LMS-ChatBot

Overview

This project is a chatbot built using a GPT-based model. The LLM/ directory uses code from the book "Build A Large Language Model (From Scratch)" by Sebastian Raschka.

Credits

This project includes code from "Build A Large Language Model (From Scratch)" by Sebastian Raschka.

If you find this book or code useful for your research, please consider citing it:

@book{build-llms-from-scratch-book,
  author       = {Sebastian Raschka},
  title        = {Build A Large Language Model (From Scratch)},
  publisher    = {Manning},
  year         = {2023},
  isbn         = {978-1633437166},
  url          = {https://www.manning.com/books/build-a-large-language-model-from-scratch},
  note         = {Work in progress},
  github       = {https://github.com/rasbt/LLMs-from-scratch}
}

Setup Instructions

- To set up the pre-trained model and generate responses, follow these steps:

1. Clone the Repository:

   git clone https://github.com/kwshr/LMS-ChatBot.git
   cd LMS-ChatBot

2. Navigate to the LLM Directory:

   cd LLM

3. Run the Main Script:

   python PreTrainMain.py

   The script will prompt you to enter a prompt and the maximum number of tokens to generate.

4. Optional Configuration:

   • If you want to manually edit the top-k and temperature settings to generate more coherent texts, you can do so by editing the PreTrainMain.py file:

     # Open PreTrainMain.py
     nano PreTrainMain.py
     
     # Locate the configuration section and adjust the parameters as needed
     top_k = <desired_value>  # Example: top_k = 40
     temperature = <desired_value>  # Example: temperature = 0.7

- To set up the frontend part of the project, follow these steps:

1. Navigate to the Frontend Directory:

   cd frontEnd/chatbot

2. Install Dependencies:

   npm install

   This command will install all the required packages listed in the package.json file.

3. Start the Development Server:

   npm start

   The application should now be running on http://localhost:3000/, and you can interact with the chatbot through the web interface.

4. Build for Production (Optional):

   npm run build

   This command will create an optimized build of the app, ready for deployment.