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Christopher Brooks

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.

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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.

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What's inside

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.
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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.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores Llama 2, which is a novel open-source large language model (LLM) developed by Facebook AI Research
Taught by Christopher Brooks, who is a leading researcher in the field of natural language processing and machine learning
Provides hands-on experience in building and deploying LLM applications using Python
Covers advanced techniques such as few-shot prompting and grammars to improve LLM output
Suitable for Python application developers with no prior experience in data science or statistics
Requires access to self-hosted hardware to run Llama 2 models

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Llama for Python Programmers with these activities:
Review Python basics
Reviewing Python basics will help you understand the concepts of Llama 2 and generative AI more easily.
Browse courses on Python Basics
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  • Go over Python syntax and data structures.
  • Solve simple Python coding challenges.
Mentor other students who are learning about Llama 2
Mentoring others will reinforce your understanding of Llama 2 and help you develop your communication skills.
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  • Identify a student who is interested in learning about Llama 2.
  • Share your knowledge and experience with the student.
  • Provide guidance and support as they progress.
Show all two activities

Career center

Learners who complete Llama for Python Programmers will develop knowledge and skills that may be useful to these careers:
Natural Language Processing Engineer
As a Natural Language Processing Engineer, you will help develop computer systems that understand and process human language. This course will help build your understanding of the field and provide hands-on experience that you can apply directly to a career in this field.
Machine Learning Engineer
As a Machine Learning Engineer, you will have a strong understanding of the fundamentals of machine learning and how to apply them to real-world problems. This course will help build these fundamentals and give you hands-on experience that can be applied directly to real-world projects.
Data Scientist
As a Data Scientist, you will use data analysis, predictive modeling, and statistical approaches to extract meaningful insights from data. The statistical methods taught in this course will be very helpful in this domain.
Data Analyst
As a Data Analyst, you will collect, clean, and analyze data to identify trends and patterns. This course will provide you with a foundation in data analysis techniques and tools.
Artificial Intelligence Engineer
As an Artificial Intelligence Engineer, you will create and innovate AI applications and solutions. This course can help build a foundation for the machine learning required to perform this job. It also provides you with hands-on skills that can be applied directly to the work you'll do.
Business Analyst
As a Business Analyst, you will help businesses identify and solve problems by analyzing data and processes. This course will provide you with a foundation in data analysis techniques and business process improvement.
Financial Analyst
As a Financial Analyst, you will use data to evaluate investments and make financial decisions. This course will provide you with a foundation in data analysis techniques and financial principles.
Marketing Analyst
As a Marketing Analyst, you will use data to understand customer behavior and make marketing decisions. This course will provide you with a foundation in data analysis techniques and marketing principles.
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to identify investment opportunities and make trading decisions. This course will build a foundation in the math and statistics necessary for success in this field.
Operations Research Analyst
As an Operations Research Analyst, you will use data to optimize business processes and make decisions. This course will provide you with a foundation in data analysis techniques and operations research principles.
Software Engineer
As a Software Engineer, you will design and build software applications. This course can help build a foundation in the computer science fundamentals necessary for success in this field.
UX Designer
As a UX Designer, you will research, design, and evaluate user interfaces for websites and apps. The skills learned in this course will help you build a foundation in human-computer interaction and user experience design.
Product Manager
As a Product Manager, you will gather and analyze customer feedback and data to identify and prioritize new product features. The skills learned in this course will help you carry out this important work.
Research Scientist
As a Research Scientist, you will perform research in a specialized field, such as engineering, medicine, or computer science. The skills learned in this course may be useful as they apply to research in a variety of fields.
Technical Writer
As a Technical Writer, you will create and edit technical documents, such as user manuals, white papers, and training materials. The skills learned in this course will help build a solid foundation in technical writing and communication.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Llama for Python Programmers.
Speech and Language Processing is widely recognized as the leading textbook in the field. will give you a comprehensive background in speech and language processing, which is an essential foundation for building LLM models.
Deep Learning is widely recognized as the leading textbook in the field of deep learning. will provide a comprehensive overview of the field, including the latest advances in LLMs.
Foundations of Statistical Natural Language Processing provides a comprehensive overview of statistical NLP techniques, covering topics such as language modeling, machine translation, and information retrieval. will help build the foundation in statistical modeling that is essential to understanding many LLM models.
Interpretable Machine Learning explains how to build and deploy machine learning models, and how to ensure that your model's output is fair, unbiased, and explainable. Experience with these techniques will be helpful in developing and understanding your own LLM applications.
Natural Language Processing in Action provides a practical introduction to NLP techniques through the use of Python packages like spaCy and nltk. will help you to build a strong foundation in NLP that will complement your study of this course.
Building Machine Learning Systems with Python provides an introduction to building machine learning systems in Python. will help you build a strong foundation in machine learning principles that will be helpful when building LLM applications.
Deep Learning for Coders with fastai and PyTorch provides a practical introduction to building deep learning models using fastai and PyTorch. While much of this book is not specific to LLMs, it will provide the necessary background in deep learning that is necessary to understand Llama 2 and other LLMs.
Python Machine Learning provides a practical introduction to building machine learning models in Python. While not strictly necessary for this course, it will provide a strong foundation in the broader field.

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