LLM Foundations with Python

Many people are interested in Large Language Models (LLMs) but feel overwhelmed by the complexity of machine learning concepts and the resource-intensive nature of these models. The good news is that you can start light! Think of it like learning to drive a car—you don’t need to understand how every part is made or the physics behind the engine. You just need to know how to use the steering wheel, pedals, and gear shift. This campaign will get you started with LLMs using a light and manageable codebase. By utilizing Google Colab, you’ll have access to powerful cloud resources, including the T4 GPU, at no cost, eliminating the need for high-end hardware. Additionally, there is no frontend programming involved, so you can focus entirely on understanding and implementing the LLMs.

This campaign is designed to provide you with a comprehensive understanding of LLMs and their practical applications using the power of open-source models through the Hugging Face platform. LLMs, such as GPT-3, Llama 2, and Mistral, have revolutionized the field of natural language processing (NLP) by enabling machines to understand and generate human-like text. Throughout this campaign, you will learn how to leverage these powerful models through Hugging Face to create sophisticated NLP applications, including chatbots and sentiment analysis tools.

By the end of this campaign, you will be able to:

• Understand the basics of LLMs and their significance in NLP
• Set up and configure the Hugging Face environment in Google Colab.
• Load and utilize pre-trained LLMs from the Hugging Face model hub.
• Implement basic natural language processing tasks such as text generation using LLMs. Develop a simple yet powerful chatbot using the Llama 2 model.