NotImplementedError when using CTransformers AutoTokenizer

[NasonZ]

env:

transformers ==4.35.2 ctransformers==0.2.27+cu121

from ctransformers import AutoModelForCausalLM, AutoTokenizer

model_name = "/home/me/project/search_engine/text-generation-webui/models/OpenHermes-2.5-Mistral-7B-GGUF/openhermes-2.5-mistral-7b.Q5_K_M.gguf"

def load_model(model_name: str):
    model = AutoModelForCausalLM.from_pretrained(model_name, hf=True)
    tokenizer = AutoTokenizer.from_pretrained(model)

    return model, tokenizer

tokenizer, model = load_model(model_name)

gives output:

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
/project/project/eda.ipynb Cell 13 line 1
      7     tokenizer = AutoTokenizer.from_pretrained(model)
      9     return model, tokenizer
---> 11 tokenizer, model = load_model(model_name)

/project/project/eda.ipynb Cell 13 line 7
      5 def load_model(model_name: str):
      6     model = AutoModelForCausalLM.from_pretrained(model_name, hf=True)
----> 7     tokenizer = AutoTokenizer.from_pretrained(model)
      9     return model, tokenizer

File ~/project/search_engine/text-generation-webui/installer_files/env/lib/python3.11/site-packages/ctransformers/hub.py:268, in AutoTokenizer.from_pretrained(cls, model)
    261 if not isinstance(model, CTransformersModel):
    262     raise TypeError(
    263         f"Currently `AutoTokenizer.from_pretrained` only accepts a model object. Please use:\n\n"
    264         "  model = AutoModelForCausalLM.from_pretrained(..., hf=True)\n"
    265         "  tokenizer = AutoTokenizer.from_pretrained(model)"
    266     )
--> 268 return CTransformersTokenizer(model._llm)

File ~/project/search_engine/text-generation-webui/installer_files/env/lib/python3.11/site-packages/ctransformers/transformers.py:84, in CTransformersTokenizer.__init__(self, llm, **kwargs)
     83 def __init__(self, llm: LLM, **kwargs):
---> 84     super().__init__(**kwargs)
     85     self._llm = llm

File ~/project/search_engine/text-generation-webui/installer_files/env/lib/python3.11/site-packages/transformers/tokenization_utils.py:367, in PreTrainedTokenizer.__init__(self, **kwargs)
    363 super().__init__(**kwargs)
    365 # 4. If some of the special tokens are not part of the vocab, we add them, at the end.
    366 # the order of addition is the same as self.SPECIAL_TOKENS_ATTRIBUTES following `tokenizers`
--> 367 self._add_tokens(
    368     [token for token in self.all_special_tokens_extended if token not in self._added_tokens_encoder],
    369     special_tokens=True,
    370 )
    372 self._decode_use_source_tokenizer = False

File ~/project/search_engine/text-generation-webui/installer_files/env/lib/python3.11/site-packages/transformers/tokenization_utils.py:467, in PreTrainedTokenizer._add_tokens(self, new_tokens, special_tokens)
    465     return added_tokens
    466 # TODO this is fairly slow to improve!
--> 467 current_vocab = self.get_vocab().copy()
    468 new_idx = len(current_vocab)  # only call this once, len gives the last index + 1
    469 for token in new_tokens:

File ~/project/search_engine/text-generation-webui/installer_files/env/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:1677, in PreTrainedTokenizerBase.get_vocab(self)
   1667 def get_vocab(self) -> Dict[str, int]:
   1668     """
   1669     Returns the vocabulary as a dictionary of token to index.
   1670 
   (...)
   1675         `Dict[str, int]`: The vocabulary.
   1676     """
-> 1677     raise NotImplementedError()

NotImplementedError: 

Seems to be a similar issue to #154.

Any suggestions on how to resolve this would be appreciated.

[mgorenstein]

Downgrading transformers fixed this for me, pip install --upgrade transformers==4.33

[Michielo1]

Just for any future folks stumbling upon this issue (as did I);

pip install --upgrade transformers==4.33 works as expected but does bring the outdated transformers with it (pretty obvious). I ended up just upgrading transformers to latest and using the ctransformers package for the AutoModelForCausalLM and using the transformers package for the AutoTokenizer