HuggingFace.Quantization_in_Depth/Transcript/0_Introduction_HFQD.txt

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Welcome to this short course

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"Quantization in Depth,"
built in partnership with Hugging Face.

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In this course
you deep dive into the core technical

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building blocks of quantization,
which is a key part of the AI software

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stack for compressing
large language models and other models.

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You implement from scratch

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the most common variants of linear
quantization, called

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asymmetric and symmetric modes,
which relate to whether

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compression algorithm maps zero
in the original representation,

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to zero in decompress representation,

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or if is allowed to shift
the location of that zero.

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You also implement different forms
of quantization, such as a per tensor

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per channel, and per group quantization
using PyTorch,

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in which you can decide

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how big a chunk of your model
you want to quantize at one time.

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You end up building a quantizer
to quantize any model

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in eight-bit precision
using per channel linear quantization.

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If some of the terms I use don't make
sense yet, don't worry about it.

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These are all key

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technical concepts in quantization
that you learn about in this course.

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And in addition to understanding
all these quantization options,

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you also hone your intuition
about when to apply which technique.

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I'm delighted to introduce our instructors
for this course.

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Younes Belkada, a machine
learning engineer at Hugging Face

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has been involved in the open source team,

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where he works at the intersection
of many open source tools

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developed by Hugging Face
such as transformers, PETF, and TRL.

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And also Marc Sun, who's also a machine
learning engineer at Hugging Face.

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Marc is part of the Open source team,
where he contributes to libraries

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such as transformers or Accelerate.

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Marc and Younes are also deeply involved
in quantization

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in order to make large models
accessible to the community.

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Thanks, Andrew.

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We are excited to work with you
and your team on this.

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In this course, you will directly try
your hand on implementing

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from scratch
different variants of linear quantization,

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symmetric and asymmetric mode.

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You will also implement
different quantization granularities, such

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as per tensor, per channel
and per group quantization in pure PyTorch.

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Each one of these algorithms having
their own advantages and drawbacks.

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After that,
you'll build your own quantizer

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in order to quantize any model
in eight-bit precision.

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Using the per channel quantization scheme
that you have seen right before.

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You will see that you'll be able
to apply this

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method to any model regardless
of its modality, meaning you can apply

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

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Once you are happy with the quantizer,
it will try your hands on

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addressing common challenges
in quantization.

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At the time, we speak the most common
way of storing low-bit precision

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weights, such as four-bit or two-bit,
seemed to be weight spiking.

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With weight spiking,
you can pack altogether 2 or 4 bits

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tensors in a larger eight-bit tensor
without allocating any extra memory.

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We will see together
why this is important,

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and you will implement from scratch
packing and unpacking algorithms.

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Finally, we will learn together
about other challenges

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when it comes to quantizing large models
such as LLMS.

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We will review together current state
of the art approaches in order to perform

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no performance degradation

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quantization on LLMs and go through
how to do that within the Hugging Face

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ecosystem.

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Quantization is a really important part

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of practical use of large models today.

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So having in-depth knowledge of it
will help you to build,

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deploy, and use models more effectively.

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Many people have worked
to create this course.

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I like to thank on the Hugging Face

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side, the entire Hugging Face team
for the review of this course content,

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as well as the Hugging Face community
for their contributions

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to open source models and quantization
methods. From DeepLearning.AI,

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Eddy Shyu,
had also contributed to this course.

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Quantization is a fairly technical topic.

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After this course,
I hope you deeply understand it

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so you better say to others, "I now get it.

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I'm not worried about model compression."

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In other words, you can say:
"I'm not sweating the small stuff."

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Let's go on to the
next video and get started.