I am not sure if this is still an issue, but I came across this at stackoverflow when looking for storing my own fine-tuned BERT model artifacts somewhere to use during the inference.
It seems helpful, and I am assuming adding
AutoTokenizer.from_pretrained(tokenizer.name, config=tokenizer_config.json)
would solve the tokenizer artifacts, but I feel like other artifacts(vocab.txt etc.) might be problematic to add with the code provided.
So, the simple solution appeared to me just creating a model repo in Huggingface Hub(might be obvious to the experienced eye, but was new to me) and calling your own pre-trained or fine-tuned models from there like
tokenizer = AutoTokenizer.from_pretrained("username/repo_name")
model = AutoModel.from_pretrained("username/repo_name").
I think one can just use pt_model.push_to_hub("my-awesome-model") documented here or using git commit/push as usual to do that.
Of course, this assumes that storing the artifacts in S3 is no must.