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.
(14 reviews)
Recommended experience
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.
Skills you'll gain
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There are 3 modules in this course
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
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
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.
What's included
4 videos3 readings1 assignment1 programming assignment2 ungraded labs1 plugin
Instructor
Offered by
Recommended if you're interested in Software Development
University of Michigan
Coursera Project Network
Korea Advanced Institute of Science and Technology(KAIST)
Fractal Analytics
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Reviewed on Jun 13, 2024
Very good course to practice the prompting engineering
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