How deeply does Generative AI in Action cover quantization?

Defines reduced-precision representation and explains benefits and risks, but stops short of a quantization workflow or hardware-specific implementation.

Quantization

As explained in Generative AI in Action

Quantization is the reduction of numerical precision used to represent model parameters, typically to reduce memory use and computational cost.

Quantization reduces the precision used to represent model parameters, commonly converting 32-bit or 16-bit floating-point weights and biases to 8-bit or 4-bit values. Smaller representations reduce memory and computation requirements, making large language models easier to deploy and run efficiently. section 9.6.1

Use quantization to improve deployment efficiency, while evaluating its trade-offs:

Run smaller models on consumer-class GPUs by reducing memory requirements. section 5.3.2
Speed inference and improve energy efficiency, although accuracy loss or hardware inefficiency can result from poor choices. section 5.3.2 section 9.6.1
Distinguish quantization from LoRA: quantization reduces numerical precision, whereas LoRA freezes pretrained weights and adds trainable low-rank matrices for adaptation. section 9.6.1

LLM deployment example

A small Code Llama model can run on a consumer-class GPU after quantization. In a related adaptation workflow, QLoRA quantizes low-rank matrices and uses an 8-bit optimizer, reducing parameters while maintaining or improving performance. section 5.3.2 section 9.6.1

How Generative AI in Action covers it

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Also explained in

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LLMs in ProductionQuantization is the conversion of model parameters or computations from higher-precision numerical representations to lower-precision representations.
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Domain-Specific Small Language ModelsMethods that reduce numeric precision or representational size of model values to improve efficiency, especially for inference; here the absmax method is a specific quantization procedure.
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AI-Powered SearchQuantization is the transformation of numerical values into a smaller set of discrete values to reduce representation precision and resource requirements.
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Deep Learning with Python, Third EditionThe technique of reducing numeric precision of model weights or activations to speed up computation while preserving output behavior as much as possible.
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Deep Learning with R, Third EditionModel quantization is the conversion of model computations or parameters to lower-precision numerical representations to reduce resource requirements.
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Math and Architectures of Deep LearningThe conversion of floating-point region boundaries or ratios into integer-aligned indices for feature extraction.
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Deep Learning with PyTorch, Second EditionA model optimization technique that reduces numerical precision of parameters and operations to lower memory use and often speed up inference.
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Transformers in ActionQuantization is the transformation of numerical model parameters or activations into lower-precision representations to reduce memory consumption or computational cost.
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The Complete Obsolete Guide to Generative AIA quantized model is a machine-learning model whose numerical parameters or activations are represented at reduced precision to lower resource requirements.
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Time Series Forecasting Using Foundation ModelsQuantization is the conversion of continuous numerical values into a finite set of discrete levels or tokens.
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Natural Language Processing in Action, Second EditionQuantization is the transformation of numerical values into lower-precision discrete representations, often to reduce memory use and accelerate computation.
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Building Reliable AI SystemsQuantization reduces the numerical precision used to represent model values, often lowering storage or computation requirements.

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Related concepts

QLoRAused by · QLoRA is a parameter-efficient fine-tuning method that quantizes a pretrained model’s weights while training low-rank adapters to reduce memory requirements.
scalar quantizationincludes · Scalar quantization maps numerical scalar values to a finite set of discrete levels, typically reducing their precision or range.
QA-LoRAused by · QA-LoRA is a low-rank adaptation method that integrates quantization-aware operations and group-wise processing to support efficient low-bit adaptation.
post-training quantizationincludes · Post-training quantization is the conversion of a trained model’s parameters or computations to lower-precision representations without additional training.
quantization-aware trainingincludes · Quantization-aware training optimizes a model while simulating or incorporating reduced-precision effects so it adapts to quantized representations.
PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.