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# /// script
# requires-python = ">=3.10"
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.45.0",
# "datasets>=2.18.0",
# "accelerate>=0.30.0",
# "torch>=2.0.0",
# ]
# ///
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B", trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen3-0.6B",
torch_dtype="auto",
trust_remote_code=True,
)
print("Loading dataset...")
dataset = load_dataset("open-r1/codeforces-cots", "solutions_py_decontaminated", split="train")
print(f"Dataset size: {len(dataset)}")
# Take a subset for faster training (full dataset is large)
dataset = dataset.shuffle(seed=42).select(range(min(10000, len(dataset))))
print(f"Using {len(dataset)} examples")
# Split
split = dataset.train_test_split(test_size=0.05, seed=42)
train_dataset = split["train"]
eval_dataset = split["test"]
print("Setting up LoRA...")
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"],
bias="none",
task_type="CAUSAL_LM",
)
print("Setting up training...")
training_args = SFTConfig(
output_dir="qwen3-0.6b-codeforces-sft",
push_to_hub=True,
hub_model_id="luiscosio/qwen3-0.6b-codeforces-sft",
num_train_epochs=3,
per_device_train_batch_size=2,
gradient_accumulation_steps=8,
gradient_checkpointing=True,
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.1,
eval_strategy="steps",
eval_steps=100,
save_strategy="steps",
save_steps=100,
save_total_limit=3,
logging_steps=10,
bf16=True,
max_length=2048,
report_to="none",
)
print("Creating trainer...")
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
args=training_args,
)
print("Starting training...")
trainer.train()
print("Saving and pushing to Hub...")
trainer.save_model()
trainer.push_to_hub()
print("Done!")
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