About This Course
This 3-day, hands-on course - Deep Dive LLM Technique for Engineers & Developers equips participants with Large Language Models (LLMs) fundamental with applied engineering skills to work directly with, using both cloud-based APIs and local deployment tools. From grasping the core concepts behind transformer architecture, Generative Pre-trained Transformers (GPTs), and open-source model ecosystems, to exploring cutting-edge techniques such as Prompt Engineering, Retrieval-Augmented Generation (RAG), and lightweight fine-tuning (LoRA/PEFT) - you'll gain both theoretical understanding and practical skills. Participants will work directly with tools like Google Colab, LM Studio, Ollama, Hugging Face, and experimenting with local and cloud-based LLM deployments.
Learning Objectives
Upon completing this course, you will be able to:
- Understand the foundational concepts of LLMs, including their architecture and training mechanisms.
- Master prompt engineering to effectively communicate with and utilize LLMs.
- Use Google Colab to test and evaluate LLMs via APIs and build prototype workflows.
- Compare and choose cloud platforms (Hugging Face, Together.ai, NVIDIA NIM) for LLM hosting.
- Analyze trade-offs in model selection: open-weight vs closed, latency vs cost, scale vs quality.
- Install and operate quantized local models using LM Studio and Ollama
- Understand and apply PEFT/LoRA for cost-efficient fine-tuning, and RAG for enhancing factuality.
Prerequisites
- Attendees must have a Laptop with wifi connection.
- Basic Python Programming: Understanding of Python is essential, though the course includes a primer for those with less experience.
- Fundamental AI Concepts: A general grasp of AI and machine learning concepts is helpful, though not mandatory as foundational lessons are included.
- Familiarity with APIs and web services: would be helpful.
- Interest in AI Development: A keen interest in learning and applying AI technologies, particularly LLMs.
Target Audience
- Developers and Engineers looking to specialize in AI, particularly with LLMs.
- Practitioners aim to expand their toolkit with LLM development skills for enhanced data analysis and AI product development.
- Entrepreneurs and Innovators: Seeking to integrate AI into their business models or products.
Training Outline
- Introduction to Machine Learning, Generative AI and LLMs
- Exploring LLM Use Cases with Chatbot Strengths & Limitations
- Mastering Prompt Engineering
- Introduction to Google Colab and API Access
- Hosting Platforms and Deployment Criteria
- Hosting Your Own LLMs with LM Studio and Ollama
- RAG Fundamentals with LangChain and LlamaIndex
- Embeddings & Vector Databases for RAG
- Fine Tuning and Advanced LLM Techniques PEFT, LoRA
- Lab Implementing RAG with NVIDIA Endpoints
- Ethics & Future of AI