Hello, I see I can use a conversational model either via pipelines or via the InferenceClient. I would like to know best practice and if there is a recommendation on what to use from these two. The third way (via transformers - model and tokenizer) is harder and I dont want to use that. Thank you!
from transformers import pipeline, Conversation converse = pipeline("conversational") conversation_1 = Conversation("Going to the movies tonight - any suggestions?") converse([conversation_1])
from huggingface_hub import InferenceClient client = InferenceClient() output = client.conversational("Going to the movies tonight - any suggestions?") response = output['generated_text'] print(response) # This will work once a PR is merged # https://github.com/huggingface/huggingface_hub/pull/1770 default_model = client.get_recommended_model() print(default_model)