Sagemaker hugging face deployment issue:

hi, good afternoon, i deployed the deepseek-ai/deepseek-coder-7b-instruct model on sagemaker with the same config as your demo on hugging face like tok_p 0.9 and top_k 50, i assume the temprature is 0.6, if it is not please tell me the one you use, do_sample as false, it is running fine, but if i try a prompt on your demo, it gives correct and accurate result, but if i prompt the one i deployed it doesn't give me as accurate result with thesame prompt, please is there any tweak that you did there and you can share it with me, please i need your help. thanks. @chester please respond to this.
and please could it be that there is "deepseek-ai/deepseek-coder-7b-instruct" and also "deepseek-ai/deepseek-coder-7b-chat"
?

and please what is the stop token, because even if i use "stop":[<|EOT|>], it still keep generating until the max_new_token is exhausted.
here is how i am deploying to 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':'deepseek-ai/deepseek-coder-6.7b-instruct',
'SM_NUM_GPUS': json.dumps(1)
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
image_uri=get_huggingface_llm_image_uri("huggingface",version="1.1.0"),
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": "My name is Julien and I like to",
"parameters": {
"do_sample": False,
"top_p": 0.90,
"top_k": 50,
"temperature": 0.35,
"max_new_tokens": 1024,
"repetition_penalty": 1.0,
"stop": ["<|EOT|>"]
}
})`