Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- HuggingFaceFW/fineweb-2
+language:
+- ru
+metrics:
+- character
+base_model:
+- meta-llama/Llama-3.3-70B-Instruct
+new_version: meta-llama/Llama-3.3-70B-Instruct
+pipeline_tag: translation
+library_name: adapter-transformers
+---
+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':'issai/LLama-3.1-KazLLM-1.0-8B',
+	'output_dir':'/opt/ml/model'
+	# add your remaining hyperparameters
+	# more info here https://github.com/huggingface/transformers/tree/v4.37.0/examples/pytorch/seq2seq
+}
+
+# git configuration to download our fine-tuning script
+git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.37.0'}
+
+# creates Hugging Face estimator
+huggingface_estimator = HuggingFace(
+	entry_point='run_translation.py',
+	source_dir='./examples/pytorch/seq2seq',
+	instance_type='ml.p3.2xlarge',
+	instance_count=1,
+	role=role,
+	git_config=git_config,
+	transformers_version='4.37.0',
+	pytorch_version='2.1.0',
+	py_version='py310',
+	hyperparameters = hyperparameters
+)
+
+# starting the train job
+huggingface_estimator.fit()