However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:

model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)

Load your custom configuration attributes into the model:

from transformers import TFDistilBertModel
my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
tf_model = TFDistilBertModel(my_config)

This creates a model with random values instead of pretrained weights.