Upload train_sft_qwen.py with huggingface_hub

train_sft_qwen.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "torch", "transformers>=4.40.0"]
+# ///
+
+"""
+SFT Fine-tuning Script for Qwen/Qwen2.5-0.5B
+Optimized for Hugging Face Jobs with Trackio monitoring
+"""
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+# Load dataset - using TRL-compatible Capybara dataset
+print("Loading dataset...")
+dataset = load_dataset("trl-lib/Capybara", split="train")
+
+# Create train/eval split for monitoring progress
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+print(f"Train size: {len(dataset_split['train'])}, Eval size: {len(dataset_split['test'])}")
+
+# LoRA configuration for efficient fine-tuning
+peft_config = LoraConfig(
+    r=16,                    # LoRA rank
+    lora_alpha=32,           # LoRA alpha (scaling factor)
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],  # Attention layers
+    lora_dropout=0.05,       # Dropout for regularization
+    bias="none",             # Don't train bias terms
+    task_type="CAUSAL_LM"    # Causal language modeling
+)
+
+# Training configuration
+training_args = SFTConfig(
+    # Output and Hub settings
+    output_dir="qwen-0.5b-sft-capybara",
+    push_to_hub=True,
+    hub_model_id="vgtomahawk/qwen-0.5b-sft-capybara",
+    hub_strategy="every_save",
+    hub_private_repo=False,
+
+    # Training parameters
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=4,
+    gradient_accumulation_steps=4,  # Effective batch size: 4 * 4 = 16
+    gradient_checkpointing=True,
+
+    # Optimization
+    learning_rate=2e-4,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.1,
+    optim="paged_adamw_8bit",  # Memory-efficient optimizer
+
+    # Evaluation and logging
+    eval_strategy="steps",
+    eval_steps=50,
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=3,  # Keep only last 3 checkpoints
+
+    # Trackio monitoring
+    report_to="trackio",
+    run_name="qwen-0.5b-sft-capybara-test",
+
+    # Performance
+    bf16=True,  # Use bfloat16 for better numerical stability
+    dataloader_num_workers=4,
+    remove_unused_columns=True,
+)
+
+# Initialize trainer
+print("Initializing SFT Trainer...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B",
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=peft_config,
+    args=training_args,
+)
+
+# Train the model
+print("Starting training...")
+trainer.train()
+
+# Save final model to Hub
+print("Pushing final model to Hub...")
+trainer.push_to_hub(commit_message="Training completed")
+
+print("✅ Training completed successfully!")
+print(f"Model saved to: https://huggingface.co/{training_args.hub_model_id}")