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Deploying Quantized LLMs for Efficient Inference

Chapter 1: Advanced LLM Quantization Fundamentals

Revisiting Quantization Principles for Large Models

Low-Bit Quantization Techniques (Below INT8)

Understanding Quantization Data Types and Formats

Post-Training Quantization (PTQ) Algorithms for LLMs

Quantization-Aware Training (QAT) Considerations

Mixed-Precision Quantization Strategies

Calibration Data Selection and Preparation

Hands-on Practical: Applying GPTQ to an LLM

Chapter 2: Implementing LLM Quantization with Toolkits

Overview of LLM Quantization Libraries

Using bitsandbytes for Low-Bit Operations

Quantization with Hugging Face Transformers and Accelerate

Applying GPTQ using AutoGPTQ

Applying AWQ using AutoAWQ

Comparing Toolkit Outputs and Performance

Handling Model Compatibility Issues

Practice: Quantizing Models with Multiple Toolkits

Chapter 3: Performance Evaluation of Quantized LLMs

Metrics for Quantized Model Evaluation

Measuring Inference Latency and Throughput

Assessing Memory Consumption (Disk and Runtime)

Evaluating Accuracy Degradation

Benchmarking Frameworks and Tools

Analyzing Performance on Target Hardware

Visualizing Performance Trade-offs

Hands-on Practical: Benchmarking a Quantized LLM

Chapter 4: Optimizing and Deploying Quantized LLMs

Inference Optimization Techniques Post-Quantization

Choosing the Right Deployment Framework

Deploying with Text Generation Inference (TGI)

Leveraging vLLM for High-Throughput Inference

GPU Optimization with NVIDIA TensorRT-LLM

Deployment using ONNX Runtime

Containerization and Scaling Strategies

Monitoring Deployed Quantized Models

Hands-on Practical: Deploying via an Inference Server

Chapter 5: Addressing Advanced Challenges

Mitigating Accuracy Loss in Low-Bit Regimes

Handling Activation and Weight Outliers

Quantizing Specific LLM Components (Attention, Normalization)

Hardware Constraints and Kernel Availability

Dynamic Quantization vs. Static Quantization Trade-offs

Debugging Quantization Issues

Integrating Quantized Models into Production Pipelines

Practice: Fine-tuning Quantization Parameters

Implementing LLM Quantization with Toolkits

Following the theoretical exploration of advanced quantization techniques in the previous chapter, this section focuses on the practical application of these methods using common software libraries. The objective is to move from understanding concepts like low-bit quantization (e.g., $INT4$ ) and algorithms such as GPTQ and AWQ to actually performing these operations on Large Language Models.

You will work with widely-used toolkits designed for LLM quantization:

Learn how bitsandbytes facilitates efficient low-bit operations.
See how to integrate quantization directly within the Hugging Face ecosystem using Transformers and Accelerate.
Apply GPTQ and AWQ using the AutoGPTQ and AutoAWQ libraries, respectively.

Throughout this chapter, we will cover the necessary steps to quantize models using these tools, examine how to compare the results and performance characteristics obtained from different libraries, and address potential compatibility challenges between models and toolkits. By the end, you will have hands-on experience using these libraries to prepare LLMs for efficient deployment.

Sections

2.1 Overview of LLM Quantization Libraries
2.2 Using bitsandbytes for Low-Bit Operations
2.3 Quantization with Hugging Face Transformers and Accelerate
2.4 Applying GPTQ using AutoGPTQ
2.5 Applying AWQ using AutoAWQ
2.6 Comparing Toolkit Outputs and Performance
2.7 Handling Model Compatibility Issues
2.8 Practice: Quantizing Models with Multiple Toolkits

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