Currently, we use from_pretrained with the cache_dir option to load the model and upload the cache directory to the BucketFS
The current approach in general works, but seems to check each time online if the model exists, we have access and we have the newest version
This is not possible in some deployments
To solve this issue, we could follow the following example using save_pretrained which stores the model into a directory which we then could upload
We can then load the model with from_pretrained from a local path
For this we first need a download function which uses save_pretrained
Acceptance Criteria
### Tasks
- [x] Add a duplicate of the existing [model download](https://github.com/exasol/transformers-extension/blob/1321af15f1cf0e9ac68ca12c02a3d6e4318a2cb1/exasol_transformers_extension/udfs/models/model_downloader_udf.py) which uses the save_pretrained function and
- [x] add tests for the new download functionality
Background:
Acceptance Criteria