Does this program cover the cost of API access?

In Course 1 and 2, you will have access to optional activities where you will be guided to access Llama models via API. The cost of API credits is not included in this program and will be at your own cost. Some API providers may provide free credits when you first register with their platform. Please check with the providers directly to confirm.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Foundations of LLMs and Llama development

Foundations of LLMs and Llama development

本课程是 Building Generative AI Apps with Llama 专业证书的一部分

位教师：Taught by Meta Staff

包含在 Coursera Plus 中

了解更多

4个模块

深入了解一个主题并学习基础知识。

高级设置等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

高级设置等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

The foundational concepts of Large Language Models and the unique capabilities of Llama.
Prompting techniques and script-based workflows for generating text with the API.
The design and deployment of a conversational assistant with consistent personality.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累 Software Development 领域的专业知识

本课程是 Building Generative AI Apps with Llama 专业证书专项课程的一部分

在注册此课程时，您还会同时注册此专业证书。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
通过 Meta 获得可共享的职业证书

该课程共有4个模块

涵盖的内容

11个视频1篇阅读材料

11个视频总计37分钟

Introduction to the program2分钟
Course introduction2分钟
Introduction: Understanding large language models0分钟
What is artificial intelligence?3分钟
Machine learning and neural networks5分钟
What are large language models (LLMs)?3分钟
How do LLMs work?6分钟
What is Llama?1分钟
Model evolution from Llama 1 to Llama 43分钟
Llama architecture: Key technical concepts5分钟
Conclusion: Introduction to LLMs and LLama1分钟

1篇阅读材料总计5分钟

Foundations of LLMs and Llama Development Course Syllabus 5分钟

涵盖的内容

13个视频

13个视频总计33分钟

Introduction: Getting started with Llama development1分钟
Authenticating with the API: Secure key use and prompt submission3分钟
Intro to SDKs and libraries1分钟
Shaping responses with structured prompts and message roles3分钟
Coding your first API call: Prompt, parameters and response5分钟
Activity introduction: Generate text with the API using prompts and parameters0分钟
Understanding common API errors4分钟
Using try/except to handle API failures and retry requests4分钟
Activity introduction: Handle common API errors0分钟
Introduce simple interfaces2分钟
Introduction to Gradio3分钟
Activity introduction: Create a Gradio chat app that sends prompts to the API0分钟
Conclusion: getting started with Llama development1分钟

涵盖的内容

23个视频

23个视频总计102分钟

Introduction to prompt engineering1分钟
Understanding prompt roles: System, user and assistant6分钟
How prompt roles influence assistant behavior10分钟
Improving prompt effectiveness through intent clarity8分钟
Composing single-turn prompts with the API5分钟
Activity introduction: Build your first prompt with system and user roles1分钟
Understanding API generation parameters4分钟
How parameters shape API output behavior8分钟
Tuning output: modifying API parameters in prompt payloads5分钟
Choosing the right parameters for specific generation goals5分钟
Activity introduction: Adjust API parameters for controlled output1分钟
Understanding API message format for multi-turn conversations3分钟
How conversation history shapes Llama model responses3分钟
Managing token limits in multi-turn conversations3分钟
Building a Python-based conversation manager for API5分钟
Activity introduction: Build and test a multi-turn conversation manager using the API1分钟
Common output formats for LLM responses: Lists, tables, and JSON4分钟
How prompts and system messages control output format5分钟
Prompting for structured output: Lists, JSON and more4分钟
Testing prompt variations for reliable output formatting2分钟
Choosing the right output format for your use case3分钟
Activity introduction: Design prompts for structured responses using the API1分钟
Conclusion: Introduction to prompt engineering for API calls1分钟

涵盖的内容

17个视频

17个视频总计77分钟

Introduction: Building your first assistant1分钟
Core components of an LLM assistant interface4分钟
Designing for UX: How layout and flow shape LLM interactions5分钟
Building a conversational UI with Gradio or Streamlit4分钟
Evaluating and improving your assistant’s UI5分钟
Activity introduction: Build a full assistant interface that connects to the API0分钟
What defines an assistant’s personality? Tone, role and consistency6分钟
Shaping assistant identity with system prompts and persona language6分钟
Creating and testing assistant personas with the API4分钟
Comparing persona behaviors: Vocabulary, style and consistency7分钟
Activity introduction: Build a consistent assistant persona using structured prompts1分钟
Deployment essentials: What you need to launch on Hugging Face spaces5分钟
How Hugging Face Spaces hosts and runs your assistant app4分钟
Solving common deployment errors: Packages, paths and permissions4分钟
Developer use guide - Llama protection tools11分钟
Conclusion: Building your first assistant1分钟
Course conclusion: Foundations of LLMs and Llama development2分钟

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将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

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Taught by Meta Staff

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Advanced assistant customization with fine-tuning and context
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Inference techniques for local and cloud LLM deployment
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常见问题

Developers, entrepreneurs, and technical professionals with 1-2 years of Python knowledge who want to build AI-enabled assistants. Ideal for those looking to upskill in generative AI development or create practical business solutions using Llama models.

1-2 years of Python programming experience (for those who need to meet this prerequisite, start with the Meta Programming in Python course)

Familiarity with command-line interfaces

Understanding of basic software development concepts

Basic knowledge of REST APIs

In this program, you will be guided to access the Llama 4 Scout 17B and Llama 3.1 8B models via API in Courses 1 and 2. The course content includes examples using one of the API providers, but you are free to choose any provider that offers access to Llama models for your learning experience. Examples of such providers include Together AI, Groq, Hugging Face, and others.

In Course 3, you will be guided to use the Llama 3.1 8B model in a local environment. Llama models are available from multiple sources, with the best place to start being https://www.llama.com.

Models are also hosted and distributed by partners such as Amazon Web Services, Microsoft Azure, Google Cloud, IBM Watsonx, Oracle Cloud, Snowflake, Databricks, Dell, Hugging Face, Groq, Cerebras, SambaNova, and many others. See the Llama.com FAQ for more information.

In Courses 1 and 2, you will access Llama models through an API. No additional device requirements are needed, but you will need internet access and to select your own API provider.

In Course 3, you will learn to use Llama in a local environment. Hardware requirements vary based on the specific Llama model being used, latency, throughput and cost constraints. For the larger Llama models to achieve low latency, one would split the model across multiple inference chips (typically a GPU) with tensor parallelism. Llama models are known to execute in a performant manner on a wide variety of hardware including GPUs, CPUs (both x86 and ARM based), TPUs, NPUs and AI Accelerators. The smaller Llama models typically run on system-on-chip (SOC) platforms found on PC, Mobile and other Edge devices.

See the Llama.com FAQ for more information.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

Foundations of LLMs and Llama development

您将学到什么

您将获得的技能

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

积累 Software Development 领域的专业知识

该课程共有4个模块

Introduction to LLMs and Llama

涵盖的内容

11个视频总计37分钟

1篇阅读材料总计5分钟

Getting started with Llama development

涵盖的内容

13个视频总计33分钟

Introduction to prompt engineering

涵盖的内容

23个视频总计102分钟

Building Your First Assistant

涵盖的内容

17个视频总计77分钟

获得职业证书

位教师

提供方

从 Software Development 浏览更多内容

Advanced assistant customization with fine-tuning and context

Inference techniques for local and cloud LLM deployment

人们为什么选择 Coursera 来帮助自己实现职业发展

通过 Coursera Plus 开启新生涯

通过在线学位推动您的职业生涯

加入超过 3400 家选择 Coursera for Business 的全球公司

常见问题

更多问题

Foundations of LLMs and Llama development

您将学到什么

您将获得的技能

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

积累 Software Development 领域的专业知识

该课程共有4个模块

Introduction to LLMs and Llama

涵盖的内容

Getting started with Llama development

涵盖的内容

Introduction to prompt engineering

涵盖的内容

Building Your First Assistant

涵盖的内容

获得职业证书

位教师

提供方

从 Software Development 浏览更多内容

Advanced assistant customization with fine-tuning and context

Inference techniques for local and cloud LLM deployment

人们为什么选择 Coursera 来帮助自己实现职业发展

通过 Coursera Plus 开启新生涯

通过在线学位推动您的职业生涯

加入超过 3400 家选择 Coursera for Business 的全球公司

常见问题

Who is this program for?

What background knowledge is necessary?

How can I access the Llama models? What models are covered in this program?

更多问题