Llama for Python Programmers is designed for programmers who want to leverage the Llama 2 large language model (LLM) and take advantage of the generative artificial intelligence (AI) revolution. In this course, you’ll learn how open-source LLMs can run on self-hosted hardware, made possible through techniques such as quantization by using the llama.cpp package.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
You’ll explore how Meta’s Llama 2 fits into the larger AI ecosystem, and how you can use it to develop Python-based LLM applications. Get hands-on skills using methods such as few-shot prompting and grammars to improve and constrain Llama 2 output, allowing you to get more robust data interchanges between Python application code and LLM inference. Lastly, gain insight into the different Llama 2 model variants, how they were trained, and how to interact with these models in Python.
This course does not require a data science or statistics background. It is developed specifically for Python application developers who are interested in integrating generative AI, such as Llama 2, into their work.
What you'll learn
- Understand how to use llama.cpp Python APIs to build Llama 2-based large language model (LLM)applications.
- Learn to run and interact with the Llama 2 large language model on commodity local hardware.
- Learn to utilize zero- and few-shot prompting as well as advanced methods like grammars in llama.cpp to enhance and constrain Llama 2 model output.
- Learn about the different Llama 2 model variants: the base model, chat model, and code llama and how to interact with these models in Python.
Syllabus
Introduction to Llama 2: A High Quality Open Source Large Language Model
This module introduces you to Llama 2, highlighting its architecture, training method, and capabilities as a high-quality open-source LLM. This foundational segment prepares you for hands-on learning in the following modules.
Under the Hood with Llama2 and Python: Understanding How it Works
This module unravels Llama 2's intricacies within Python, guiding you through tokenization, the development of Llama 2 applications via llama.cpp, and parameter adjustments for improved interactions.
Building a Llama 2 Application
This module begins with a demonstration of zero and few-shot prompting techniques, then moves on to controlling model output for tailored responses. It culminates in practical programming assignments, enabling you to apply your knowledge and showcase your skills in crafting refined Llama 2 applications.
