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# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.44.0", "datasets>=2.14.0", "torch>=2.0.0", "accelerate>=0.24.0"]
# ///
"""
Fine-tune Qwen3-0.6B on open-r1/codeforces-cots for instruction following.
This script uses SFT (Supervised Fine-Tuning) with LoRA for efficient training.
"""
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import AutoTokenizer
import trackio
print("Loading model and tokenizer...")
model_name = "Qwen/Qwen2.5-0.5B" # Using Qwen2.5-0.5B as base (closest to 0.6B)
tokenizer = AutoTokenizer.from_pretrained(model_name)
print("Loading dataset...")
dataset = load_dataset("open-r1/codeforces-cots", name="solutions_py_decontaminated", split="train")
print(f"Dataset size: {len(dataset)}")
# Take a manageable subset for initial training
dataset = dataset.select(range(min(5000, len(dataset))))
print(f"Using {len(dataset)} examples for training")
# Filter out examples without valid messages
print("Filtering valid examples...")
dataset = dataset.filter(lambda x: x.get('messages') and len(x['messages']) >= 2)
print(f"After filtering: {len(dataset)} examples")
# Create train/eval split for monitoring
dataset_split = dataset.train_test_split(test_size=0.05, seed=42)
print(f"Train: {len(dataset_split['train'])}, Eval: {len(dataset_split['test'])}")
# Configure LoRA for efficient fine-tuning
peft_config = LoraConfig(
r=16,
lora_alpha=32,
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"]
)
# Training configuration
training_args = SFTConfig(
output_dir="qwen3-codeforces-sft",
# Training parameters
num_train_epochs=3,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
gradient_accumulation_steps=8, # Effective batch size = 16
gradient_checkpointing=True,
# Optimization
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.1,
optim="adamw_torch",
# Evaluation and saving
eval_strategy="steps",
eval_steps=100,
save_strategy="steps",
save_steps=200,
save_total_limit=3,
# Logging
logging_steps=10,
report_to="trackio",
# Hub configuration - CRITICAL for saving results
push_to_hub=True,
hub_model_id="udaykiran212/qwen3-0.6b-codeforces-sft",
hub_strategy="every_save",
hub_private_repo=False,
# Performance
bf16=True,
max_grad_norm=1.0,
# Dataset formatting - NO dataset_text_field for chat format
packing=False,
)
# Initialize trainer for chat format
print("Initializing trainer...")
trainer = SFTTrainer(
model=model_name,
train_dataset=dataset_split["train"],
eval_dataset=dataset_split["test"],
peft_config=peft_config,
args=training_args,
processing_class=tokenizer,
)
# Start training
print("Starting training...")
trainer.train()
# Save and push final model
print("Saving final model...")
trainer.save_model()
trainer.push_to_hub()
print("Training completed successfully!")
print(f"Model saved to: https://huggingface.co/udaykiran212/qwen3-0.6b-codeforces-sft")
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