Practical Quantization for Large Language Models
Chapter 1: Foundations of Model Quantization
Introduction to Model Compression
Why Quantize Large Language Models?
Representing Numbers: Floating-Point vs. Fixed-Point
Integer Data Types in Quantization
Quantization Schemes: Symmetric vs. Asymmetric
Quantization Granularity Options
Measuring Quantization Error
Overview of Quantization Techniques
Quiz for Chapter 1
Chapter 2: Post-Training Quantization (PTQ)
Principles of Post-Training Quantization
Calibration: Selecting Representative Data
Static vs. Dynamic Quantization
Common PTQ Algorithms
Handling Outliers in PTQ
Applying PTQ to LLM Layers
Limitations of Basic PTQ
Hands-on Practical: Applying Static PTQ
Quiz for Chapter 2
Chapter 3: Advanced PTQ Techniques
Introduction to GPTQ
Understanding GPTQ Algorithm Mechanics
AWQ: Activation-aware Weight Quantization
SmoothQuant: Mitigating Activation Outliers
Comparing Advanced PTQ Methods
Implementation Considerations for Advanced PTQ
Hands-on Practical: Quantizing with GPTQ
Quiz for Chapter 3
Chapter 4: Quantization-Aware Training (QAT)
Need for Quantization-Aware Training
Simulating Quantization Effects During Training
Straight-Through Estimator (STE)
Implementing QAT with Deep Learning Frameworks
Fine-tuning Models with Quantization Nodes
Benefits and Drawbacks of QAT vs. PTQ
Practical Considerations for QAT Execution
Hands-on Practical: Setting up a Simple QAT Run
Quiz for Chapter 4
Chapter 5: Quantization Formats and Tooling
Overview of Common Quantized Model Formats
GGUF: Structure and Usage
GPTQ Format: Library Support and Implementation
AWQ Format Details
Working with Hugging Face Transformers and Optimum
Using bitsandbytes for Quantization
Tools for Model Conversion and Loading
Practice: Converting and Loading Quantized Formats
Quiz for Chapter 5
Chapter 6: Evaluating and Deploying Quantized LLMs
Metrics for Evaluating Quantized Models
Benchmarking Inference Speed and Memory Usage
Hardware Considerations for Quantized Inference
Deployment Strategies for Quantized LLMs
Troubleshooting Common Quantization Issues
Analyzing Accuracy vs. Performance Trade-offs
Practice: Benchmarking a Quantized LLM
Quiz for Chapter 6
Deployment Strategies for Quantized LLMs
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - A comprehensive guide on designing, building, and operating machine learning systems, including essential aspects of model deployment and MLOps practices.
- llama.cpp GitHub Repository, Georgi Gerganov and llama.cpp contributors, 2024 - The official repository for
llama.cpp, a C/C++ inference engine for LLMs optimized for CPU, supporting GGUF and various quantization formats for efficient on-device execution. - NVIDIA TensorRT Developer Guide, NVIDIA Corporation, 2024 (NVIDIA) - Official documentation for NVIDIA TensorRT, a software development kit for high-performance deep learning inference, supporting quantized models on NVIDIA GPUs.
- TensorFlow Lite Developer Guide, Google, 2024 (Google) - The official developer guide for TensorFlow Lite, providing tools and methods for deploying machine learning models, including quantized LLMs, on mobile, edge, and embedded devices.
- Deploy models for inference with Amazon SageMaker, Amazon Web Services, 2024 (Amazon Web Services) - Official AWS documentation on deploying models for inference using Amazon SageMaker, a managed service that facilitates hosting and managing ML models in the cloud.
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