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import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
kwargs = {
'per_device_train_batch_size': 2,
'save_steps': 50,
'gradient_accumulation_steps': 4,
'num_train_epochs': 3,
}
def test_llm():
from swift.llm import sft_main, TrainArguments, infer_main, InferArguments
result = sft_main(
TrainArguments(
model='Qwen/Qwen2-7B-Instruct',
dataset=['AI-ModelScope/alpaca-gpt4-data-zh#1000', 'swift/self-cognition#1000'],
split_dataset_ratio=0.01,
packing=True,
max_length=4096,
attn_impl='flash_attn',
logging_steps=1,
**kwargs))
last_model_checkpoint = result['last_model_checkpoint']
infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
def test_streaming():
from swift.llm import sft_main, TrainArguments, infer_main, InferArguments
result = sft_main(
TrainArguments(
model='Qwen/Qwen2-7B-Instruct',
dataset=['AI-ModelScope/alpaca-gpt4-data-zh#10000'],
packing=True,
max_length=4096,
streaming=True,
attn_impl='flash_attn',
max_steps=100,
dataset_num_proc=1,
**kwargs))
last_model_checkpoint = result['last_model_checkpoint']
infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
def test_mllm_streaming():
from swift.llm import sft_main, TrainArguments, infer_main, InferArguments
result = sft_main(
TrainArguments(
model='Qwen/Qwen2.5-VL-7B-Instruct',
dataset=['AI-ModelScope/LaTeX_OCR#20000'],
packing=True,
max_length=8192,
streaming=True,
attn_impl='flash_attn',
max_steps=100,
dataset_num_proc=4,
**kwargs))
last_model_checkpoint = result['last_model_checkpoint']
infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
if __name__ == '__main__':
# test_llm()
# test_streaming()
test_mllm_streaming()
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