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https://github.com/hiyouga/LLaMA-Factory.git
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45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
import os
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import pytest
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from datasets import load_dataset
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from llamafactory.data import get_dataset
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from llamafactory.hparams import get_train_args
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from llamafactory.model import load_tokenizer
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-LlamaForCausalLM")
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TRAINING_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"stage": "sft",
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"do_train": True,
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"finetuning_type": "full",
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"dataset": "llamafactory/tiny_dataset",
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"dataset_dir": "ONLINE",
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"template": "llama3",
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"cutoff_len": 1024,
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"overwrite_cache": True,
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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.parametrize("test_num", [5])
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def test_supervised(test_num: int):
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model_args, data_args, training_args, _, _ = get_train_args(TRAINING_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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tokenized_data = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
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original_data = load_dataset(TRAINING_ARGS["dataset"], split="train")
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for test_idx in range(test_num):
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decode_result = tokenizer.decode(tokenized_data["input_ids"][test_idx])
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messages = [
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{"role": "user", "content": original_data[test_idx]["instruction"]},
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{"role": "assistant", "content": original_data[test_idx]["output"]},
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]
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templated_result = tokenizer.apply_chat_template(messages, tokenize=False)
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assert decode_result == templated_result
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