Submission V2 (Course Copy)

Browse files

Files changed (6) hide show

README.md +2 -2
config.json +1 -1
model.py +0 -1
pytorch_model.bin +2 -2
train.py +9 -9
vocab.json +309 -1196

README.md CHANGED Viewed

@@ -10,12 +10,12 @@ license: mit
 ## Submission Info
 - **Submitted by:** MDaytek
-- **Parameters:** 979,712
 - **Organization:** LLM-course
 ## Model Details
 - **Architecture:** Chess Transformer (Custom)
-- **Vocab size:** 1200
 - **Embedding dim:** 128
 - **Layers:** 4
 - **Heads:** 4

 ## Submission Info
 - **Submitted by:** MDaytek
+- **Parameters:** 866,176
 - **Organization:** LLM-course
 ## Model Details
 - **Architecture:** Chess Transformer (Custom)
+- **Vocab size:** 313
 - **Embedding dim:** 128
 - **Layers:** 4
 - **Heads:** 4

config.json CHANGED Viewed

@@ -10,7 +10,7 @@
     "n_layer": 4,
     "n_positions": 256,
     "transformers_version": "4.57.3",
-    "vocab_size": 1200,
     "auto_map": {
         "AutoConfig": "model.ChessConfig",
         "AutoModelForCausalLM": "model.ChessForCausalLM"

     "n_layer": 4,
     "n_positions": 256,
     "transformers_version": "4.57.3",
+    "vocab_size": 313,
     "auto_map": {
         "AutoConfig": "model.ChessConfig",
         "AutoModelForCausalLM": "model.ChessForCausalLM"

model.py CHANGED Viewed

@@ -68,5 +68,4 @@ class ChessForCausalLM(PreTrainedModel):
         return CausalLMOutputWithPast(loss=loss, logits=logits)
 def print_parameter_budget(config):
-    # Cette fonction est maintenant gérée dynamiquement dans train.py
     pass

         return CausalLMOutputWithPast(loss=loss, logits=logits)
 def print_parameter_budget(config):
     pass

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:03ab19f8da39e37ae955700e749003d0b15671d2f9d805647800d1f3e7bddd03
-size 3935651

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+oid sha256:a32d55c14d5b479a60d60c2ba42b56685bce5cb5728a0ece72a019d8c6f8fa04
+size 3481507

train.py CHANGED Viewed

@@ -7,7 +7,7 @@ from transformers import Trainer, TrainingArguments, DataCollatorForLanguageMode
 from src.tokenizer import ChessTokenizer
 from src.model import ChessConfig, ChessForCausalLM
-# Configuration globale pour la réutiliser dans le README
 ORG_NAME = "LLM-course"
 MY_PSEUDO = "MDaytek"
@@ -31,13 +31,13 @@ def encode_game(game, tokenizer, max_len=256):
 def main():
     print("Loading 5000 games dataset...")
-    raw_ds = load_dataset("dlouapre/lichess_2025-01_1M", split="train[:5000]")
     print("Building tokenizer...")
     vocab = build_vocab(raw_ds)
     tokenizer = ChessTokenizer(vocab)
-    # Configuration des hyperparamètres
     N_EMBD = 128
     N_LAYER = 4
     N_HEAD = 4
@@ -51,9 +51,9 @@ def main():
     )
     model = ChessForCausalLM(config)
-    # --- CALCUL PRÉCIS DES PARAMÈTRES ---
     total_params = sum(p.numel() for p in model.parameters())
-    print(f"📊 Nombre total de paramètres calculé : {total_params:,}")
     print("Tokenizing dataset...")
     input_ids = [encode_game(g, tokenizer) for g in raw_ds]
@@ -64,7 +64,7 @@ def main():
     args = TrainingArguments(
         output_dir=train_output,
         per_device_train_batch_size=32,
-        num_train_epochs=5,
         logging_steps=50,
         save_strategy="no",
         report_to="none",
@@ -78,7 +78,7 @@ def main():
         data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
     )
-    print("🚀 Starting FULL training (5k games, 5 epochs)...")
     trainer.train()
     print("💾 Saving model locally...")
@@ -86,7 +86,7 @@ def main():
     model.save_pretrained(final_path, safe_serialization=False)
     tokenizer.save_pretrained(final_path)
-    # --- GÉNÉRATION DU README PRO ---
     readme_content = f"""---
 library_name: transformers
 tags:
@@ -112,7 +112,7 @@ license: mit
     with open(os.path.join(final_path, "README.md"), "w") as f:
         f.write(readme_content)
-    print("✅ Training complete & README generated.")
 if __name__ == "__main__":
     main()

 from src.tokenizer import ChessTokenizer
 from src.model import ChessConfig, ChessForCausalLM
+# Infos pour le README
 ORG_NAME = "LLM-course"
 MY_PSEUDO = "MDaytek"
 def main():
     print("Loading 5000 games dataset...")
+    raw_ds = load_dataset("dlouapre/lichess_2025-01_1M", split="train[:5]")
     print("Building tokenizer...")
     vocab = build_vocab(raw_ds)
     tokenizer = ChessTokenizer(vocab)
+    # Paramètres du modèle
     N_EMBD = 128
     N_LAYER = 4
     N_HEAD = 4
     )
     model = ChessForCausalLM(config)
+    # Calcul des paramètres
     total_params = sum(p.numel() for p in model.parameters())
+    print(f"📊 Paramètres totaux : {total_params:,}")
     print("Tokenizing dataset...")
     input_ids = [encode_game(g, tokenizer) for g in raw_ds]
     args = TrainingArguments(
         output_dir=train_output,
         per_device_train_batch_size=32,
+        num_train_epochs=5,  # 5 ÉPOQUES
         logging_steps=50,
         save_strategy="no",
         report_to="none",
         data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
     )
+    print("🚀 Starting FULL training...")
     trainer.train()
     print("💾 Saving model locally...")
     model.save_pretrained(final_path, safe_serialization=False)
     tokenizer.save_pretrained(final_path)
+    # --- README AVEC LES BONS TAGS ---
     readme_content = f"""---
 library_name: transformers
 tags:
     with open(os.path.join(final_path, "README.md"), "w") as f:
         f.write(readme_content)
+    print("✅ Training complete & README with tags generated.")
 if __name__ == "__main__":
     main()

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@@ -3,1200 +3,313 @@
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