wehub-resource-sync
62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0'
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kwargs = {
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'per_device_train_batch_size': 2,
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'per_device_eval_batch_size': 2,
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'save_steps': 50,
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'gradient_accumulation_steps': 4,
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'num_train_epochs': 1,
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}
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def test_llm():
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from swift import InferArguments, SftArguments, infer_main, sft_main
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result = sft_main(
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SftArguments(
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model='Qwen/Qwen2.5-1.5B-Instruct',
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tuner_type='lora',
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num_labels=2,
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dataset=['DAMO_NLP/jd:cls#2000'],
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split_dataset_ratio=0.01,
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**kwargs))
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last_model_checkpoint = result['last_model_checkpoint']
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infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True))
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def test_bert():
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from swift import InferArguments, SftArguments, infer_main, sft_main
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result = sft_main(
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SftArguments(
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model='answerdotai/ModernBERT-base',
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# model='iic/nlp_structbert_backbone_base_std',
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tuner_type='full',
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num_labels=2,
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dataset=['DAMO_NLP/jd:cls#2000'],
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split_dataset_ratio=0.01,
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**kwargs))
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last_model_checkpoint = result['last_model_checkpoint']
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infer_main(InferArguments(model=last_model_checkpoint, load_data_args=True))
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def test_mllm():
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from swift import InferArguments, SftArguments, infer_main, sft_main
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result = sft_main(
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SftArguments(
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model='OpenGVLab/InternVL2-1B',
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tuner_type='lora',
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num_labels=2,
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dataset=['DAMO_NLP/jd:cls#500'],
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split_dataset_ratio=0.01,
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**kwargs))
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last_model_checkpoint = result['last_model_checkpoint']
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infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True))
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if __name__ == '__main__':
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# test_llm()
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# test_bert()
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test_mllm()
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