from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained("MODEL_NAME")
model = BertModel.from_pretrained("MODEL_NAME")

我将模型下载到了本地，MiniRBT-h256-pt文件夹下有三个文件 config.json pytorch_model.bin vocab.txt
我将MODEL_NAME替换成本地模型路径：XXXXX/MiniRBT-h256-pt，
config能够正常加载
但是加载model时候报错
Some weights of the model checkpoint at XXXXX/MiniRBT-h256-pt were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight']

• This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  Some weights of BertModel were not initialized from the model checkpoint at XXXXXX/MiniRBT-h256-pt and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']
  You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

根据错误提示的是，模型结构和权重不一致，导致的，请问这个问题是我下载文件有错误？还是哪里的错误