text: 
  
  TITLE
  block sparse bert
  
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  3
  
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  I got following error while running example usage from https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.09-ampere-v1
  
  Do I need specific torch or transformers setup? Thanks in advance!
  
  
  Some weights of BertModel were not initialized from the model checkpoint at madlag/bert-base-uncased-squad1.1-block-sparse-0.09-ampere-v1 and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']
  You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
  Traceback (most recent call last):
    File "test.py", line 6, in <module>
      tokenizer="madlag/bert-base-uncased-squad1.1-block-sparse-0.09-ampere-v1"
    File "/workspace/transformers/src/transformers/pipelines.py", line 3231, in pipeline
      framework = framework or get_framework(model)
    File "/workspace/transformers/src/transformers/pipelines.py", line 107, in get_framework
      model = AutoModel.from_pretrained(model, revision=revision)
    File "/workspace/transformers/src/transformers/models/auto/modeling_auto.py", line 698, in from_pretrained
      pretrained_model_name_or_path, *model_args, config=config, **kwargs
    File "/workspace/transformers/src/transformers/modeling_utils.py", line 1156, in from_pretrained
      model.__class__.__name__, "\n\t".join(error_msgs)
  RuntimeError: Error(s) in loading state_dict for BertModel:
  	size mismatch for bert.encoder.layer.0.attention.self.query.weight: copying a param with shape torch.Size([768, 768]) from checkpoint, the shape in current model is torch.Size([256, 768]).
  	size mismatch for bert.encoder.layer.0.attention.self.query.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([256]).
  	size mismatch for bert.encoder.layer.0.attention.self.key.weight: copying a param with shape torch.Size([768, 768]) from checkpoint, the shape in current model is torch.Size([256, 768]).
  	size mismatch for bert.encoder.layer.0.attention.self.key.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([256]).
  	size mismatch for bert.encoder.layer.0.attention.self.value.weight: copying a param with shape torch.Size([768, 768]) from checkpoint, the shape in current model is torch.Size([256, 768]).