Upload train_qwen_hf_jobs.py with huggingface_hub

train_qwen_hf_jobs.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers", "datasets", "torch"]
+# ///
+
+"""
+Fine-tune Qwen2.5-0.5B on open-r1/codeforces-cots for instruction following.
+Production-ready script with LoRA, Trackio monitoring, and Hub saving.
+"""
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+# Load dataset - using the "messages" field for chat format
+print("📚 Loading dataset: open-r1/codeforces-cots")
+dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train")
+
+# For demo purposes, use a subset. Remove this line for full training.
+dataset = dataset.select(range(min(1000, len(dataset))))
+print(f"📊 Training on {len(dataset)} examples")
+
+# Create train/eval split for monitoring
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+
+# Configure LoRA for efficient training
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# Initialize trainer with SFT configuration
+print("🎯 Initializing SFTTrainer")
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B",
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=lora_config,
+    args=SFTConfig(
+        # Output and Hub settings
+        output_dir="qwen-codeforces-sft",
+        push_to_hub=True,
+        hub_model_id="nathens/qwen-codeforces-sft",
+        hub_strategy="every_save",
+
+        # Training hyperparameters
+        num_train_epochs=1,
+        per_device_train_batch_size=2,
+        gradient_accumulation_steps=4,
+        learning_rate=2e-4,
+
+        # Evaluation and logging
+        eval_strategy="steps",
+        eval_steps=50,
+        logging_steps=10,
+        save_steps=100,
+        save_total_limit=2,
+
+        # Optimization settings
+        bf16=True,
+        gradient_checkpointing=True,
+        optim="adamw_torch",
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.1,
+
+        # Monitoring with Trackio
+        report_to="trackio",
+        project="codeforces-instruction-tuning",
+        run_name="qwen-codeforces-v1",
+    )
+)
+
+# Train
+print("🏋️  Starting training...")
+trainer.train()
+
+# Save final model
+print("💾 Saving final model to Hub")
+trainer.push_to_hub()
+
+print("✅ Training complete!")
+print(f"📁 Model available at: https://huggingface.co/nathens/qwen-codeforces-sft")