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",
+#     "transformers>=4.50.0",
+#     "accelerate>=0.24.0",
+#     "trackio",
+#     "bitsandbytes",
+# ]
+# ///
+
+"""
+Fine-tune Qwen3-0.6B on open-r1/codeforces-cots for instruction following.
+Dataset: Competitive programming with chain-of-thought reasoning.
+"""
+
+import trackio
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+# Load dataset with Python solutions (decontaminated)
+print("Loading dataset open-r1/codeforces-cots...")
+dataset = load_dataset(
+    "open-r1/codeforces-cots",
+    name="solutions_py_decontaminated",
+    split="train"
+)
+print(f"Dataset loaded: {len(dataset)} examples")
+
+# Create train/eval split
+print("Creating train/eval split...")
+dataset_split = dataset.train_test_split(test_size=0.05, seed=42)
+train_dataset = dataset_split["train"]
+eval_dataset = dataset_split["test"]
+print(f"   Train: {len(train_dataset)} examples")
+print(f"   Eval: {len(eval_dataset)} examples")
+
+# Training configuration
+config = SFTConfig(
+    # Hub settings - CRITICAL
+    output_dir="qwen3-0.6b-codeforces-cots",
+    push_to_hub=True,
+    hub_model_id="stmasson/qwen3-0.6b-codeforces-cots",
+    hub_strategy="every_save",
+
+    # Training parameters
+    num_train_epochs=1,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=8,
+    learning_rate=2e-4,
+    max_length=2048,
+
+    # Logging & checkpointing
+    logging_steps=25,
+    save_strategy="steps",
+    save_steps=500,
+    save_total_limit=2,
+
+    # Evaluation
+    eval_strategy="steps",
+    eval_steps=500,
+
+    # Optimization
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+    bf16=True,
+    gradient_checkpointing=True,
+
+    # Monitoring
+    report_to="trackio",
+    project="codeforces-finetuning",
+    run_name="qwen3-0.6b-codeforces-sft",
+)
+
+# LoRA configuration for efficient training
+peft_config = LoraConfig(
+    r=32,
+    lora_alpha=64,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+)
+
+# Initialize trainer
+print("Initializing trainer with Qwen/Qwen3-0.6B...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-0.6B",
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    args=config,
+    peft_config=peft_config,
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Pushing final model to Hub...")
+trainer.push_to_hub()
+
+print("Training complete! Model at: https://huggingface.co/stmasson/qwen3-0.6b-codeforces-cots")
+print("View metrics at: https://huggingface.co/spaces/stmasson/trackio")