readme: import sagemaker
  from sagemaker.huggingface import HuggingFace
  
  # gets role for executing training job
  role = sagemaker.get_execution_role()
  hyperparameters = {
  	'model_name_or_path':'Grossmend/rudialogpt3_medium_based_on_gpt2',
  	'output_dir':'/opt/ml/model'
  	# add your remaining hyperparameters
  	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling
  }
  
  # git configuration to download our fine-tuning script
  git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}
  
  # creates Hugging Face estimator
  huggingface_estimator = HuggingFace(
  	entry_point='run_clm.py',
  	source_dir='./examples/pytorch/language-modeling',
  	instance_type='ml.p3.2xlarge',
  	instance_count=1,
  	role=role,
  	git_config=git_config,
  	transformers_version='4.17.0',
  	pytorch_version='1.10.2',
  	py_version='py38',
  	hyperparameters = hyperparameters
  )
  
  # starting the train job
  huggingface_estimator.fit()