Video Chat T P O

Introduction
VideoChat-TPO is a multimodal large language model designed to improve vision task alignment through Task Preference Optimization. It processes video and text inputs to generate textual outputs, making it suitable for video-text-to-text applications.

Architecture
The model is built on the Mistral-7B-Instruct-v0.2 architecture and utilizes the transformers library. It leverages advanced techniques, including safetensors for feature extraction, to efficiently handle video-text inputs.

Training
VideoChat-TPO was trained with a focus on aligning vision tasks with language models to optimize multimodal interactions. The training process is detailed in the associated paper, "Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment."

Guide: Running Locally

1. Installation:
   • Clone the repository and install dependencies:

     pip install -r requirements.txt
     

   • Run the application:

     python app.py
     

2. Usage:
   • Import necessary modules and load the model:

     from transformers import AutoModel, AutoTokenizer
     from tokenizer import MultimodalLlamaTokenizer
     
     model_path = "OpenGVLab/VideoChat-TPO"
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
     model = AutoModel.from_pretrained(model_path, trust_remote_code=True, _tokenizer=tokenizer).eval()
     

3. Cloud GPU Recommendation:
   • For optimal performance, consider using cloud GPUs from providers such as AWS, Google Cloud, or Azure.

License
The VideoChat-TPO model is released under the MIT License, allowing for open usage and modification.