---
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()