LAX - Local AI eXecution

Welcome to the LAX - Local AI eXecution repository! This project provides a straightforward method for loading and running AI models locally on your machine. With this setup, you can interact with the GPT-2 model directly from your terminal, enabling a local AI execution environment.

Overview

The repository contains a Python script that uses the transformers library to load and interact with the GPT-2 model. The script features a text generation function and a chat loop for real-time interaction.

Requirements

Hardware

• CPU: A modern multi-core processor is recommended.
• RAM: Minimum of 4GB of RAM required. 8GB or more is recommended for better performance.
• Storage: Sufficient disk space to download and store model weights (approximately 500MB for GPT-2).

Software

• Python: Python 3.7 or later.
• Dependencies: The script requires the following Python packages:
  • torch
  • transformers

Installation and Setup

1. Clone the Repository

   Clone this repository to your local machine:

   git clone https://github.com/Jeff9497/LAX.git

2. Navigate to the Project Directory

   cd LAX

3. Create and Activate a Virtual Environment (Optional but Recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

4. Install the Required Packages

   Install dependencies using pip:

   pip install torch transformers

Running the Script

1. Run the Script

   python LAX28.py

2. Interact with the Model

   • Type your messages into the terminal and press Enter.
   • Type exit or quit to end the chat session.

Future Updates

We plan to expand this repository by introducing additional AI models. Future updates will include options for:

• GPT-2 XL: An extended version of GPT-2 with more parameters for improved performance.
• DistilBERT: A lighter version of BERT for faster inference and lower resource consumption.
• T5 (Text-To-Text Transfer Transformer): A versatile model for various NLP tasks.

You will be able to select and switch between these models according to your needs.

Power and Performance Considerations

Running AI models locally requires significant computational resources. While the script can operate with 4GB of RAM, better performance is achieved with more RAM and a faster CPU. Ensure your machine meets the recommended specifications for optimal results.

Contributing

Contributions are welcome! If you have suggestions, improvements, or encounter issues, please submit issues or pull requests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or support, reach out to (mailto:[email protected]).