Upload train_qwen3_codeforces.py with huggingface_hub

train_qwen3_codeforces.py

@@ -10,18 +10,27 @@ This script uses SFT (Supervised Fine-Tuning) with LoRA for efficient training.
 from datasets import load_dataset
 from peft import LoraConfig
 from trl import SFTTrainer, SFTConfig
+from transformers import AutoTokenizer
 import trackio
 
+print("Loading model and tokenizer...")
+model_name = "Qwen/Qwen2.5-0.5B"  # Using Qwen2.5-0.5B as base (closest to 0.6B)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
 print("Loading dataset...")
 dataset = load_dataset("open-r1/codeforces-cots", name="solutions_py_decontaminated", split="train")
 
 print(f"Dataset size: {len(dataset)}")
 
 # Take a manageable subset for initial training
-# You can increase this later for production training
 dataset = dataset.select(range(min(5000, len(dataset))))
 print(f"Using {len(dataset)} examples for training")
 
+# Filter out examples without valid messages
+print("Filtering valid examples...")
+dataset = dataset.filter(lambda x: x.get('messages') and len(x['messages']) >= 2)
+print(f"After filtering: {len(dataset)} examples")
+
 # Create train/eval split for monitoring
 dataset_split = dataset.train_test_split(test_size=0.05, seed=42)
 print(f"Train: {len(dataset_split['train'])}, Eval: {len(dataset_split['test'])}")
@@ -74,15 +83,15 @@ training_args = SFTConfig(
     bf16=True,
     max_grad_norm=1.0,
 
-    # Dataset formatting - use messages format
-    dataset_text_field="messages",  # Use messages format for chat
+    # Dataset formatting - NO dataset_text_field for chat format
     packing=False,
 )
 
-# Initialize trainer
+# Initialize trainer with tokenizer for chat format
 print("Initializing trainer...")
 trainer = SFTTrainer(
-    model="Qwen/Qwen2.5-0.5B",  # Using Qwen2.5-0.5B as base (closest to 0.6B)
+    model=model_name,
+    tokenizer=tokenizer,
     train_dataset=dataset_split["train"],
     eval_dataset=dataset_split["test"],
     peft_config=peft_config,