Upload train_qwen_codeforces.py with huggingface_hub

train_qwen_codeforces.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.37.0", "datasets", "torch"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+print("Loading dataset: open-r1/codeforces-cots...")
+dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train")
+
+# Take a subset for quick training (t4-small is memory-constrained)
+print(f"Original dataset size: {len(dataset)}")
+dataset = dataset.select(range(min(500, len(dataset))))
+print(f"Using subset: {len(dataset)} examples")
+
+# Create small eval split for monitoring
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+print(f"Train: {len(dataset_split['train'])}, Eval: {len(dataset_split['test'])}")
+
+# Configure LoRA for efficient training
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+print("Initializing trainer...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B-Instruct",
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=lora_config,
+    args=SFTConfig(
+        output_dir="sr-test-qwen-codeforces-ft",
+
+        # Training hyperparameters optimized for t4-small
+        num_train_epochs=1,
+        per_device_train_batch_size=1,
+        per_device_eval_batch_size=1,
+        gradient_accumulation_steps=8,  # Effective batch size = 8
+        gradient_checkpointing=True,
+
+        # Learning rate
+        learning_rate=2e-4,
+        warmup_ratio=0.03,
+        lr_scheduler_type="cosine",
+
+        # Logging and evaluation
+        logging_steps=10,
+        eval_strategy="steps",
+        eval_steps=50,
+        save_strategy="steps",
+        save_steps=100,
+        save_total_limit=2,
+
+        # Memory optimization
+        optim="adamw_torch",
+        bf16=True,  # Use bf16 if supported, else will fall back to fp32
+
+        # Hub configuration
+        push_to_hub=True,
+        hub_model_id="nishant-research/sr-test-qwen-codeforces-ft",
+        hub_strategy="every_save",
+        hub_private_repo=False,
+
+        # Trackio monitoring
+        report_to="trackio",
+        project="qwen-codeforces-training",
+        run_name="qwen2.5-0.5b-codeforces-ft-test",
+    )
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Training complete! Pushing final model to Hub...")
+trainer.push_to_hub()
+
+print("✅ Training job completed successfully!")
+print(f"Model saved to: https://huggingface.co/nishant-research/sr-test-qwen-codeforces-ft")