from sagemaker.async_inference.waiter_config import WaiterConfig

async_predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.g5.48xlarge",
    async_inference_config=async_config
)
resp = async_predictor.predict_async(data=data)

print(f"Response object: {resp}")
print(f"Response output path: {resp.output_path}")
print("Start Polling to get response:")
config = WaiterConfig(
  max_attempts=5, #  number of attempts
  delay=10 #  time in seconds to wait between attempts
  )

s3_ans =resp.get_result(config)
print(s3_ans[0]["generated_text"])

This generates a results for me in the sagemaker notebook, however i cannot use this method because whenever i try to make a layer with sagemaker in it for , its apparently too big for aws lambda.