wehub-resource-sync
87 lines
2.8 KiB
Python
87 lines
2.8 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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'save_steps': 5,
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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, RLHFArguments, infer_main, rlhf_main
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result = rlhf_main(
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RLHFArguments(
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rlhf_type='dpo',
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model='Qwen/Qwen2-7B-Instruct',
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dataset=['hjh0119/shareAI-Llama3-DPO-zh-en-emoji#100'],
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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, merge_lora=True))
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def test_mllm():
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from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
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os.environ['MAX_PIXLES'] = f'{1280 * 28 * 28}'
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result = rlhf_main(
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RLHFArguments(
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rlhf_type='dpo',
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model='Qwen/Qwen2-VL-7B-Instruct',
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dataset=['swift/RLAIF-V-Dataset#100'],
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split_dataset_ratio=0.01,
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dataset_num_proc=8,
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max_pixels=512 * 512,
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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, merge_lora=True))
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def test_mllm_zero3():
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os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
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os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0,1'
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os.environ['MAX_PIXLES'] = f'{1280 * 28 * 28}'
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from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
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rlhf_main(
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RLHFArguments(
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rlhf_type='dpo',
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model='Qwen/Qwen2-VL-7B-Instruct',
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dataset=['swift/RLAIF-V-Dataset#100'],
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split_dataset_ratio=0.01,
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dataset_num_proc=8,
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max_pixels=512 * 512,
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deepspeed='zero3',
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**kwargs))
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def test_dpo_minimal():
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from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
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result = rlhf_main(
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RLHFArguments(
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rlhf_type='dpo',
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model='Qwen/Qwen2-0.5B',
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dataset=['AI-ModelScope/orpo-dpo-mix-40k#20'],
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max_steps=2,
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per_device_train_batch_size=1,
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gradient_accumulation_steps=1,
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save_steps=2,
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split_dataset_ratio=0.01,
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tuner_type='lora',
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logging_steps=1,
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**{
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k: v
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for k, v in kwargs.items() if k not in
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['per_device_train_batch_size', 'save_steps', 'gradient_accumulation_steps', 'num_train_epochs']
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}))
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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_mllm()
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# test_mllm_zero3()
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# test_dpo_minimal()
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