Ultron-11B

Ultron-Summarizer-8B Model Card

🏠 Homepage | 💻 Github | 📄 Arxiv | 📕 PDF

List of Provided Model Series

• Ultron-Summarizer-Series: 🤖 Ultron-Summarizer-1B | 🤖 Ultron-Summarizer-3B | 🤖 Ultron-Summarizer-8B
• Ultron 7B: 🤖 Ultron-7B | 🤖 Ultron-11B

🚨 Disclaimer: All models and datasets are intended for research purposes only.

Model Description

• Repository: Code
• Paper: Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge
• Point of Contact: Young-Jun Lee

Model Details

• Model: Ultron-11B is a fully open-source multi-modal conversation model that generates the most appropriate image description at the image-sharing moment.
• Date: Ultron-11B was trained in 2024.
• Training Dataset: Stark-Dialogue
• Architecture: Ultron-11B was trained on top of LLaMA-3.2-11B-Vision.

How to Use

import logging
from PIL import Image
import torch
from transformers import (
    AutoModelForVision2Seq,
    BitsAndBytesConfig,
    AutoProcessor,
)

# Define Ultron template
ULTRON_TEMPLATE = 'You are an excellent image sharing system that generates <RET> token with the following image description. The image description must be provided with the following format: <RET> <h> image description </h>. The following conversation is between {name} and AI assistant on {date}. The given image is {name}\'s appearance.\n{dialogue}'

# Ultron model initialization
def load_ultron_model(model_path):
    """
    Loads the Ultron model and processor.

    Args:
        model_path (str): Path to the pre-trained model.

    Returns:
        model: Loaded Vision-to-Seq model.
        processor: Corresponding processor for the model.
    """
    logging.info(f"Loading Ultron model from {model_path}...")
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type='nf4'
    )
    model_kwargs = dict(
        torch_dtype=torch.bfloat16,
        low_cpu_mem_usage=True,
        trust_remote_code=True,
        device_map="auto",
    )
    processor = AutoProcessor.from_pretrained(
        'meta-llama/Llama-3.2-11B-Vision-Instruct', torch_dtype=torch.bfloat16
    )
    model = AutoModelForVision2Seq.from_pretrained(
        model_path, 
        **model_kwargs
    ).eval()
    logging.info("Ultron model loaded successfully.")
    return model, processor

# Run Ultron model
def run_ultron_model(model, processor, dialogue, name='Tom', date='2023.04.20', face_image_path='sample_face.png'):
    """
    Runs the Ultron model with a given dialogue, name, and image.

    Args:
        model: Pre-trained model instance.
        processor: Processor for model input.
        dialogue (str): Input dialogue for the assistant.
        name (str): Name of the user.
        date (str): Date of the conversation.
        face_image_path (str): Path to the face image file.

    Returns:
        str: Description of the shared image.
    """
    logging.info("Running Ultron model...")
    face_image = Image.open(face_image_path).convert("RGB")

    prompt = ULTRON_TEMPLATE.format(
        dialogue=dialogue,
        name=name,
        date=date
    )
    messages = [
        {
            "content": [
                {"text": prompt, "type": "text"},
                {"type": "image"}
            ], 
            "role": "user"
        },
    ]

    logging.info("Preparing input for Ultron model...")
    prompt_input = processor.apply_chat_template(messages, add_generation_prompt=True)
    inputs = processor(face_image, prompt_input, return_tensors='pt').to('cuda')

    with torch.inference_mode():
        logging.info("Generating output from Ultron model...")
        output = model.generate(
            **inputs, 
            do_sample=True,
            temperature=0.9,
            max_new_tokens=512,
            top_p=1.0,
            use_cache=True,
            num_beams=1,
        )
    
    output_text = processor.decode(output[0], skip_special_token=True)
    logging.info("Output generated successfully from Ultron model.")
    return parse_ultron_output(output_text)

# Parse Ultron output
def parse_ultron_output(output):
    """
    Parses the output to extract the image description.

    Args:
        output (str): The generated output text from the model.

    Returns:
        str: Extracted image description.
    """
    logging.info("Parsing output from Ultron model...")
    if '<RET>' in output:
        return output.split('<h>')[-1].split('</h>')[0].strip()
    else:
        logging.warning("<RET> not found in output.")
        return output

# Example usage
def main():
    """
    Example usage of Ultron model.
    """
    model_path = "passing2961/Ultron-11B"
    model, processor = load_ultron_model(model_path)

    dialogue = """Tom: I have so much work at the office, I'm exhausted...
    Personal AI Assistant: How can I help you feel less tired?
    Tom: Hmm.. I miss my dog Star at home.
    Personal AI Assistant: """
    
    image_description = run_ultron_model(model, processor, dialogue)
    logging.info(f"Image description generated: {image_description}")

if __name__ == "__main__":
    main()

License and Recommendations

🚨 Ultron-11B is intended to be used for research purposes only.

Acknowledgement

This work was supported by a grant of the KAIST-KT joint research project through AI Tech Lab, Institute of convergence Technology, funded by KT [Project No. G01230605, Development of Task-oriented Persona-based Dialogue Generation Combining Multi-modal Interaction and Knowledge Modeling].

Citation

If you find the resources in this repository useful, please cite our work:

@article{lee2024stark,
  title={Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge},
  author={Lee, Young-Jun and Lee, Dokyong and Youn, Junyoung and Oh, Kyeongjin and Ko, Byungsoo and Hyeon, Jonghwan and Choi, Ho-Jin},
  journal={arXiv preprint arXiv:2407.03958},
  year={2024}
}