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
Implementation Considerations for Advanced PTQ
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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- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers, Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh, 2022 ICLR DOI: 10.48550/arXiv.2210.17323 - The foundational paper for the GPTQ algorithm, detailing its method for accurate weight-only quantization of large language models at very low bitrates.
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models, Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han, 2022 ICML DOI: 10.48550/arXiv.2211.10438 - Presents SmoothQuant, a technique designed to mitigate challenges posed by activation outliers in post-training quantization by strategically shifting quantization difficulty from activations to weights.
- Hugging Face Optimum Documentation, Hugging Face, 2024 (Hugging Face) - Official documentation for Hugging Face Optimum, offering practical guides, tutorials, and API references for optimizing and quantizing models using various advanced techniques, including GPTQ and AWQ.
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