MultiModal Prompting

This repository explores innovative multimodal prompting strategies using LLama 3.1/3.2 models. It introduces new capabilities such as tokenizer customization, API stack integration, and tool calling. The ultimate goal is to leverage these technologies to create a ballet assistant tailored for specific literature and expertise.

Table of Contents

• Highlights and Work in Progress
• Features
• Setup and Installation
• Usage
• Dependencies
• Future Enhancements
• Acknowledgments
• License

Highlights and Work in Progress

Build a Spanish Tokenizer

• Custom tokenizer for improved language understanding and generation in Spanish.

Develop a Statistical Literature Review Tool

• Create an agent for statistical literature review and selection.
• Build a tutor for statistical concepts, supporting advanced academic workflows.

Fine-Tune LLama Model

• Adapt the model to the specific needs of a ballet school and its literature.

Features

Multimodal Prompting

Utilize LLama 3.1/3.2's advanced multimodal capabilities for creative applications.

Custom Tool-Calling

Implement tool-calling strategies to enhance interactivity and functionality.

Fine-Tuning for Domain-Specific Tasks

Train models on custom datasets for niche applications (e.g., ballet-specific content).

Powered by Together.ai

Use together.ai endpoints for model integration and API calls.

Setup and Installation

Prerequisites

• Python 3.8 or newer.
• A package manager like pip or conda.
• Access to the Together.ai API.

Installation Steps

1. Clone the repository:

   git clone [https://github.com/AMorQ/MultiModal_LLM.git](https://github.com/AMorQ/MultiModal_LLM.git)
   cd MultiModal_LLM

2. Create and activate the environment:

   conda env create -f environment.yaml
   conda activate multimodal_env

3. Install additional requirements (if needed):

   pip install -r requirements.txt

Usage

Exploring Notebooks

Open the Jupyter notebooks (.ipynb) in the repository to explore multimodal prompting experiments.

Fine-Tuning

Use the scripts provided to fine-tune the LLama model with your specific dataset (e.g., ballet school literature).

Custom Tokenizer

Experiment with the Spanish tokenizer to enhance language-specific tasks.

Tool Calling

Test and develop the statistical literature review functionalities and other agent-based tools.

Dependencies

The repository relies on the following key libraries and tools:

• LLama 3.1/3.2 Models
• Together.ai API
• Python Libraries:
  • TensorFlow
  • NumPy
  • Pandas
• Jupyter Notebooks

Refer to the environment.yaml or requirements.txt for a full list of dependencies.

Future Enhancements

• Expand the ballet assistant to include interactive choreography suggestions.
• Add support for additional languages in the tokenizer.
• Integrate GPT-based models for enhanced multimodal capabilities.
• Publish statistical literature review tools as standalone utilities.

Acknowledgments

Special thanks to Together.ai for providing endpoints for LLama models and enabling advanced experimentation in this project.