I'm trying to deploy jina-clip v2 on sagemaker (using huggingface, not jumpstart). I've tried to find examples on how to do it, but I haven't been able to find anything for jina-clip. Below is my code:

import sagemaker
import json
from sagemaker.huggingface import HuggingFaceModel

# Initialize SageMaker session and role
sess = sagemaker.Session()
role = sagemaker.get_execution_role()

model_kwargs = {"device": "cpu", "trust_remote_code":"True"}
encode_kwargs = {"normalize_embeddings": True}

# Define model configuration
model_id = "jinaai/jina-clip-v2"  # Model ID for Jina CLIP v2 from Hugging Face
instance_type = "ml.g5.xlarge"     # Choose an appropriate instance type

# Define environment variables
config = {
    'HF_MODEL_ID': model_id,
    'HF_TASK': 'feature-extraction',  # Define the task
    'HF_MODEL_TRUST_REMOTE_CODE': json.dumps(True)                    # Allow remote code execution
}

# Create HuggingFaceModel
clip_model = HuggingFaceModel(
    role=role,
    env=config,
    transformers_version='4.37.0',
    pytorch_version='2.1.0',
    py_version='py310'
)

# Deploy the model to an endpoint
clip_endpoint = clip_model.deploy(
    initial_instance_count=1,
    instance_type=instance_type,
)

# Prepare data for prediction (example)
data = {
    "model": "jina-clip-v2",
    "dimensions": 1024,
    "normalized": True,
    "embedding_type": "float",
    "input": [
        {"text": "A beautiful sunset over the beach"},
        {"text": "Un beau coucher de soleil sur la plage"},
        {"text": "海滩上美丽的日落"},
        {"text": "浜辺に沈む美しい夕日"},
        {"image": ".jpg"},
        {"image": ".jpg"},
        {
            "image": "R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7"
        }
    ]
}

# Make a prediction
res = clip_endpoint.predict(data=data)

# Print results
print(res)

# Clean up the endpoint after use
sagemaker.Session().delete_endpoint(clip_endpoint.endpoint_name)

However, I'm getting the following 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/jinaai__jina-clip-v2 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."
}

I tried adding HF_MODEL_TRUST_REMOTE_CODE to the config but didn't see any change. Any help would be appreciated!