Load model from cloud storage
### Feature request

I would like to load models directly from GCS/AWS

Example if your model is on Google Cloud:

```python
   model = AutoModelForSequenceClassification.from_pretrained('gs://my_bucket/my_model')
   tokenizer = AutoTokenizer.from_pretrained('gs://my_bucket/my_model')
   pipe = pipeline('text-classification', model='gs://my_bucket/my_model')
```

The `datasets` library supports this use case through `fsspec`. I propose to also use this library.

This could also simplify the code of `PretrainedConfig` as everything would use `fsspec` except if it's on the Hub.

### Motivation

I train my models in the cloud and then I load them into another tool such as Azimuth or a local Jupyter notebook for error analysis. 

I could simply upload them to the Hub, but I don't want to upload all my models to the Hub or manually upload them.

### Your contribution

I would be happy to submit a PR, might require some help.

Thanks!
Thanks for opening an issue. There is no plan to support any other platform than the Hugging Face Hub for remote models.<|||||>That's fair.

Just saw the notice around the deprecation of remote model using HTTP as well.

Closing :) 