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| import sys
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| print(f"Python version: {sys.version}")
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| import torch
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| print(f"PyTorch version: {torch.__version__}")
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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"CUDA device: {torch.cuda.get_device_name(0)}")
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| import transformers
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| print(f"Transformers version: {transformers.__version__}")
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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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| print("Loading dataset...")
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| ds = load_dataset("trl-lib/Capybara", split="train[:50]")
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| print(f"Loaded {len(ds)} examples")
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| config = SFTConfig(
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| output_dir="test-output",
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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=1,
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| logging_steps=5,
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| report_to="none",
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| push_to_hub=True,
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| hub_model_id="luiscosio/qwen25-test-v2",
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| bf16=True,
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| )
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| peft_config = LoraConfig(
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| r=8,
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| lora_alpha=16,
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| task_type="CAUSAL_LM",
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| target_modules=["q_proj", "v_proj"]
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| )
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| print("Creating trainer with Qwen2.5-0.5B...")
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| trainer = SFTTrainer(
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| model="Qwen/Qwen2.5-0.5B",
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| train_dataset=ds,
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| args=config,
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| peft_config=peft_config,
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| )
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| print("Training...")
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| trainer.train()
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| trainer.push_to_hub()
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| print("Done!")
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