Exception: data did not match any variant of untagged enum ModelWrappe

[HoangLayor]

Can you provide me with the versions of torch, transformers, tokenizers, and accelerate?
I guess I'm having an issue with my installation environment.

# pip install accelerate
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration

processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b", cache_dir="/tmp")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map="auto")

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
Cell In[1], line 6
      4 from transformers import Blip2Processor, Blip2ForConditionalGeneration
----> 6 processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b", cache_dir="/tmp")
      7 model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map="auto")

in ProcessorMixin.from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, **kwargs)
    225 if token is not None:
    226     kwargs["token"] = token
--> 228 args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
    229 return cls(*args)

 in ProcessorMixin._get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    269     else:
    270         attribute_class = getattr(transformers_module, class_name)
--> 272     args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs))
    273 return args

 in AutoTokenizer.from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs)
    783     if tokenizer_class is None:
    784         raise ValueError(
    785             f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported."
    786         )
--> 787     return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
    789 # Otherwise we have to be creative.
    790 # if model is an encoder decoder, the encoder tokenizer class is used by default
    791 if isinstance(config, EncoderDecoderConfig):

in PreTrainedTokenizerBase.from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, *init_inputs, **kwargs)

-> 2028 return cls._from_pretrained(
   2029     resolved_vocab_files,
   2030     pretrained_model_name_or_path,
   2031     init_configuration,
   2032     *init_inputs,
   2033     token=token,
   2034     cache_dir=cache_dir,
   2035     local_files_only=local_files_only,
   2036     _commit_hash=commit_hash,
   2037     _is_local=is_local,
   2038     **kwargs,
   2039 )

in PreTrainedTokenizerBase._from_pretrained(cls, resolved_vocab_files, pretrained_model_name_or_path, init_configuration, token, cache_dir, local_files_only, _commit_hash, _is_local, *init_inputs, **kwargs)
   2258 # Instantiate the tokenizer.
   2259 try:
-> 2260     tokenizer = cls(*init_inputs, **init_kwargs)

in GPT2TokenizerFast.__init__(self, vocab_file, merges_file, tokenizer_file, unk_token, bos_token, eos_token, add_prefix_space, **kwargs)
  
--> 134     super().__init__(
    135         vocab_file,
    136         merges_file,
    137         tokenizer_file=tokenizer_file,
    138         unk_token=unk_token,
    139         bos_token=bos_token,
    140         eos_token=eos_token,
    141         add_prefix_space=add_prefix_space,
    142         **kwargs,
    143     )

in PreTrainedTokenizerFast.__init__(self, *args, **kwargs)
    108     fast_tokenizer = copy.deepcopy(tokenizer_object)
    109 elif fast_tokenizer_file is not None and not from_slow:
    110     # We have a serialization from tokenizers which let us directly build the backend
--> 111     fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
    112 elif slow_tokenizer is not None:
    113     # We need to convert a slow tokenizer to build the backend
    114     fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)

Exception: data did not match any variant of untagged enum ModelWrapper at line 250373 column 3