Hello everyone,
I have made a deployment of Falcon 7B instruct using the AWS sagemaker SDK code in the model’s page.
import json
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
try:
role = sagemaker.get_execution_role()
except ValueError:
iam = boto3.client('iam')
role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
'HF_MODEL_ID':'tiiuae/falcon-7b-instruct',
'SM_NUM_GPUS': json.dumps(1)
}
# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
image_uri=get_huggingface_llm_image_uri("huggingface",version="0.8.2"),
env=hub,
role=role,
)
# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
container_startup_health_check_timeout=300,
)
# send request
predictor.predict({
"inputs": "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?",
})
It is working just fine but the outputs are too short (just a couple of sentences). Where should the changes be made so that it managed to produce at least one paragraph ? Thank you in advance.