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"""Fine-tune Qwen3-0.6B on CodeForces-CoTS (100 examples)""" |
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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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import torch |
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print(f"CUDA available: {torch.cuda.is_available()}") |
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if torch.cuda.is_available(): |
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print(f"GPU: {torch.cuda.get_device_name(0)}") |
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dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train").select(range(100)) |
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print(f"Dataset: {len(dataset)} examples") |
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peft_config = LoraConfig( |
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r=16, lora_alpha=32, 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", task_type="CAUSAL_LM" |
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) |
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training_args = SFTConfig( |
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output_dir="./qwen3-0.6b-codeforces-cots", |
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num_train_epochs=1, |
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per_device_train_batch_size=1, |
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gradient_accumulation_steps=8, |
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learning_rate=2e-4, |
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warmup_ratio=0.1, |
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logging_steps=5, |
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save_strategy="no", |
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eval_strategy="no", |
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max_length=2048, |
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push_to_hub=True, |
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hub_model_id="gilbaes/qwen3-0.6b-codeforces-cots", |
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report_to="none", |
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bf16=True, |
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gradient_checkpointing=True, |
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optim="adamw_torch_fused", |
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) |
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trainer = SFTTrainer( |
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model="Qwen/Qwen3-0.6B", |
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train_dataset=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(f"Trainable params: {trainer.model.num_parameters(only_trainable=True):,}") |
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trainer.train() |
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trainer.push_to_hub() |
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print("Done! Model at gilbaes/qwen3-0.6b-codeforces-cots") |
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