Text-Summarization-Model-Using-LLMs

Introduction

Text-Summarization-Model-Using-LLMs is an innovative solution crafted to condense extensive texts into concise, insightful summaries. This project uniquely integrates the power of two Large Language Models (LLMs) - LLaMA and GPT-3.5, leveraging their advanced capabilities for text analysis and summarization. Designed to serve academics, professionals, and casual users alike, our model ensures quick comprehension of complex documents, articles, and reports, embodying the pinnacle of current AI text summarization technology.

Features

• Efficient Text Summarization: Condense articles, reports, and conversations into short summaries without losing essential content.
• Powered by LLMs: Utilizes cutting-edge models like GPT-3.5/ Llama2 for superior text generation and summarization quality.
• Web Application: Features a Flask-based web application and Streamlit interface, making it accessible and user-friendly.
• Cross-Platform Compatibility: Designed for seamless operation across different platforms and devices.

Setup and Installation

Get started with the Text-Summarization-Model-Using-LLMs by following these simple setup instructions:

1. Clone the Repository

   git clone https://github.com/ambuj991/Text-Summarization-Model-Using-LLMs.git
   cd Text-Summarization-Model-Using-LLMs

2. Install Required Packages

   pip install transformers accelerate huggingface_hub flask flask-ngrok pyngrok==4.1.1 flask-cors

3. Hugging Face Authentication

   • Obtain a token from Hugging Face.
   • Log in using huggingface-cli login and enter your token as prompted.

4. Run the Flask App

   flask run

   Follow the ngrok or Flask app instructions for local or public access.

5. Streamlit App Execution Ensure Streamlit is installed:

   pip install streamlit

   Execute the Streamlit application:

   streamlit run your_streamlit_app.py

Contributing

We welcome contributions to enhance Text-Summarization-Model-Using-LLMs. Whether it's feature improvements, bug fixes, or documentation, your help is appreciated. Please read through our contribution guidelines for more details on submitting pull requests.

Acknowledgements

• OpenAI for their LLMs.
• Hugging Face for the model hosting.
• Flask for the web framework.
• Streamlit for creating interactive web apps.