help me fix this python error I don't understand:

from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceInstructEmbeddings

Output_folder = './mal-vectordb'
embeddings_model_repo = 'sentence-transformers/all-MiniLM-L6-v2'
### we create the embeddings only if they do not exist yet
if not os.path.exists(Output_folder):
    ### download embeddings model
    embeddings = HuggingFaceInstructEmbeddings(
        model_name = embeddings_model_repo,
    )

    ### create embeddings and DB
    vectordb = FAISS.from_documents(
        documents = documents, 
        embedding = embeddings
    )

    ### persist vector database
    vectordb.save_local(f"{Output_folder}/faiss_index_mal") # save in output folder
else:
    ### download embeddings model
    embeddings = HuggingFaceInstructEmbeddings(
        model_name = embeddings_model_repo,
        #model_kwargs = {"device": "cuda"}
    )

    ### load vector DB embeddings
    vectordb = FAISS.load_local(
        f"{Output_folder}/faiss_index_mal", # from output folder
        embeddings,
        allow_dangerous_deserialization=True
    )

Output:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[10], line 9
      6 ### we create the embeddings only if they do not exist yet
      7 if not os.path.exists(Output_folder):
      8     ### download embeddings model
----> 9     embeddings = HuggingFaceInstructEmbeddings(
     10         model_name = embeddings_model_repo,
     11     )
     13     ### create embeddings and DB
     14     vectordb = FAISS.from_documents(
     15         documents = documents, 
     16         embedding = embeddings
     17     )

File /opt/conda/lib/python3.10/site-packages/langchain_community/embeddings/huggingface.py:190, in HuggingFaceInstructEmbeddings.__init__(self, **kwargs)
    187 try:
    188     from InstructorEmbedding import INSTRUCTOR
--> 190     self.client = INSTRUCTOR(
    191         self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
    192     )
    193 except ImportError as e:
    194     raise ImportError("Dependencies for InstructorEmbedding not found.") from e

File /opt/conda/lib/python3.10/site-packages/sentence_transformers/SentenceTransformer.py:294, in SentenceTransformer.__init__(self, model_name_or_path, modules, device, prompts, default_prompt_name, similarity_fn_name, cache_folder, trust_remote_code, revision, local_files_only, token, use_auth_token, truncate_dim, model_kwargs, tokenizer_kwargs, config_kwargs, model_card_data)
    285         model_name_or_path = __MODEL_HUB_ORGANIZATION__ + "/" + model_name_or_path
    287 if is_sentence_transformer_model(
    288     model_name_or_path,
    289     token,
   (...)
    292     local_files_only=local_files_only,
    293 ):
--> 294     modules, self.module_kwargs = self._load_sbert_model(
    295         model_name_or_path,
    296         token=token,
    297         cache_folder=cache_folder,
    298         revision=revision,
    299         trust_remote_code=trust_remote_code,
    300         local_files_only=local_files_only,
    301         model_kwargs=model_kwargs,
    302         tokenizer_kwargs=tokenizer_kwargs,
    303         config_kwargs=config_kwargs,
    304     )
    305 else:
    306     modules = self._load_auto_model(
    307         model_name_or_path,
    308         token=token,
   (...)
    315         config_kwargs=config_kwargs,
    316     )

TypeError: INSTRUCTOR._load_sbert_model() got an unexpected keyword argument 'token'