SageMaker inference endpoint with HuggingFaceModel ignores custom inference.py script

Hello, I'm trying do deploy a HuggingFaceModel using sagemaker inference endpoint. I've been following some guides, e.g.: this one and this. My model of choice is Llama-2 fine-tuned on my own data. I've packed it and created a model.tar.gz which contains the following structure:

model.tar.gz/
├── config.json
├── generation_config.json
├── tokenizer.json
├── pytorch_model-00001-of-00003.bin
├── ... (other model files)
└── code/
  ├── inference.py
  └── requirements.txt

My inference.py script defines the functions model_fn and output_fn, with custom model loading and output parsing logic.

I've uploaded this model.tar.gz to the S3 bucket in model_s3_path.

During the sagemaker endpoint creation, I define my HuggingFaceModel as follows:

from sagemaker.huggingface import get_huggingface_llm_image_uri
from sagemaker.huggingface import HuggingFaceModel

llm_image = get_huggingface_llm_image_uri(
  "huggingface",
  version="0.9.3"
)

huggingface_model = HuggingFaceModel(
    model_data=model_s3_path,
    role=aws_role, 
    image_uri=llm_image,
    env={
      'HF_MODEL_ID': 'meta-llama/Llama-2-7b-hf',
      'SM_NUM_GPUS': '1',
      'MAX_INPUT_LENGTH': '2048',
      'MAX_TOTAL_TOKENS': '4096',
      'MAX_BATCH_TOTAL_TOKENS': '8192', 
      'HUGGING_FACE_HUB_TOKEN': "<my-hf-token>"
    }
)

And then I deploy the model:

huggingface_model.deploy(
    initial_instance_count=1,
    instance_type=inference_instance_type,
    endpoint_name=endpoint_name,
    container_startup_health_check_timeout=300
)

However, during the inference, the resulting endpoint model doesn't seem to use any of the functionality from inference.py, but rather sticks to all default methods. For instance, it still returns response as [{"generated_texts": model_response}] although my post-processing function (output_fn) should've changed the return type.

1. I've tried setting entry_point="inference.py" and source_dir="./code" during the HF model creation - the endpoint was not deploying at all.
2. Used env variable "SAGEMAKER_PROGRAM": "inference.py" - did not change the model's responses, functionality from inference.py still was ignored.
3. Tried various image_uri - did not change the endpoint's behaviour.