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| import os
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| import pytest
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| from llamafactory.train.test_utils import load_dataset_module
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| DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
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| TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
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| TINY_DATA = os.getenv("TINY_DATA", "llamafactory/tiny-supervised-dataset")
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| TRAIN_ARGS = {
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| "model_name_or_path": TINY_LLAMA3,
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| "stage": "sft",
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| "do_train": True,
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| "finetuning_type": "full",
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| "template": "llama3",
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| "dataset": TINY_DATA,
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| "dataset_dir": "ONLINE",
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| "cutoff_len": 8192,
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| "output_dir": "dummy_dir",
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| "overwrite_output_dir": True,
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| "fp16": True,
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| }
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| @pytest.mark.runs_on(["cpu", "mps"])
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| def test_load_train_only():
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| dataset_module = load_dataset_module(**TRAIN_ARGS)
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| assert dataset_module.get("train_dataset") is not None
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| assert dataset_module.get("eval_dataset") is None
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| @pytest.mark.runs_on(["cpu", "mps"])
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| def test_load_val_size():
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| dataset_module = load_dataset_module(val_size=0.1, **TRAIN_ARGS)
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| assert dataset_module.get("train_dataset") is not None
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| assert dataset_module.get("eval_dataset") is not None
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| @pytest.mark.runs_on(["cpu", "mps"])
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| def test_load_eval_data():
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| dataset_module = load_dataset_module(eval_dataset=TINY_DATA, **TRAIN_ARGS)
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| assert dataset_module.get("train_dataset") is not None
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| assert dataset_module.get("eval_dataset") is not None
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