UNHAS-GPT

UNHAS-GPT (Universitas Hasanuddin - Generative Pre-trained Transformer) is an AI-powered natural language processing model developed by IMRAN ABU LIBDA.

Overview

UNHAS-GPT leverages state-of-the-art machine learning techniques, including transformer-based models, to understand and generate human-like text. It is specifically tailored to comprehend and respond to text in Bahasa Indonesia, making it a valuable tool for various applications, including education, business, and research.

Key Features

• Natural Language Understanding: UNHAS-GPT can understand and generate text in Bahasa Indonesia, providing accurate and contextually relevant responses.
• Knowledge Integration: UNHAS-GPT integrates a vast repository of textual data to enhance its understanding of language and context.
• Customizable: Developers can fine-tune and customize UNHAS-GPT for specific use cases and applications.
• Scalable: UNHAS-GPT is designed to scale horizontally to handle large volumes of text data and user requests.

Getting Started

To get started with UNHAS-GPT, follow these steps:

1. Clone the Repository: Clone this GitHub repository to your local machine.

   git clone https://github.com/3m0r9/unhas-gpt.git

2. Install Dependencies: Install the required dependencies using pip.

   pip install -r requirements.txt

3. Run the Model: Use the provided scripts to train, evaluate, and deploy the UNHAS-GPT model.

4. Explore the Documentation: Refer to the documentation for detailed instructions on using and customizing UNHAS-GPT for your needs.

Contributing

We welcome contributions from the community to improve UNHAS-GPT. If you have ideas for new features, bug fixes, or other enhancements, please submit a pull request or open an issue on GitHub.

License

This project is licensed under the MIT License.

Acknowledgments

• Hugging Face for providing the transformer-based models used in UNHAS-GPT.
• Universitas Hasanuddin for supporting this research project.