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Course

Working with Llama 3

Intermediate2 hr

Explore the latest techniques for running the Llama LLM locally and integrating it within your stack.

Python2 hr7 videos21 Exercises1,750 XP14,608Statement of accomplishment

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Course Description

Learn to use the Llama Large-Language Model (LLM)

What is the Llama LLM, and how can you use it to enhance your projects? This course will teach you how to run Llama locally, guide its responses using prompt engineering, and build structured and context-aware conversations.

Understand the Llama model and its applications

You'll start with an introduction to the foundational concepts of Llama, learning how to interact with Llama models and exploring their use cases. You'll gain hands-on experience setting up, running, and interacting with the model using the llama-cpp-python library. As you progress, you'll apply techniques to refine Llama’s responses, maintain conversation history, and generate structured outputs ready to be integrated into automated workflows.

This is an ideal introduction to Llama for developers and AI practitioners. It explores the foundations of this powerful open-source LLM and how to apply it in real-world scenarios.

Prerequisites

Curriculum

Course outline

1

Understanding LLMs and Llama

The field of large language models has exploded, and Llama is a standout. With Llama 3, possibilities have soared. Explore its capabilities, learn to use it with llama-cpp-python, and understand how to control the model's behavior by adjusting parameters and assigning specific roles to guide the conversation.
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2

Using Llama Locally

Language models can do more than just generate text - they can be guided to produce structured and context-aware responses. In this chapter, you'll explore how to leverage llama-cpp-python to generate outputs in specific formats, including JSON-compatible responses for data integration. You'll also refine Llama's output using prompting techniques to align responses with different tasks. Finally, you'll build conversations that maintain context across multiple exchanges, ensuring more natural interactions.
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Working with Llama 3

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