train_qwen_hf_jobs.py

# /// 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
from transformers import AutoTokenizer
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")

# Load tokenizer to apply chat template
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B", trust_remote_code=True)

# Define formatting function to convert messages to text using chat template
def formatting_func(example):
    """Convert messages field to formatted text using tokenizer's chat template."""
    if "messages" in example and example["messages"]:
        # Apply chat template to convert messages to text
        text = tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
            add_generation_prompt=False
        )
        return {"text": text}
    return {"text": ""}

# Apply formatting to dataset
print("🔄 Formatting dataset with chat template...")
dataset = dataset.map(formatting_func, remove_columns=dataset.column_names)

# 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(
        # Dataset configuration - using default "text" field from formatting_func

        # 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")