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https://github.com/hiyouga/LLaMA-Factory.git
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50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import os
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import torch
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from llamafactory.hparams import get_infer_args, get_train_args
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from llamafactory.model import load_model, load_tokenizer
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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TRAIN_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-supervised-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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INFER_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"finetuning_type": "full",
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"template": "llama3",
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"infer_dtype": "float16",
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}
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def test_full_train():
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model_args, _, _, finetuning_args, _ = get_train_args(TRAIN_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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for param in model.parameters():
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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def test_full_inference():
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model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
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for param in model.parameters():
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assert param.requires_grad is False
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assert param.dtype == torch.float16
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