Llama for Python Programmers
Learn to use Llama 2 LLM for Python-based AI applications. Master open-source LLMs on local hardware.
Learn to use Llama 2 LLM for Python-based AI applications. Master open-source LLMs on local hardware.
Master Llama 2, a powerful open-source large language model (LLM), in this intermediate-level course designed for Python programmers. Learn to leverage Llama 2 for building generative AI applications on local hardware. Explore techniques like quantization using llama.cpp to run LLMs efficiently. Gain hands-on experience with zero-shot and few-shot prompting, advanced methods like grammars, and different Llama 2 variants. Discover how to integrate Llama 2 into Python applications, control model output, and optimize performance. Ideal for developers looking to harness the potential of open-source LLMs in their projects.
4.4
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What you'll learn
Understand Llama 2's architecture and capabilities as an open-source LLM
Learn to run and interact with Llama 2 on local hardware using llama.cpp
Master zero-shot and few-shot prompting techniques for improved model performance
Implement advanced methods like grammars to enhance and constrain Llama 2 output
Gain practical experience in building Python-based LLM applications with Llama 2
Explore different Llama 2 model variants and their specific use cases
Skills you'll gain
This course includes:
103 Minutes PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to using Llama 2, an open-source large language model, for Python-based AI applications. Students learn about Llama 2's architecture, capabilities, and how it compares to other LLMs. The curriculum covers practical skills such as running Llama 2 on local hardware, using llama.cpp for efficient inference, and implementing various prompting techniques. Participants gain hands-on experience in building Llama 2 applications, controlling model output, and utilizing advanced features like grammars. The course also explores different Llama 2 variants and their specific use cases in Python programming contexts.
Introduction to Llama 2: A High Quality Open Source Large Language Model
Module 1 · 1 Hours to complete
Under the Hood with Llama2 and Python: Understanding How it Works
Module 2 · 1 Hours to complete
Building a Llama 2 Application
Module 3 · 3 Hours to complete
Instructor
Associate Professor at the University of Michigan
Christopher Brooks is an Associate Professor in the School of Information at the University of Michigan, where he specializes in designing tools to enhance teaching and learning experiences in higher education. His research focuses on the application of learning analytics within human-computer interaction, utilizing methods from educational data mining, machine learning, and information visualization. Brooks has published extensively in these areas and is actively involved in directing the Educational Technology Collective, which includes postdoctoral scholars and students collaborating on innovative projects. He teaches various courses related to applied data science and has contributed to online education platforms such as Coursera. His work aims to leverage data to improve educational outcomes and foster better learning environments.
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4.4 course rating
12 ratings
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