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
Prerequisites: #216, #217, #218, #219
After adding the new parameters to the model saving and loading fuctionality, we now need to add them to the model upload functions:
Add the "task" , and "version" parameters to the
[ ] TE_MODEL_DOWNLOADER_UDF
[ ] upload_model cli
[ ] change the tests for TE_MODEL_DOWNLOADER_UDF and upload_model cli accordingly. Potentially add new tests for new parameters
prerequisites 1, 2, 3, 4
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.
Prerequisites: #216, #217, #218, #219
After adding the new parameters to the model saving and loading fuctionality, we now need to add them to the model upload functions:
Add the "task" , and "version" parameters to the
[ ] TE_MODEL_DOWNLOADER_UDF
[ ] upload_model cli
[ ] change the tests for TE_MODEL_DOWNLOADER_UDF and upload_model cli accordingly. Potentially add new tests for new parameters