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 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.
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.
涵盖的内容
6个视频4篇阅读材料1个作业1个讨论话题1个非评分实验室
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6个视频•总计40分钟
Welcome to the Class: Llama for Python Programmers•3分钟
What is Llama 2? An Open Source Large Language Model (LLM)•9分钟
Llamas Llamas Everywhere! Inference, Chat, and Code Completion•6分钟
Open Source LLMs: Llama and Its Competitors•7分钟
The Elephant in the Room: Running Large Language Models on Small Hardware•5分钟
Introducing Llama 3 - Understanding the Llama 3 and Llama 3.1 Herd of Models•9分钟
4篇阅读材料•总计30分钟
Course Syllabus•5分钟
Help Us Learn About You•5分钟
Optional Readings: Llama License and Academic Papers•10分钟
The Llama 3 Herd of Models by Meta•10分钟
1个作业•总计5分钟
Module 1 Quiz•5分钟
1个讨论话题•总计10分钟
Meet Your Fellow Learners•10分钟
1个非评分实验室•总计1分钟
Llama 2 Workspace•1分钟
Under the Hood with Llama2 and Python: Understanding How it Works
2 Module•小时 后完成
单元详情
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.
涵盖的内容
4个视频1篇阅读材料1个作业1个非评分实验室
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4个视频•总计31分钟
Llama 2 Tokenization•5分钟
Building Our First Llama 2 Application with llama.cpp•9分钟
Choices/Parameters•9分钟
Llama 2 Chat and Conversations•8分钟
1篇阅读材料•总计10分钟
Optional Readings: Sampling Methods, Bindings, and Quantization•10分钟
1个作业•总计8分钟
Module 2 Quiz•8分钟
1个非评分实验室•总计1分钟
Llama 2 Workspace•1分钟
Building a Llama 2 Application
3 Module•小时 后完成
单元详情
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.
涵盖的内容
4个视频4篇阅读材料1个作业1个编程作业2个非评分实验室1个插件
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4个视频•总计32分钟
Zero and Few Shot Prompting•10分钟
Controlling Model Output•10分钟
Using Grammars•9分钟
Course Conclusion•3分钟
4篇阅读材料•总计40分钟
Mycophiles Attack! 🍄🔥•10分钟
Optional Reading: Grammars•10分钟
Post-Course Survey•10分钟
Continue your learning with the AI Collection from Michigan Online•10分钟
1个作业•总计6分钟
Module 3 Quiz•6分钟
1个编程作业•总计60分钟
Generate Realistic Fake Data•60分钟
2个非评分实验室•总计61分钟
Llama 2 Workspace•1分钟
Llama's Adventure: A Journey Through the Dungeon 🌋(Optional)•60分钟
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