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Update README.md

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@@ -28,7 +28,7 @@ We release SongComposer series in two versions:
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  ### Import from Transformers
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  To load the SongComposer_pretrain model using Transformers, use the following code:
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  ```python
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- from transformers import AutoTokenizer, AutoModelForCausalLM
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  ckpt_path = "Mar2Ding/songcomposer_pretrain"
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  tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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  model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half()
@@ -39,7 +39,7 @@ model.inference_pretrain(prompt, tokenizer, model)
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  ### 通过 Transformers 加载
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  通过以下的代码加载 SongComposer_pretrain 模型
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  ```python
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- from transformers import AutoTokenizer, AutoModelForCausalLM
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  ckpt_path = "Mar2Ding/songcomposer_pretrain"
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  tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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  model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half()
 
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  ### Import from Transformers
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  To load the SongComposer_pretrain model using Transformers, use the following code:
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  ```python
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+ from transformers import AutoTokenizer, AutoModel
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  ckpt_path = "Mar2Ding/songcomposer_pretrain"
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  tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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  model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half()
 
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  ### 通过 Transformers 加载
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  通过以下的代码加载 SongComposer_pretrain 模型
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  ```python
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+ from transformers import AutoTokenizer, AutoModel
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  ckpt_path = "Mar2Ding/songcomposer_pretrain"
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  tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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  model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half()