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Working with Llama 3

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

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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 about the architecture of Llama and its applications. It will also provide you with the techniques required to fine-tune and deploy the model for specific tasks, and to optimize its performance.

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 general use cases. You'll also gain hands-on experience setting up, running, and performing inference using the llama-cpp-python library.

Learn to Fine-Tune and Deploy Llama Models

You'll explore dataset preprocessing, model fine-tuning with Hugging Face, and advanced optimization techniques for efficient performance. To wrap up the course, you'll implement a RAG system using Llama and LangChain.

Throughout the course, you'll engage with practical examples, including an example of creating a customer service bot, to reinforce your understanding of these concepts.

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.
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In the following Tracks

Associate AI Engineer for Data Scientists

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  1. 1

    Understanding LLMs and Llama

    Free

    The field of large language models has exploded, and Llama is a standout. With Llama 3, possibilities have soared. Explore how it was built, learn to use it with llama-cpp-python, and understand how to craft precise prompts to control the model's behavior.

    Play Chapter Now
    What is Llama?
    50 xp
    Loading and using Llama 3
    100 xp
    Parsing Llama 3 completion outputs
    100 xp
    Getting started with Llama
    50 xp
    More creative Llama completions
    100 xp
    Make a philosophy chatbot
    100 xp
    Prompt engineering Llama 3
    50 xp
    Make Llama speak like a pirate
    100 xp
    3-shot prompting with Llama
    100 xp
  2. 2

    Using Llama Locally

    Language models are often useful as agents, and in this Chapter, you'll explore how you can leverage llama-cpp-python's capabilities for local text generation and creating agents with personalities. You'll also learn about decoding parameters' impact on output quality. Finally, you'll build specialized inference classes for diverse text generation tasks.

    Play Chapter Now
  3. 3

    Finetuning Llama for Customer Service using Hugging Face & Bitext Dataset

    Language models are powerful, and you can unlock their full potential with the right techniques. Learn how fine-tuning can significantly improve the performance of smaller models for specific tasks. Dive into fine-tuning smaller Llama models to enhance their task-specific capabilities. Next, discover parameter-efficient fine-tuning techniques such as LoRA, and explore quantization to load and use even larger models.

    Play Chapter Now
  4. 4

    Creating a Customer Service Chatbot with Llama and LangChain

    LLMs work best when they solve a real-world problem, such as creating a customer service chatbot using Llama and LangChain. Explore how to customize LangChain, integrate fine-tuned models, and craft templates for a real-world use case, utilizing RAG to enhance your chatbot's intelligence and accuracy. This chapter equips you with the technical skills to develop responsive and specialized chatbots.

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Training 2 or more people?

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In the following Tracks

Associate AI Engineer for Data Scientists

Go To Track

collaborators

Collaborator's avatar
James Chapman

prerequisites

Introduction to LLMs in Python
Imtihan Ahmed HeadshotImtihan Ahmed

Machine Learning Engineer

Machine learning engineer with 6 years of experience working on large-scale software systems serving millions of users. If you are looking for someone with experience in machine learning or software engineering, focusing on large language models (LLMs), recommendation systems, and NLP, feel free to reach out and we can talk!
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