LLM Model Quantization: An Overview
A General Introduction and Overview of LLM Model Quantization Techniques and Practices
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What You Will Learn!
- Understand the fundamental principles of model quantization and its critical role in optimizing Large Language Models (LLMs) for diverse applications.
- Explore and differentiate between various types of model quantization methods, including post-training quantization, quantization-aware training.
- Gain proficiency in implementing model quantization using major frameworks like TensorFlow, PyTorch, ONNX, and NVIDIA TensorRT.
- Develop skills to effectively evaluate the performance and quality of quantized LLMs using standard metrics and real-world testing scenarios.
Description
Course Description:
This course offers a deep dive into the world of model quantization, specifically focusing on its application in Large Language Models (LLMs). It is tailored for students, professionals, and enthusiasts interested in machine learning, natural language processing, and the optimization of AI models for various platforms. The course covers fundamental concepts, practical methodologies, various frameworks, and real-world applications, providing a well-rounded understanding of model quantization in LLMs.
Course Objectives:
Understand the basic principles and necessity of model quantization in LLMs.
Explore different types and methods of model quantization, such as post-training quantization, quantization-aware training, and dynamic quantization.
Gain proficiency in using major frameworks like PyTorch, TensorFlow, ONNX, and NVIDIA TensorRT for model quantization.
Learn to evaluate the performance and quality of quantized models in real-world scenarios.
Master the deployment of quantized LLMs on both edge devices and cloud platforms.
Course Structure:
Lecture 1: Introduction to Model Quantization
Overview of model quantization
Significance in LLMs
Basic concepts and benefits
Lecture 2: Types and Methods of Model Quantization
Post-training quantization
Quantization-aware training
Dynamic quantization
Comparative analysis of each type
Lecture 3: Frameworks for Model Quantization
PyTorch's quantization tools
TensorFlow and TensorFlow Lite
ONNX quantization capabilities
NVIDIA TensorRT's role in quantization
Lecture 4: Evaluating Quantized Models
Performance metrics: accuracy, latency, and throughput
Quality metrics: perplexity, BLEU, ROUGE
Human evaluation and auto-evaluation techniques
Lecture 5: Deploying Quantized Models
Strategies for edge device deployment
Cloud platform deployment: OpenAI and Azure OpenAI
Trade-offs, benefits, and challenges in deployment
Target Audience:
AI and Machine Learning enthusiasts
Data Scientists and Engineers
Students in Computer Science and related fields
Professionals in AI and NLP industries
Who Should Attend!
- Anyone who is interested in learning about model quantization, the steps, and the process.