wehub-resource-sync
42 lines
1.4 KiB
Python
42 lines
1.4 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_rm():
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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='rm',
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model='Shanghai_AI_Laboratory/internlm2-1_8b-reward',
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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_ppo():
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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='ppo',
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model='LLM-Research/Llama-3.2-1B-Instruct',
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reward_model='AI-ModelScope/GRM-Llama3.2-3B-rewardmodel-ft',
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dataset=['AI-ModelScope/alpaca-gpt4-data-zh#100', 'AI-ModelScope/alpaca-gpt4-data-en#100'],
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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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if __name__ == '__main__':
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# test_rm()
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test_ppo()
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