tests/train/test_ppo.py

import os

os.environ['CUDA_VISIBLE_DEVICES'] = '0'

kwargs = {
    'per_device_train_batch_size': 2,
    'save_steps': 5,
    'gradient_accumulation_steps': 4,
    'num_train_epochs': 1,
}


def test_rm():
    from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
    result = rlhf_main(
        RLHFArguments(
            rlhf_type='rm',
            model='Shanghai_AI_Laboratory/internlm2-1_8b-reward',
            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_ppo():
    from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
    result = rlhf_main(
        RLHFArguments(
            rlhf_type='ppo',
            model='LLM-Research/Llama-3.2-1B-Instruct',
            reward_model='AI-ModelScope/GRM-Llama3.2-3B-rewardmodel-ft',
            dataset=['AI-ModelScope/alpaca-gpt4-data-zh#100', 'AI-ModelScope/alpaca-gpt4-data-en#100'],
            **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_rm()
    test_ppo()