Llama for Python Programmers

Llama for Python Programmers

Instructor: Christopher Brooks

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.

Details to know

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Assessments

3 assignments

Taught in English

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There are 3 modules in this course

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. 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 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.

What's included

6 videos4 readings1 assignment1 discussion prompt1 ungraded lab

6 videosTotal 40 minutes

Welcome to the Class: Llama for Python Programmers3 minutesPreview module
What is Llama 2? An Open Source Large Language Model (LLM)8 minutes
Llamas Llamas Everywhere! Inference, Chat, and Code Completion6 minutes
Open Source LLMs: Llama and Its Competitors7 minutes
The Elephant in the Room: Running Large Language Models on Small Hardware5 minutes
Introducing Llama 3 - Understanding the Llama 3 and Llama 3.1 Herd of Models9 minutes

4 readingsTotal 30 minutes

Course Syllabus5 minutes
Help Us Learn About You5 minutes
Optional Readings: Llama License and Academic Papers10 minutes
The Llama 3 Herd of Models by Meta10 minutes

1 assignmentTotal 5 minutes

Module 1 Quiz5 minutes

1 discussion promptTotal 10 minutes

Meet Your Fellow Learners10 minutes

1 ungraded labTotal 1 minute

Llama 2 Workspace1 minute

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.

What's included

4 videos1 reading1 assignment1 ungraded lab

4 videosTotal 31 minutes

Llama 2 Tokenization5 minutesPreview module
Building Our First Llama 2 Application with llama.cpp9 minutes
Choices/Parameters8 minutes
Llama 2 Chat and Conversations7 minutes

1 readingTotal 10 minutes

Optional Readings: Sampling Methods, Bindings, and Quantization10 minutes

1 assignmentTotal 8 minutes

Module 2 Quiz8 minutes

1 ungraded labTotal 1 minute

Llama 2 Workspace1 minute

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.