UNK]",
50
- "vocab_files_names": {
51
- "vocab_file": "vocab.json"
52
- },
53
- "vocab_size": 1200,
54
- "vocab_file": "vocab.json"
55
  }
 
34
  }
35
  },
36
  "auto_map": {
37
+ "AutoTokenizer": "tokenizer.ChessTokenizer"
 
 
 
38
  },
39
  "bos_token": "[BOS]",
40
  "clean_up_tokenization_spaces": false,
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  "eos_token": "[EOS]",
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  "extra_special_tokens": {},
43
+ "model_max_length": 1000000000000000019884624838656,
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  "pad_token": "[PAD]",
45
  "tokenizer_class": "ChessTokenizer",
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+ "unk_token": "[UNK]"
 
 
 
 
 
47
  }
vocab.json CHANGED
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1202
  }
 
15
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+ "WKf5f6": 1622,
1625
+ "WKc3d2": 1623,
1626
+ "WKb3a4": 1624,
1627
+ "BKh5g4": 1625,
1628
+ "BRg8g4": 1626,
1629
+ "WNd5b4": 1627,
1630
+ "WRd5d7": 1628,
1631
+ "WQb3b4": 1629,
1632
+ "WKd4e4": 1630,
1633
+ "BRe8h8": 1631,
1634
+ "WBe3c1": 1632,
1635
+ "BRd2d1": 1633,
1636
+ "BKb6c7": 1634,
1637
+ "BBh4g5": 1635,
1638
+ "BKg4g3": 1636,
1639
+ "WBa3c5": 1637,
1640
+ "BBe4g6": 1638,
1641
+ "BKd5c6": 1639,
1642
+ "WRf2g2": 1640,
1643
+ "BRb1b2": 1641,
1644
+ "WBa3b2": 1642,
1645
+ "BKe4d3": 1643,
1646
+ "WBe4b7": 1644,
1647
+ "WNd6c8": 1645,
1648
+ "BKd5e5": 1646,
1649
+ "WRg1g5": 1647,
1650
+ "WNb3a5": 1648,
1651
+ "BNc4a3": 1649,
1652
+ "WNe2c1": 1650,
1653
+ "BRe6f6": 1651,
1654
+ "BRa4a3": 1652,
1655
+ "WKb2c2": 1653,
1656
+ "BQf6d6": 1654,
1657
+ "BQd7e8": 1655,
1658
+ "BBb6e3": 1656,
1659
+ "BBg7h8": 1657,
1660
+ "BRd1d2": 1658,
1661
+ "BKh5g6": 1659,
1662
+ "BNf4g2": 1660,
1663
+ "WRd7f7": 1661,
1664
+ "BRd2f2": 1662,
1665
+ "WQh5e2": 1663,
1666
+ "WRe7b7": 1664,
1667
+ "WNd6c4": 1665,
1668
+ "WQe4e5": 1666,
1669
+ "WQf3c6": 1667,
1670
+ "BKb6c6": 1668,
1671
+ "WQb7a7": 1669,
1672
+ "WNb7c5": 1670,
1673
+ "WQb3d1": 1671,
1674
+ "BBe7a3": 1672,
1675
+ "WRe5e7": 1673,
1676
+ "BQd6c7": 1674,
1677
+ "BQe6g6": 1675,
1678
+ "BKd4c3": 1676,
1679
+ "BRc1c2": 1677,
1680
+ "WRg3f3": 1678,
1681
+ "WQd3b3": 1679,
1682
+ "BBd4f2": 1680,
1683
+ "WQh6h7": 1681,
1684
+ "WQg4g7": 1682,
1685
+ "BKc5d6": 1683,
1686
+ "WKg4g3": 1684,
1687
+ "BNc7e6": 1685,
1688
+ "BRd4d2": 1686,
1689
+ "WRg1g6": 1687,
1690
+ "BBg6h7": 1688,
1691
+ "BQc7b8": 1689,
1692
+ "BNc7d5": 1690,
1693
+ "BNc2e3": 1691,
1694
+ "BKc7c8": 1692,
1695
+ "BRe2b2": 1693,
1696
+ "BQb6d6": 1694,
1697
+ "WNd2b1": 1695,
1698
+ "BRc7d7": 1696,
1699
+ "BQe6f6": 1697,
1700
+ "BNd8e6": 1698,
1701
+ "WKe4e3": 1699
1702
  }