Open Source Generative AI
Class Overview
Learn how to write practical AI applications via hands-on labs. You will design and develop Transformer models, ensuring data security in your work.
This 4-day course covers AI transformer architectures, Python programming, hardware requirements, training techniques, and AI tasks such as classification and regression. It includes hands-on exercises with open-source LLM frameworks, along with advanced topics such as fine-tuning and quantization.
Ideal for Python Developers, DevSecOps Engineers, Project Managers, Data Acquisition Specialists, Architects, and Directors.
This course requires basic Python skills and provides access to a GPU-accelerated server for practical experience.
Class Details
Objectives
After taking this training, the student should be able to:
- Train and optimize Transformer models with PyTorch.
- Explain advanced prompt engineering.
- Identify AI architecture, especially Transformers.
- Write a real-world AI web application.
- Describe tokenization and word embeddings.
- Install and use frameworks like Llama-2.
- Apply strategies to maximize model performance.
- Describe model quantization and fine-tuning.
- Compare CPU vs. GPU hardware acceleration.
- Identify chat vs. instruct interaction modes.
Prerequisite Knowledge Advisory
Before taking this class, you should have:
- Working knowledge of Python (ex. PCEP Certification or equivalent experience)
- Familiarity with Linux
Required Exam for Gen AI Certification
Exam: (contact us)
Open Source Generative AI Training Class Outline
Module 1: Deep Learning Introduction
- Lecture: What is Intelligence?
- Lecture: Generative AI Unveiled
- Lecture: The Transformer Model
- Lecture: Feed Forward Neural Networks
- Lecture + Lab: Tokenization
- Lecture + Lab: Word Embeddings
- Lecture + Lab: Positional Encoding
Module 2: Building a Transformer Model from Scratch
- Lecture: PyTorch
- Lecture + Lab: Construct a Tensor from a Dataset
- Lecture + Lab: Orchestrate Tensors in Blocks and Batches
- Lecture + Lab: Initialize PyTorch Generator Function
- Lecture + Lab: Train the Transformer Model
- Lecture + Lab: Apply Positional Encoding and Self-Attention
- Lecture + Lab: Attach the Feed Forward Neural Network
- Lecture + Lab: Build the Decoder Block
- Lecture + Lab: Transformer Model as Code
Module 3: Prompt Engineering
- Lecture: Introduction to Prompt Engineering
- Lecture + Lab: Getting Started with Gemini
- Lecture + Lab: Developing Basic Prompts
- Lecture + Lab: Intermediate Prompts: Define Task/Inputs/Outputs/Constraints/Style
- Lecture + Lab: Advanced Prompts: Chaining, Set Role, Feedback, Examples
Module 4: Hardware Requirements
- Lecture: GPUs Role in AI Performance (CPU vs GPU)
- Lecture: Current GPUs and Cost vs Value
- Lecture: Tensor Core vs Older GPU Architectures
Module 5: Pre-trained Large Language Model (LLM)
- Lecture: A History of Neural Network Architectures
- Lecture: Introduction to the llama.cpp Interface
- Lecture: Preparing A100 for Server Operations
- Lecture + Lab: Operate LLaMA2 Models with llama.cpp
- Lecture + Lab: Selecting Quantization Level to Meet Performance and Perplexity Requirements
- Lecture: Running the llama.cpp Package
- Lecture + Lab: LLaMA Interactive Mode
- Lecture + Lab: Persistent Context with LLaMA
- Lecture + Lab: Constraining Output with Grammars
- Lecture + Lab: Deploy LLaMA API Server
- Lecture + Lab: Develop LLaMA Client Application
- Lecture + Lab: Write a Real-World AI Application Using the LLaMA API
Module 6: Fine Tuning
- Lecture + Lab: Using PyTorch to Fine Tune Models
- Lecture + Lab: Advanced Prompt Engineering Techniques
Module 7: Testing and Pushing Limits
- Lecture + Lab: Maximizing Model Limits
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Important Notice: NC-Expert does not accept enrollment applications from independent individuals. We require that employers pay for their employees. We continue to service corporate clients, using B2B transactions, with no change in service. We apologize for any inconvenience.
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