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# /// script
# requires-python = ">=3.10"
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.45.0",
# "accelerate>=0.24.0",
# "trackio",
# ]
# ///
"""
Fine-tune Qwen3-0.6B on open-r1/codeforces-cots for instruction following.
Uses SFT with LoRA for efficient training.
"""
import trackio
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
# Load dataset - uses messages format which is TRL-compatible
print("Loading open-r1/codeforces-cots dataset...")
dataset = load_dataset("open-r1/codeforces-cots", split="train")
print(f"Dataset loaded: {len(dataset)} examples")
# Sample for faster training (adjust as needed)
if len(dataset) > 10000:
dataset = dataset.shuffle(seed=42).select(range(10000))
print(f"Sampled to 10,000 examples for training")
# 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
output_dir="qwen3-0.6b-codeforces-sft",
push_to_hub=True,
hub_model_id="atlaswang/qwen3-0.6b-codeforces-sft",
hub_strategy="every_save",
# Training parameters
num_train_epochs=3,
per_device_train_batch_size=2,
gradient_accumulation_steps=8,
learning_rate=2e-4,
max_length=2048,
# Logging & checkpointing
logging_steps=10,
save_strategy="steps",
save_steps=200,
save_total_limit=3,
# Evaluation
eval_strategy="steps",
eval_steps=200,
# Optimization
warmup_ratio=0.1,
lr_scheduler_type="cosine",
gradient_checkpointing=True,
bf16=True,
# Monitoring
report_to="trackio",
project="qwen3-codeforces-sft",
run_name="qwen3-0.6b-codeforces-instruction-tuning",
)
# LoRA configuration
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 and train
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 to Hub...")
trainer.push_to_hub()
trackio.finish()
print("Training complete! Model at: https://huggingface.co/atlaswang/qwen3-0.6b-codeforces-sft")
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