Downloading a model and saving it locally using AutoModel.from_pretrained loses the model specialization (detailed description in #213) . After loading these broken models, using them for prediction returns bad results.
We see only one option to solve this problem, and this is to specify the task during download of the model.
This means the download udf and the upload cli need an additional parameter for the task
The task should be part of the name of the uploaded model file, such that users can upload a model for multiple tasks
Furthermore, we should store the task inside the model archive file
To solve the testing issues, we need to make the prediction udfs deterministic
For this, we need to be able to download a fixed version of a model and set the seed inside the prediction udf.
This means we need a version parameter in download udf, prediction udf and model upload cli
The version should be part of the name of the uploaded model file, such that users can upload multiple versions
We need a seed parameter in the prediction udfs
we need to document the changes made for this before releasing. Check changes in #216, #217, #218, #219, #220, #221
Downloading a model and saving it locally using AutoModel.from_pretrained loses the model specialization (detailed description in #213) . After loading these broken models, using them for prediction returns bad results.
we need to document the changes made for this before releasing. Check changes in #216, #217, #218, #219, #220, #221