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--- |
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license: apache-2.0 |
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datasets: |
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- HuggingFaceFW/fineweb-2 |
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language: |
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- ru |
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metrics: |
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- character |
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base_model: |
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- meta-llama/Llama-3.3-70B-Instruct |
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new_version: meta-llama/Llama-3.3-70B-Instruct |
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pipeline_tag: translation |
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library_name: adapter-transformers |
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--- |
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import sagemaker |
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import boto3 |
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from sagemaker.huggingface import HuggingFace |
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try: |
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role = sagemaker.get_execution_role() |
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except ValueError: |
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iam = boto3.client('iam') |
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role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn'] |
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hyperparameters = { |
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'model_name_or_path':'issai/LLama-3.1-KazLLM-1.0-8B', |
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'output_dir':'/opt/ml/model' |
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# add your remaining hyperparameters |
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# more info here https://github.com/huggingface/transformers/tree/v4.37.0/examples/pytorch/seq2seq |
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} |
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# git configuration to download our fine-tuning script |
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git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.37.0'} |
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# creates Hugging Face estimator |
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huggingface_estimator = HuggingFace( |
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entry_point='run_translation.py', |
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source_dir='./examples/pytorch/seq2seq', |
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instance_type='ml.p3.2xlarge', |
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instance_count=1, |
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role=role, |
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git_config=git_config, |
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transformers_version='4.37.0', |
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pytorch_version='2.1.0', |
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py_version='py310', |
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hyperparameters = hyperparameters |
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) |
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# starting the train job |
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huggingface_estimator.fit() |
