Upload train_qwen3_codeforces.py with huggingface_hub

train_qwen3_codeforces.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.44.0", "datasets"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+# Load dataset with editorials for better instruction following
+dataset = load_dataset(
+    "open-r1/codeforces-cots",
+    name="solutions_w_editorials_decontaminated",
+    split="train"
+)
+
+# Create train/eval split (90/10)
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+
+# LoRA configuration for efficient fine-tuning
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    task_type="CAUSAL_LM"
+)
+
+# SFT Training configuration
+training_args = SFTConfig(
+    output_dir="qwen3-0.6b-codeforces-instruct",
+    
+    # Training hyperparameters
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=4,
+    gradient_accumulation_steps=4,  # Effective batch size: 16
+    gradient_checkpointing=True,
+    
+    # Learning rate and optimization
+    learning_rate=2e-4,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.1,
+    optim="paged_adamw_8bit",
+    
+    # Evaluation and logging
+    eval_strategy="steps",
+    eval_steps=100,
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=200,
+    save_total_limit=3,
+    
+    # Hub integration (CRITICAL - saves model to Hub)
+    push_to_hub=True,
+    hub_model_id="kneeraj/qwen3-0.6b-codeforces-instruct",
+    hub_strategy="every_save",
+    hub_private_repo=False,
+    
+    # Trackio monitoring
+    report_to="trackio",
+    project="codeforces-finetuning",
+    run_name="qwen3-0.6b-codeforces-sft",
+    
+    # Performance optimizations
+    bf16=True,
+    max_grad_norm=1.0,
+    
+    # Data processing
+    max_seq_length=2048,  # CodeForces problems can be lengthy
+    dataset_text_field="messages",  # Use chat format
+    packing=False,  # Don't pack for instruction following
+)
+
+# Initialize trainer
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B-Instruct",  # Using Qwen2.5-0.5B as base (Qwen3-0.6B may not be available)
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=peft_config,
+    args=training_args,
+)
+
+print("Starting training...")
+print(f"Training samples: {len(dataset_split['train'])}")
+print(f"Evaluation samples: {len(dataset_split['test'])}")
+
+# Train the model
+trainer.train()
+
+# Final push to Hub
+print("Pushing final model to Hub...")
+trainer.push_to_hub()
+
+print("Training complete! Model saved to: kneeraj/qwen3-0.6b-codeforces-instruct")