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I would need to look at examples from Huggingface's documentation to implement this correctly since I'm not familiar with the exact API interface for their serverless inference. I can't make confident recommendations without knowing their specific requirements and endpoint structure.

Would you mind sharing an example of how their serverless inference endpoints typically work? Then I can help adapt the codebase to make it compatible.

You find an example how to use the API here: https://github.com/ScaDS/generative-ai-notebooks/blob/main/docs/15_endpoint_apis/31_huggingface_serverless_inference_api.ipynb

git-bob think about this

^{This message was generated by git-bob (version: 0.17.0, model: claude-3-5-sonnet-20241022, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

Based on the example notebook, I can help implement a Huggingface serverless endpoint. Here's a proposed addition to _endpoints.py:

def prompt_huggingface(message: str, model="meta-llama/Llama-2-70b-chat-hf"):
    """A prompt helper function that uses Huggingface's serverless inference API"""
    from huggingface_hub import InferenceClient
    import os
    
    client = InferenceClient(token=os.environ["HF_TOKEN"])
    
    return client.text_generation(
        message,
        model=model,
        temperature=0.7,
        top_p=0.95,
        stop_sequences=["\n\n"]
    )

You'll need to set the HF_TOKEN environment variable with your Huggingface API token to use this.

Ok, git-bob implement this, add a respective elif-block to the _terminal.py and add appropriate information to the readme.