ZoneTwelve/NCUEatingAI-0.5B-v1.1

NCUEatingAI-0.5B-v1

This repository provides an example of how to use the NCUEatingAI-0.5B-v1 large language model from Hugging Face for chat-based inference. The model can be customized to act like any persona you specify in the system prompt, and it generates conversational responses based on user inputs.

Model Information

• Model: ZoneTwelve/NCUEatingAI-0.5B-v1
• Size: 0.5 billion parameters
• Task: Conversational AI / Chatbot

Usage

System Prompt

You can set a system prompt to define how the model should behave during interactions. A simple example format is:

"You act like $USERNAME"

Where $USERNAME can be replaced with the desired persona (e.g., "a helpful assistant", "a curious learner", etc.).

Inference Example

Here’s a simple way to perform inference using the model. You’ll need to load the model and tokenizer, define the user and system prompts, and format the input using the apply_chat_template method.

Code Example

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

def chat_with_ncueatingai(
    model_path: str = "ZoneTwelve/NCUEatingAI-0.5B-v1",
    prompt: str = "What's for lunch?",
    system_prompt: str = "You act like a @ZoneTwelve.",
    max_tokens: int = 64,
):
    # Load the model and tokenizer
    model = AutoModelForCausalLM.from_pretrained(model_path)
    tokenizer = AutoTokenizer.from_pretrained(model_path)

    # Prepare the chat messages
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": prompt}
    ]

    # Apply chat template
    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

    # Tokenize inputs
    inputs = tokenizer(input_text, return_tensors="pt")

    # Generate response
    with torch.no_grad():
        outputs = model.generate(
            inputs.input_ids,
            max_length=max_tokens,
            pad_token_id=tokenizer.eos_token_id
        )

    # Decode the response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Example usage
if __name__ == "__main__":
    response = chat_with_ncueatingai(
        prompt="What's a healthy meal?",
        system_prompt="You act like a nutrition expert."
    )
    print("Model Response:", response)

Parameters

• model_path: The path or Hugging Face model hub identifier, default is "ZoneTwelve/NCUEatingAI-0.5B-v1".
• prompt: The user’s input prompt, which the model will respond to.
• system_prompt: Defines the behavior or persona of the model.
• max_tokens: The maximum number of tokens in the generated response.

Requirements

Ensure the following Python packages are installed:

pip install torch transformers

Model Download

You can download the model directly from Hugging Face using:

model = AutoModelForCausalLM.from_pretrained("ZoneTwelve/NCUEatingAI-0.5B-v1")
tokenizer = AutoTokenizer.from_pretrained("ZoneTwelve/NCUEatingAI-0.5B-v1")

License

This project is licensed under the terms of the MIT license. See LICENSE for details.

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Enjoy using NCUEatingAI-0.5B-v1 to build your personalized conversational AI!