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Quantization methods are used to make models smaller,

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which makes them more accessible to the AI community.

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In this lesson, you'll get an overview of what Quantization is,

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and how it works. Let's get started.

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We have seen previously
that quantization is an exciting topic

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as it enables us to shrink models
for better accessibility to the community.

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In this lesson,
we will learn how to implement

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some quantization primitives from scratch,
and we will also implement

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our own model quantizer and cover
some challenges that anyone can face

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when it comes to lower bit quantization,
such as weights packing.

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Let's get started.

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So let's first have a quick glance
on what we have learned

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so far from the first course.

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So, in the introduction of the first
course, we listed all available techniques

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that one could use in order
to compress a model in general.

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So first of all, quantization
aims at representing

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parameters of the model
in a lower precision.

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With knowledge distillation,
you can train a student model

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using the bigger teacher model outputs.

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And finally, with pruning,
you can simply remove some connections

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inside the model, meaning removing weights
to make the model more sparse.

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We also covered
common data types in machine

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learning, such as INT8 or float.
We also performed

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linear quantization using Hugging Face's
quantum library with few lines of code.

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And finally,
we wrapped up the course with an overview

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how quantization can be leveraged
in different use cases,

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such as large language
models, finetuning.

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So let's see together what we are going
to cover exactly in this course.

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So, first of all, we are going
to deep dive together into the internals

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of linear quantization and implement
some of their variants from scratch,

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such as per channel,
per tensor or per group quantization.

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We will study what are the advantages
and drawbacks for each of these methods,

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and we will see their impact
on some random tensors.

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And next,
we will try to build our own quantizer

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to quantize any model in eight-bit

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precision using one of the quantization
schemes presented before.

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Note the quantization scheme
is agnostic to modalities,

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meaning you can apply to any model as long
as your model contains linear layers.

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Technically, you will be able
to use your quantizer to quantize

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a vision, text, audio,
or even a multimodal model.

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And finally, we will wrap up the course
by learning more

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about some challenges that you can face
when it comes to extreme quantization,

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such as weight packing,
which is a common challenge these days.

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As of the time we speak, PyTorch
does not have

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a native support for two-bit or four-bit
precision weights.

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One way to address
this issue is to pack these low precision

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weights into a higher precision tensor,
for example INT8.

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And we will deep dive into that

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and implement
packing and unpacking algorithms together.

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And we will end the course by covering
what are the other common challenges

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when it comes to quantizing large models
such as LLMs.

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And review together some state of the art
quantization methods together.

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Yeah, so let's try to get started
and shrink some models.