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import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
kwargs = {
'per_device_train_batch_size': 2,
'save_steps': 5,
'gradient_accumulation_steps': 4,
'num_train_epochs': 1,
}
def test_llm():
from swift.llm import rlhf_main, RLHFArguments, infer_main, InferArguments
result = rlhf_main(
RLHFArguments(
rlhf_type='dpo',
model='Qwen/Qwen2-7B-Instruct',
dataset=['hjh0119/shareAI-Llama3-DPO-zh-en-emoji#100'],
split_dataset_ratio=0.01,
**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():
from swift.llm import rlhf_main, RLHFArguments, infer_main, InferArguments
os.environ['MAX_PIXLES'] = f'{1280 * 28 * 28}'
result = rlhf_main(
RLHFArguments(
rlhf_type='dpo',
model='Qwen/Qwen2-VL-7B-Instruct',
dataset=['swift/RLAIF-V-Dataset#100'],
split_dataset_ratio=0.01,
dataset_num_proc=8,
max_pixels=512 * 512,
**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_zero3():
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
os.environ['MAX_PIXLES'] = f'{1280 * 28 * 28}'
from swift.llm import rlhf_main, RLHFArguments, infer_main, InferArguments
rlhf_main(
RLHFArguments(
rlhf_type='dpo',
model='Qwen/Qwen2-VL-7B-Instruct',
dataset=['swift/RLAIF-V-Dataset#100'],
split_dataset_ratio=0.01,
dataset_num_proc=8,
max_pixels=512 * 512,
deepspeed='zero3',
**kwargs))
if __name__ == '__main__':
# test_llm()
test_mllm()
# test_mllm_zero3()
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