Working with Llama 3

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Overview

Working with Llama 3 is a practical course designed to help developers and AI practitioners understand and leverage the capabilities of Meta’s Llama 3 large language model. This course provides a hands-on approach to deploying, fine-tuning, and integrating Llama 3 into real-world applications such as chatbots, content generation, summarization, and more. Learners begin by exploring the architecture, tokenization strategies, and model variants within the Llama 3 family. The course covers environment setup for both local and cloud-based deployments, including using tools...

Syllabus

Understanding LLMs and Llama

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.

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.

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.

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.