MathMate | Multi-Modal AI for Mathematical Learning

Capstone project

MathMate | Multi-Modal AI for Mathematical Learning

This capstone project aims to develop a multi-modal AI assistant that integrates computer vision, natural language processing, and speech recognition technologies. The system will be accessible through a user-friendly chatbot interface, allowing users to interact with it using text, images, or voice inputs.

Installation

In order to set up the necessary environment:

1. review and uncomment what you need in environment.yml and create an environment Capstone_project with the help of conda:

   conda env create -f environment.yml
   

2. activate the new environment with:

   conda activate Capstone_project
   

NOTE: The conda environment will have Capstone_project installed in editable mode. Some changes, e.g. in setup.cfg, might require you to run pip install -e . again.

Optional and needed only once after git clone:

3. install several pre-commit git hooks with:

   pre-commit install
   # You might also want to run `pre-commit autoupdate`

   and checkout the configuration under .pre-commit-config.yaml. The -n, --no-verify flag of git commit can be used to deactivate pre-commit hooks temporarily.

4. install nbstripout git hooks to remove the output cells of committed notebooks with:

   nbstripout --install --attributes notebooks/.gitattributes

   This is useful to avoid large diffs due to plots in your notebooks. A simple nbstripout --uninstall will revert these changes.

Then take a look into the scripts and notebooks folders.

Dependency Management & Reproducibility

1. Always keep your abstract (unpinned) dependencies updated in environment.yml and eventually in setup.cfg if you want to ship and install your package via pip later on.
2. Create concrete dependencies as environment.lock.yml for the exact reproduction of your environment with:

   conda env export -n Capstone_project -f environment.lock.yml

   For multi-OS development, consider using --no-builds during the export.
3. Update your current environment with respect to a new environment.lock.yml using:

   conda env update -f environment.lock.yml --prune

4. Hope to create marimo notebooks with uvx commands which will help in isolated environments (dependency-related issues) and smooth running of notebooks.

Project Organization

├── AUTHORS.md              <- List of developers and maintainers.
├── CHANGELOG.md            <- Changelog to keep track of new features and fixes.
├── CONTRIBUTING.md         <- Guidelines for contributing to this project.
├── Dockerfile              <- Build a docker container with `docker build .`.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yml         <- The conda environment file for reproducibility.
├── models                  <- Trained and serialized models, model predictions,
│                              or model summaries.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── pyproject.toml          <- Build configuration. Don't change! Use `pip install -e .`
│                              to install for development or to build `tox -e build`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- [DEPRECATED] Use `python setup.py develop` to install for
│                              development or `python setup.py bdist_wheel` to build.
├── src
│   └── capstone_project    <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `pytest`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

TODO:

1. Model Merging

• Set up Lightning AI's Efficient Linear Model Merging environment
• Download and prepare NuminaMath-7B-TIR.q6_k model
• Download and prepare Qwen2-Math-7B-Instruct-Q6_K.gguf model
• Research merging methodologies in mergekit repo
• Select appropriate merging method for math-oriented models
• Execute model merging process
• Save and document the merged model

2. Model Evaluation

• Set up llm-autoeval environment
• Define evaluation metrics focusing on mathematical capabilities
• Prepare evaluation datasets
• Run evaluation on the merged model
• Document baseline performance metrics

3. Fine-tuning Preparation

• Gather and prepare datasets for fine-tuning
• Set up development environment for various fine-tuning techniques

4. ORPOO Fine-tuning

• Set up ORPOO fine-tuning environment
• Prepare model and data for ORPOO
• Execute ORPOO fine-tuning
• Evaluate model post-ORPOO
• Document results and improvements

5. Axolotl Fine-tuning

• Set up Axolotl environment
• Prepare model and data for Axolotl
• Configure Axolotl parameters
• Run Axolotl fine-tuning
• Evaluate model post-Axolotl
• Document results and improvements

Relevant diagrams:

Flow diagram:

Mindmap:

Note

This project has been set up using PyScaffold 4.5 and the dsproject extension 0.7.2.