Leveraging Llama2 for Advanced AI Solutions
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What you'll learn
Evaluate LLMs conceptually and comprehend the solution development process
Analyze use cases for LLMs and determine Optimal Architectures, Models, and Optimization Techniques
Apply and compare Diverse Optimization Techniques for LLM Models
Design and develop Advanced LLM Solutions Utilizing LLama2
Skills you'll gain
Tools you'll learn
Details to know
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There is 1 module in this course
The focus of this course is to equip learners with the skills and knowledge to design, develop, and optimize advanced large language model (LLM) solutions using LLama2. Topics covered will include a comprehensive understanding of LLM architectures, techniques for fine-tuning LLMs, retrieval-augmented generation (RAG), and the utilization of tools like Ollama, LangChain, Streamlit, and Hugging Face. This course will be exciting for learners as it delves into cutting-edge advancements in AI, offering hands-on experience with state-of-the-art tools and techniques.
A key highlight of the course is building two different implementations of a solution that consumes the original LLama2 paper published by Meta, enabling Q&A interactions with the AI about the paper. This hands-on project not only provides practical experience but also demonstrates the benefits of using LLama2 for deep understanding and knowledge extraction from complex documents. This course targets Software Engineers, Machine Learning Engineers, Data Scientists, and Engineering Managers. Participants will gain insights into leveraging Llama2 for advanced AI solutions. Software Engineers will deepen their understanding of LLM architectures, Machine Learning Engineers will enhance model optimization skills, Data Scientists will explore innovative applications, and Engineering Managers will learn to lead AI-driven projects effectively. Participants should have a beginner-level knowledge of Python and accounts on GitHub and Hugging Face for hands-on projects. A minimum hardware setup of 8 GB RAM and 3.8 GB of free storage is required, and the course is compatible with macOS or Windows operating systems. By the end of this course, participants will be able to evaluate large language models (LLMs) and understand the solution development process. They will analyze use cases to identify optimal architectures and optimization techniques, apply and compare various optimization methods, and design advanced LLM solutions using Llama2, equipping them to create sophisticated AI applications.
This course is designed to equip learners with the skills and knowledge to design, develop, and optimize advanced large language model (LLM) solutions using Llama2. It covers a comprehensive understanding of LLM architectures, techniques for fine-tuning LLMs, retrieval-augmented generation (RAG), and the utilization of tools like Ollama, LangChain, Streamlit, and Hugging Face.
What's included
12 videos4 readings2 assignments1 peer review
12 videos•Total 58 minutes
- Introduction to the Course & Meet Your Instructor•2 minutes
- Demystifying LLM Solutions •7 minutes
- Tool Time: Download & Install Essentials •6 minutes
- LLama2 in Action: Create YOUR First Model •6 minutes
- RAG Solutions: The Basics and Beyond •4 minutes
- New Bringing RAG to Life: Implementing the Solution •7 minutes
- Crafting the UI: Making RAG User-Friendly •6 minutes
- Fine-Tuning Fundamentals: Key Concepts Explained •3 minutes
- Step-by-Step: Preparing and Training Your Model •5 minutes
- Final Touches: Bringing Your Fine-Tuned Model to Life•4 minutes
- Choosing the Right Path: Fine-Tuning vs. RAG for Your AI Projects •6 minutes
- Congratulations and Continuous Learning Journey•2 minutes
4 readings•Total 20 minutes
- Welcome to the Course: Course Overview•5 minutes
- LLM App Development in a Nutshell •5 minutes
- RAFT: Sailing Llama Towards Better Domain-Specific RAG •5 minutes
- RAG vs Finetuning: Which Is the Best Tool to Boost Your LLM Application? •5 minutes
2 assignments•Total 50 minutes
- Leveraging Llama2 for Advanced AI Solutions•20 minutes
- Building Hybrid RAG & Fine-Tuned LLama2 Solutions•30 minutes
1 peer review•Total 15 minutes
- Build Hybrid Solutions: RAG & Fine-Tuned •15 minutes
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