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# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio>=0.1.0", "datasets>=2.0.0", "transformers>=4.36.0"]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import AutoTokenizer
import trackio
# Load dataset - 1000 examples for ~20 min training
print("📦 Loading dataset...")
dataset = load_dataset(
"open-r1/codeforces-cots",
"solutions_w_editorials_py_decontaminated",
split="train[:1000]"
)
print(f"📊 Loaded {len(dataset)} examples")
# Load tokenizer to get chat template
print("🔤 Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
# Pre-process dataset - convert messages to text format
print("🔄 Converting messages to text format...")
def convert_messages_to_text(example):
"""Convert messages format to text using chat template."""
if "messages" in example and example["messages"]:
text = tokenizer.apply_chat_template(
example["messages"],
tokenize=False,
add_generation_prompt=False
)
return {"text": text}
return {"text": ""}
# Apply the conversion
dataset = dataset.map(convert_messages_to_text, remove_columns=dataset.column_names)
print(f"✅ Dataset preprocessed - training on {len(dataset)} examples for 3 epochs")
# LoRA configuration for efficient training
peft_config = LoraConfig(
r=8,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]
)
# Training configuration - optimized for T4 small
config = SFTConfig(
# Hub settings - CRITICAL for saving results
output_dir="qwen-codeforces-finetuned",
push_to_hub=True,
hub_model_id="papebaba/qwen-codeforces-finetuned",
hub_strategy="end",
hub_private_repo=False,
# Training parameters
num_train_epochs=3,
per_device_train_batch_size=1,
gradient_accumulation_steps=8, # Effective batch size = 8
learning_rate=2e-4,
max_length=512, # Shorter sequences for T4 small
# Checkpointing
logging_steps=10,
save_strategy="epoch",
save_total_limit=1,
# Optimization for T4 small
gradient_checkpointing=True,
bf16=True,
max_grad_norm=1.0,
warmup_ratio=0.1,
lr_scheduler_type="cosine",
optim="adamw_torch",
# Trackio monitoring
report_to="trackio",
run_name="qwen-codeforces-sft-1k",
)
# Initialize trainer with preprocessed dataset
print("🎯 Initializing trainer...")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=dataset,
args=config,
peft_config=peft_config,
)
# Train
print("🚀 Starting training on T4 small...")
trainer.train()
# Push to Hub
print("📤 Pushing final model to Hub...")
trainer.push_to_hub()
print("✅ Training complete!")
print("📊 View metrics at: https://huggingface.co/spaces/papebaba/trackio")
print("🤗 Model at: https://huggingface.co/papebaba/qwen-codeforces-finetuned")
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