hi, community!
I have trained a Bert classification model with below config on sagemaker:
from sagemaker.huggingface import HuggingFace

# hyperparameters, which are passed into the training job
hyperparameters={'epochs': 5,
                 'train_batch_size': 4,
                 'model_name':'bert-base-uncased',
                 'num_labels':31,
                 }

huggingface_estimator = HuggingFace(entry_point='train.py',
                            source_dir='./scripts',
                            instance_type='ml.p3.16xlarge',
                            instance_count=1,
                            role=role,
                            # add volume size set up
                            volume_size=200,
                            transformers_version='4.6',
                            pytorch_version='1.7',
                            py_version='py36',
                            hyperparameters = hyperparameters)
# starting the train job with our uploaded datasets as input
huggingface_estimator.fit({'train': training_input_path, 'test': test_input_path})

this works perfectly. however, I have two problems:

the generated model file is huge: 64gb. why is that?
I can’t deploy endpoint successfully, I am guessing due to the model size too big, any idea on how to solve?