Hey @JB2022,
we added support for conversational pipeline with a later release. Can you use instead of transformers_version="4.6" => 4.12 and for pytorch_version="1.7" => 1.9.
You can find the whole list of available containers here: Reference 1
Then your fist code snippet should work.
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'microsoft/DialoGPT-medium',
	'HF_TASK':'conversational'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.12',
	pytorch_version='1.9',
	py_version='py36',
	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': {
		"past_user_inputs": ["Which movie is the best ?"],
		"generated_responses": ["It's Die Hard for sure."],
		"text": "Can you explain why ?",
	}
})