Deploy falcon 7b problems
Hey, I’m trying to deploy falcon 7b model via SageMaker.At first I was trying to use the “Deploy Amazon Sage Maker” code fromhttps://huggingface.co/tiiuae/falcon-7b:import sagemaker
import boto3
from sagemaker.huggingface import HuggingFaceModel

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',
	'HF_TASK':'text-generation'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.26.0',
	pytorch_version='1.13.1',
	py_version='py39',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	"inputs": "Can you please let us know more details about your ",
})Then i got this error:ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from primary with message "{
  "code": 400,
  "type": "InternalServerException",
  "message": "Loading /.sagemaker/mms/models/tiiuae__falcon-7b requires you to execute the configuration file in that repo on your local machine. Make sure you have read the code there to avoid malicious use, then set the option `trust_remote_code\u003dTrue` to remove this error."
}Then i was trying to create inference endpoint in other way - using custom inference.py i’ve created:from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_name = 'tiiuae/falcon-7b'


def model_fn(model_dir):
    
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    pipeline = transformers.pipeline(
    "text-generation",
    model=model_name,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
    return pipeline, tokenizer


def predict_fn(data, pipeline_and_tokenizer):
    pipeline, tokenizer = pipeline_and_tokenizer
    sequences = pipeline(
    data,
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
    return sequencesAfter deploying it using this code:from sagemaker.huggingface.model import HuggingFaceModel

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
   model_data=model_url,      # path to your model and script
   role=role,                    # iam role with permissions to create an Endpoint
   transformers_version="4.26.0",  # transformers version used
   pytorch_version="1.13.1",       # pytorch version used
   py_version='py39',            # python version used
   model_server_workers=1
)

model_name = 'falcon7bmodel'
# deploy the endpoint endpoint
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.g5.2xlarge",
    endpoint_name=model_name + "endpoint",
    )I was trying to predict and got this error:File "/opt/conda/lib/python3.9/site-packages/transformers/pipelines/text_generation.py", line 205, in preprocess 
prefix + prompt_text, padding=False, add_special_tokens=False, return_tensors=self.framework
TypeError: can only concatenate str (not "dict") to strDoes someone deployed falcon-7b successfully and can help me here to understand what i’m doing wrong here?Thanks!
2023-06-05T11:28:49Z
I have also tried to deploy falcon-7b model in Sagemaker and keep having the same error (trust_remote_code = True). Any help would be appreciated!
2023-06-05T11:32:02Z
cc@philschmid
2023-06-06T07:24:58Z
@Smart2233it seems that youpredict_fnis incorrectdatawill be a dictionary with “inputs”, seeDeploy FLAN-UL2 20B on Amazon SageMakerfor how to fix it.@cw-franabellanthe code@Smart2233should work with the change in theinfernece.py
2023-06-06T07:49:09Z
@philschmidThanks for the answer! extracting inputs from data did fix the issue.Although I still not sure why my first approach did not worked.(trust_remote_code error)