bubajanneh

Small Language Model Chatbot 🤖

Welcome to the Small Language Model Chatbot repository! This project showcases an interactive chatbot built with Python, utilizing Hugging Face’s distilGPT-2 model. It’s designed to be fun and lightweight, making it perfect for demonstrating the capabilities of small-scale language models.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Interactive Widgets
• Visual Tracking
• Contributing
• License
• Contact
• Releases

Introduction

The Small Language Model Chatbot serves as an engaging way to explore natural language processing. With its interactive design, users can easily input queries and receive responses in real-time. This chatbot highlights the capabilities of smaller language models, making it accessible for educational purposes, demonstrations, and casual conversations.

Features

• Lightweight and Fast: Built on the distilGPT-2 model, the chatbot runs efficiently without requiring heavy computational resources.
• Interactive Widgets: Users can interact with the chatbot through easy-to-use input fields and buttons.
• Visual Tracking: The chatbot tracks and displays conversation history, providing users with context as they chat.
• Open Source: This project is open for contributions, allowing developers to enhance its features and capabilities.

Installation

To get started with the Small Language Model Chatbot, follow these steps:

1. Clone the Repository:

   git clone https://github.com/bubajanneh/small-language-model-chatbot.git
   cd small-language-model-chatbot
   

2. Install Dependencies: Make sure you have Python installed. Then, install the required libraries:

   pip install -r requirements.txt
   

3. Run the Chatbot: Open Jupyter Notebook and run the provided notebook file to start the chatbot.

Usage

After setting up the environment, you can start using the chatbot. Simply launch the Jupyter Notebook and follow the instructions in the notebook to interact with the chatbot.

1. Input Queries: Type your question or statement in the input box.
2. Receive Responses: The chatbot will generate a response based on your input.
3. View Conversation History: Keep track of your chat as the conversation progresses.

Interactive Widgets

The chatbot utilizes ipywidgets to create an interactive experience. Users can input text, submit queries, and receive responses without needing to refresh the page. This seamless interaction enhances user engagement and makes the chatbot more enjoyable to use.

Visual Tracking

One of the standout features of this chatbot is its ability to visually track conversation history. As users interact with the bot, the chat history is displayed in a user-friendly format. This allows users to refer back to previous messages, making conversations more coherent and enjoyable.

Contributing

We welcome contributions from the community! If you would like to enhance the Small Language Model Chatbot, please follow these steps:

1. Fork the Repository: Create your own copy of the repository.
2. Create a Branch: Make a new branch for your feature or bug fix.

   git checkout -b feature/your-feature-name
   

3. Make Changes: Implement your changes and test them.
4. Commit Your Changes:

   git commit -m "Add your message here"
   

5. Push to Your Branch:

   git push origin feature/your-feature-name
   

6. Open a Pull Request: Submit a pull request to the main repository for review.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute the code as per the license terms.

Contact

For any questions or feedback, please reach out to the project maintainer:

• Email: bubajanneh@example.com
• GitHub: bubajanneh

Releases

You can find the latest releases of the Small Language Model Chatbot here. Make sure to download the latest version and execute the files to experience the chatbot.

To keep up with updates, check the “Releases” section regularly.

Feel free to explore the repository, contribute, and enjoy the chatbot experience!