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# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.44.0", "datasets"]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
# Load dataset with editorials for better instruction following
dataset = load_dataset(
"open-r1/codeforces-cots",
name="solutions_w_editorials_decontaminated",
split="train"
)
# Create train/eval split (90/10)
dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
# LoRA configuration for efficient fine-tuning
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
task_type="CAUSAL_LM"
)
# SFT Training configuration
training_args = SFTConfig(
output_dir="qwen3-0.6b-codeforces-instruct",
# Training hyperparameters
num_train_epochs=3,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=4, # Effective batch size: 16
gradient_checkpointing=True,
# Learning rate and optimization
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.1,
optim="paged_adamw_8bit",
# Evaluation and logging
eval_strategy="steps",
eval_steps=100,
logging_steps=10,
save_strategy="steps",
save_steps=200,
save_total_limit=3,
# Hub integration (CRITICAL - saves model to Hub)
push_to_hub=True,
hub_model_id="kneeraj/qwen3-0.6b-codeforces-instruct",
hub_strategy="every_save",
hub_private_repo=False,
# Trackio monitoring
report_to="trackio",
project="codeforces-finetuning",
run_name="qwen3-0.6b-codeforces-sft",
# Performance optimizations
bf16=True,
max_grad_norm=1.0,
# Data processing
max_seq_length=2048, # CodeForces problems can be lengthy
dataset_text_field="messages", # Use chat format
packing=False, # Don't pack for instruction following
)
# Initialize trainer
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B-Instruct", # Using Qwen2.5-0.5B as base (Qwen3-0.6B may not be available)
train_dataset=dataset_split["train"],
eval_dataset=dataset_split["test"],
peft_config=peft_config,
args=training_args,
)
print("Starting training...")
print(f"Training samples: {len(dataset_split['train'])}")
print(f"Evaluation samples: {len(dataset_split['test'])}")
# Train the model
trainer.train()
# Final push to Hub
print("Pushing final model to Hub...")
trainer.push_to_hub()
print("Training complete! Model saved to: kneeraj/qwen3-0.6b-codeforces-instruct")
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