This repository contains Jupyter notebooks for generating questions, answers, and context-based questions and answers using the Stanford Question Answering Dataset (SQuAD). The notebooks demonstrate how to work with the SQuAD dataset and generate natural language questions and answers.

1. Question Generation: This notebook focuses on generating questions from given text passages. It uses the SQuAD dataset to fine-tune a model for question generation.

2. Answer Generation: In this notebook, you can generate answers to questions based on a given context. It uses pre-trained models to identify answers within the context.

3. Context-Based QA: The context-based question and answer generation notebook combines the previous two functionalities. Given a context, it generates questions and answers. This is particularly useful for tasks like question-answering chatbots.

4. Chain Of Thought QLoRA based QA : Mistral 7B non instruct model finetuned on the SQUAD Dataset with comparable results. Trained till 9000 steps. (Link)[https://huggingface.co/vpgits/Mistral-7B-v0.1-qagen-v0.3]