Introduction LLM_SillyTry is a sample project that demonstrates the use of Large Language Models (LLMs) for question answering and text summarization. It utilizes the powerful Transformers library from Hugging Face to load and fine-tune a pre-trained LLM model for these tasks.
Features Question Answering: Given a passage of text and a question, the model can provide an answer based on the information in the text.
Text Summarization: The model can generate a concise summary of a given text, capturing the key points and ideas.
Model Saving and Loading: The trained model can be saved to disk and loaded later for further use or fine-tuning.
Object-Oriented Design: The code is structured in an object-oriented manner, with the LLMQAModel class encapsulating the model, tokenizer, and related functionality.
Requirements Python 3.7 or higher
Transformers library (version 4.x or higher)
PyTorch (version 1.x or higher)
Usage Clone the repository: