README.md

import sagemaker import boto3 from sagemaker.huggingface import HuggingFace

try: role = sagemaker.get_execution_role() except ValueError: iam = boto3.client('iam') role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']

hyperparameters = { 'model_name_or_path':'TheBloke/dolphin-2.5-mixtral-8x7b-GGUF', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.26.0/examples/pytorch/question-answering }

git configuration to download our fine-tuning script

git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.26.0'}

creates Hugging Face estimator

huggingface_estimator = HuggingFace( entry_point='run_qa.py', source_dir='./examples/pytorch/question-answering', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.26.0', pytorch_version='1.13.1', py_version='py39', hyperparameters = hyperparameters )

starting the train job

huggingface_estimator.fit()