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| import os
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| os.environ["TOKENIZERS_PARALLELISM"] = "false"
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| from datasets import load_dataset
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| from peft import LoraConfig
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| from trl import SFTTrainer, SFTConfig
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| from transformers import AutoModelForCausalLM, AutoTokenizer
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| print("Loading tokenizer...")
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| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B", trust_remote_code=True)
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| if tokenizer.pad_token is None:
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| tokenizer.pad_token = tokenizer.eos_token
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| print("Loading model...")
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| model = AutoModelForCausalLM.from_pretrained(
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| "Qwen/Qwen3-0.6B",
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| torch_dtype="auto",
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| trust_remote_code=True,
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| )
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| print("Loading dataset...")
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| dataset = load_dataset("open-r1/codeforces-cots", "solutions_py_decontaminated", split="train")
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| print(f"Dataset size: {len(dataset)}")
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| dataset = dataset.shuffle(seed=42).select(range(min(10000, len(dataset))))
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| print(f"Using {len(dataset)} examples")
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| split = dataset.train_test_split(test_size=0.05, seed=42)
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| train_dataset = split["train"]
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| eval_dataset = split["test"]
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| print("Setting up LoRA...")
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| peft_config = LoraConfig(
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| r=16,
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| lora_alpha=32,
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| lora_dropout=0.05,
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| target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
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| bias="none",
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| task_type="CAUSAL_LM",
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| )
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| print("Setting up training...")
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| training_args = SFTConfig(
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| output_dir="qwen3-0.6b-codeforces-sft",
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| push_to_hub=True,
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| hub_model_id="luiscosio/qwen3-0.6b-codeforces-sft",
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| num_train_epochs=3,
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| per_device_train_batch_size=2,
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| gradient_accumulation_steps=8,
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| gradient_checkpointing=True,
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| learning_rate=2e-4,
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| lr_scheduler_type="cosine",
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| warmup_ratio=0.1,
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| eval_strategy="steps",
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| eval_steps=100,
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| save_strategy="steps",
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| save_steps=100,
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| save_total_limit=3,
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| logging_steps=10,
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| bf16=True,
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| max_length=2048,
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| report_to="none",
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| )
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| print("Creating trainer...")
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| trainer = SFTTrainer(
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| model=model,
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| tokenizer=tokenizer,
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| train_dataset=train_dataset,
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| eval_dataset=eval_dataset,
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| peft_config=peft_config,
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| args=training_args,
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| )
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| print("Starting training...")
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| trainer.train()
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| print("Saving and pushing to Hub...")
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| trainer.save_model()
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| trainer.push_to_hub()
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| print("Done!")
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