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
Calibration: Selecting Representative Data
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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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- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference, Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko, 2018 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR.2018.00097 - A foundational paper introducing the principles of post-training quantization and quantization-aware training, detailing the role of activation calibration for static quantization.
- Post Training Static Quantization, PyTorch Documentation, 2019 (PyTorch Foundation) - Provides practical guidance and API details for implementing static post-training quantization in PyTorch, including how to collect and use calibration data.
- Integer Quantization for Deep Learning Inference: Principles and Empirical Advances, Ron Banner, Yaniv Landa, Alexander Fefelov, Elad Hoffer, 2019 Journal of Signal Processing Systems, Vol. 91 (Springer US) DOI: 10.1007/s11265-019-01460-1 - Discusses the fundamental principles and advancements in integer quantization, covering activation range estimation and the use of calibration data for scale and zero-point determination.
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