Grok is an innovative machine learning toolkit designed to enhance the efficiency and effectiveness of model training and evaluation. By providing advanced tools for data processing, performance measurement, and result visualization, Grok simplifies the complexities involved in developing and fine-tuning machine learning models.
The primary aim of Grok is to offer a streamlined workflow for machine learning practitioners, enabling them to achieve better model performance with less manual effort. Whether you're conducting research, developing predictive models, or exploring new machine learning techniques, Grok provides the necessary tools to accelerate your work.
To install Grok, pip to install the project in editable mode. This allows you to modify the Grok code on-the-fly and have the changes reflected immediately.
pip install -e .To start training a model using Grok, simply run the train.py script located in the scripts directory. This script is designed to be flexible, supporting various training configurations and model types.
./scripts/train.py You can customize the training process by adjusting the script's parameters according to your needs.
For more detailed examples and guides on how to use Grok's features, please refer to the Jupyter notebooks located in the nbs folder. These notebooks provide in-depth tutorials and examples on how to use Grok for a wide range of machine learning tasks.
Contributions to Grok are welcome! If you have ideas for improvements or encounter any issues, please feel free to open an issue or submit a pull request.
Grok is licensed under the MIT License. For more information, see the LICENSE file in this repository.
We'd like to thank all the contributors to the Grok project, as well as the open-source community for providing the tools and libraries that make projects like this possible.