• Clone this repositry using git clone https://github.com/ramashisx/oneAPI_hackathon_submission
• Make sure you have all the files in data directory atleast train.csv, test.csv must be there to start
• To run this project using scripts navigate to scripts folder to run using notebooks go to notebook folder
• Note running using scripts is easier to train without waiting a lot of time while training just make sure you have tmux installed in your system.
• To deploy the webapp go to the webapp folder
• The blog is there in the docs folder
• Navigate to your preferred folder for further instructions

Welcome to the README for the solution developed for the oneAPI Hackathon: The LLM Challenge. This solution focuses on harnessing the power of Language Models (LLMs) to address the challenge of text understanding and generation for accurate, context-aware answers. The primary goal of this project is to create a robust LLM, and then utilize it effectively within an application. In addition, this README will provide an overview of the steps taken to build the solution, including the tools and technologies used.

In this section, we'll provide a concise overview of our solution, focusing on the key components and steps involved in harnessing Intel oneAPI's potential for creating a generative Question Answering model and deploying it as a web application.

Data Preparation

Data is the bedrock of our solution. Our unique question-answering dataset poses challenges with 35% of answers not present in the context, requiring a generative question-answering model with an encoder-decoder architecture.

Training the Model

After assessing the problem statement and the available resources, we determined that this was not just an extractive question-answering task but also involved generative question-answering. To tackle this, I decided to fine-tune a T5-based model trained on the Squad v2 dataset.

Quantization and Pruning

To optimize our model, we employed quantization and pruning techniques. Quantization boosts speed and efficiency by reducing numerical precision, while pruning enhances efficiency by eliminating less critical parameters. This dual strategy balances accuracy and operational speed.

Deployment as a Web Application

After successful training, we deployed the model as a web application. The architecture featured a FastAPI-powered backend and a Streamlit-based frontend for an intuitive user experience. Modularity allows easy adaptation for different machine learning models.

1. Intel® Extension for Pytorch: Used for our Multilingual Extractive QA model Training/Inference optimization.
2. Intel® Neural Compressor: Used for Generative QA model Inference optimization, using quantization and pruning.

1. Data Analysis: We began by examining the problem statement and the available data. It was clear that this challenge required both extractive and generative question-answering capabilities. We decided to fine-tune a T5-based model, as it is well-suited for both types of QA tasks.

2. Fine-Tuning: Using the Squad v2 dataset, we fine-tuned the T5 model to make it proficient in generating accurate answers for both extractive and generative QA. This involved training the model on relevant data to align it with the requirements of the challenge.

3. Model Optimization: Post-fine-tuning, we utilized Intel's Neural Compressor to optimize the trained model. This step included model pruning and quantization to make it more efficient and deployable.

4. Web Application Development: To showcase the power of our LLM and allow users to interact with it easily, we developed a web application. This application integrates the model and offers a user-friendly interface for generating answers to text-based queries.

✅ Utilizing the Intel® AI Analytics Toolkit: By utilizing the Intel® AI Analytics Toolkit, developers can leverage familiar Python* tools and frameworks to accelerate the entire data science and analytics process on Intel® architecture. This toolkit incorporates oneAPI libraries for optimized low-level computations, ensuring maximum performance from data preprocessing to deep learning and machine learning tasks. Additionally, it facilitates efficient model development through interoperability.

✅ Seamless Adaptability: The Intel® AI Analytics Toolkit enables smooth integration with machine learning and deep learning workloads, requiring minimal modifications.