cd /path/to/parent directory of project folder
git clone git@hf.co:USERNAME/myHFrepo

cd myHFrepo
python -m venv jointvenv
source jointvenv/bin/activate

cat /path/to/myHFrepo/Cookiecutter-MLOps/.gitattributes >> /path/to/myHFrepo/.gitattributes
rm /path/to/myHFrepo/Cookiecutter-MLOps/.gitattributes
git add .gitattributes
git commit -m "Concatenate .gitattributes info from DagsHub/Cookiecutter-MLOps"

mv /path/to/myHFrepo/Cookiecutter-MLOps/README.md /path/to/myHFrepo/README.md
git add README.md
git commit -m "Get README info from DagsHub/Cookiecutter-MLOps"

cd /path/to/myHFrepo/Cookiecutter-MLOps
mv * .[^.]* ..
cd /path/to/myHFrepo
rmdir /path/to/myHFrepo/Cookiecutter-MLOps

echo '' >> .gitignore
echo '#'Virtual Environment >> .gitignore
echo jointvenv/ >> .gitignore
git add .
git commit -m "add remaining DagsHub/Cookiecutter-MLOps repo content"

mv requirements.txt requirementsCookiecutter-MLOps.txt
git add requirementsCookiecutter-MLOps.txt
git commit -m "external requirements from Cookiecutter-MLOps"

pip freeze > requirements.txt
git add requirements.txt
git commit -m "First report venv requirements"

git remote add dcc git@hf.co:MYORG/mywslHFrepo
git pull dcc main --allow-unrelated-histories

git add .
git commit -m "Merge HuggingFace individual and organization repos"
git push dcc main

├── LICENSE
├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump
│
├── 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 short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│   └── metrics.txt    <- Relevant metrics after evaluating the model.
│   └── training_metrics.txt    <- Relevant metrics from training the model.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   ├── great_expectations  <- Folder containing data integrity check files
│   │   ├── make_dataset.py
│   │   └── data_validation.py  <- Script to run data integrity checks
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
├── .pre-commit-config.yaml  <- pre-commit hooks file with selected hooks for the projects.
├── dvc.lock           <- constructs the ML pipeline with defined stages.
└── dvc.yaml           <- Traing a model on the processed data.