Sagemaker Deployment Issues: TypeError: Not A String

Trying to get codegen25-7b-multi to launch on Sagemaker and hitting issues trying to launch on 2xlarge, 8xlarge and 12xlarge instances. All are throwing the same errors:

Error #1

#033[2m2023-08-04T15:30:16.074558Z#033[0m #033[31mERROR#033[0m #033[1mshard-manager#033[0m: #033[2mtext_generation_launcher#033[0m#033[2m:#033[0m Error when initializing model

Error #2

File "/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py", line 124, in serve_inner
    model = get_model(model_id, revision, sharded, quantize, trust_remote_code)
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/__init__.py", line 237, in get_model
    return FlashLlamaSharded(
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/flash_llama.py", line 166, in __init__
    tokenizer = LlamaTokenizer.from_pretrained(
  File "/usr/src/transformers/src/transformers/tokenization_utils_base.py", line 1812, in from_pretrained
    return cls._from_pretrained(
  File "/usr/src/transformers/src/transformers/tokenization_utils_base.py", line 1975, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
  File "/usr/src/transformers/src/transformers/models/llama/tokenization_llama.py", line 96, in __init__
    self.sp_model.Load(vocab_file)
  File "/opt/conda/lib/python3.9/site-packages/sentencepiece/__init__.py", line 905, in Load
    return self.LoadFromFile(model_file)
  File "/opt/conda/lib/python3.9/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
    return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)

Error #3

TypeError: not a string
 #033[2m#033[3mrank#033[0m#033[2m=#033[0m0#033[0m

Error #4

#033[2m2023-08-04T15:30:16.396605Z#033[0m #033[31mERROR#033[0m #033[2mtext_generation_launcher#033[0m#033[2m:#033[0m Shard 1 failed to start:

Notebook for deployment in Sagemaker:

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':'Salesforce/codegen25-7b-multi',
	'SM_NUM_GPUS': '2',
    'HF_API_TOKEN':'<TOKEN>'
}

# 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.g4dn.12xlarge",
	container_startup_health_check_timeout=300,
    endpoint_name="codegen25",
    model_name="codegen25"
  )