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| import os |
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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 = { |
| "model_name_or_path": TINY_LLAMA3, |
| "stage": "sft", |
| "do_train": True, |
| "finetuning_type": "full", |
| "template": "llama3", |
| "dataset": TINY_DATA, |
| "dataset_dir": "ONLINE", |
| "cutoff_len": 8192, |
| "output_dir": "dummy_dir", |
| "overwrite_output_dir": True, |
| "fp16": True, |
| } |
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| def test_load_train_only(): |
| dataset_module = load_dataset_module(**TRAIN_ARGS) |
| assert dataset_module.get("train_dataset") is not None |
| assert dataset_module.get("eval_dataset") is None |
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| def test_load_val_size(): |
| dataset_module = load_dataset_module(val_size=0.1, **TRAIN_ARGS) |
| assert dataset_module.get("train_dataset") is not None |
| assert dataset_module.get("eval_dataset") is not None |
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| def test_load_eval_data(): |
| dataset_module = load_dataset_module(eval_dataset=TINY_DATA, **TRAIN_ARGS) |
| assert dataset_module.get("train_dataset") is not None |
| assert dataset_module.get("eval_dataset") is not None |
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