You can specify the tokenizer with the tokenizer
argument and do what is suggested in the error message.
Here is an example based on the documentation.
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModel.from_pretrained("gpt2")
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model.resize_token_embeddings(len(tokenizer))
pipe = pipeline('feature-extraction', model=model, tokenizer=tokenizer)
This works for me. Let me know if you have any other questions