| import os
|
|
|
| os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
|
| kwargs = {
|
| 'per_device_train_batch_size': 2,
|
| 'save_steps': 30,
|
| 'gradient_accumulation_steps': 2,
|
| 'num_train_epochs': 1,
|
| }
|
|
|
|
|
| def test_sft():
|
| from swift import sft_main, SftArguments, infer_main, InferArguments
|
| result = sft_main(
|
| SftArguments(
|
| model='Qwen/Qwen2.5-7B-Instruct',
|
| dataset=['swift/self-cognition#200'],
|
| split_dataset_ratio=0.01,
|
| use_liger_kernel=True,
|
| **kwargs))
|
| last_model_checkpoint = result['last_model_checkpoint']
|
| infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True))
|
|
|
|
|
| def test_mllm_dpo():
|
| os.environ['MAX_PIXLES'] = f'{1280 * 28 * 28}'
|
| from swift import rlhf_main, RLHFArguments, infer_main, InferArguments
|
| result = rlhf_main(
|
| RLHFArguments(
|
| rlhf_type='dpo',
|
| model='Qwen/Qwen2.5-VL-3B-Instruct',
|
| tuner_type='full',
|
| dataset=['swift/RLAIF-V-Dataset#1000'],
|
| split_dataset_ratio=0.01,
|
| dataset_num_proc=8,
|
| deepspeed='zero3',
|
| use_liger_kernel=True,
|
| **kwargs))
|
| last_model_checkpoint = result['last_model_checkpoint']
|
| infer_main(InferArguments(model=last_model_checkpoint, load_data_args=True))
|
|
|
|
|
| if __name__ == '__main__':
|
| test_sft()
|
|
|
|
|
