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# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.37.0", "datasets", "torch"]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
print("Loading dataset: open-r1/codeforces-cots...")
dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train")
# Take a subset for quick training (t4-small is memory-constrained)
print(f"Original dataset size: {len(dataset)}")
dataset = dataset.select(range(min(500, len(dataset))))
print(f"Using subset: {len(dataset)} examples")
# Create small eval split for monitoring
dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
print(f"Train: {len(dataset_split['train'])}, Eval: {len(dataset_split['test'])}")
# Configure LoRA for efficient training
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
bias="none",
task_type="CAUSAL_LM"
)
print("Initializing trainer...")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B-Instruct",
train_dataset=dataset_split["train"],
eval_dataset=dataset_split["test"],
peft_config=lora_config,
args=SFTConfig(
output_dir="sr-test-qwen-codeforces-ft",
# Training hyperparameters optimized for t4-small
num_train_epochs=1,
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
gradient_accumulation_steps=8, # Effective batch size = 8
gradient_checkpointing=True,
# Learning rate
learning_rate=2e-4,
warmup_ratio=0.03,
lr_scheduler_type="cosine",
# Logging and evaluation
logging_steps=10,
eval_strategy="steps",
eval_steps=50,
save_strategy="steps",
save_steps=100,
save_total_limit=2,
# Memory optimization
optim="adamw_torch",
bf16=True, # Use bf16 if supported, else will fall back to fp32
# Hub configuration
push_to_hub=True,
hub_model_id="nishant-research/sr-test-qwen-codeforces-ft",
hub_strategy="every_save",
hub_private_repo=False,
# Trackio monitoring
report_to="trackio",
project="qwen-codeforces-training",
run_name="qwen2.5-0.5b-codeforces-ft-test",
)
)
print("Starting training...")
trainer.train()
print("Training complete! Pushing final model to Hub...")
trainer.push_to_hub()
print("✅ Training job completed successfully!")
print(f"Model saved to: https://huggingface.co/nishant-research/sr-test-qwen-codeforces-ft")
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