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We're going to wrap up the whole course
with these explanations.

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If you have followed the previous lab,
I've quickly mentioned,

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the notion of emergent features
for large language models.

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And this is part of
like one of the biggest challenges

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when when it comes to quantizing
large language models.

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Once in the open source community,
we had more and more large language

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models such as OPT the opened
pre-trained transformers from Facebook.

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In 2022, researchers started to directly
dive into the capabilities of the model,

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and they discovered
some so-called emergent features at scale.

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What do we mean
exactly by emergent features?

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Simply, some characteristics
or features that appear at scale

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so when the model is large.

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So it turns out that for some models
that scale

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the features predicted by the model,

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meaning the magnitude of the hidden states

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started to get large,
thus making the classic quantization

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schemes quite obsolete,
which led to, you know, classic,

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linear quantization algorithms,

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just failing on those models.

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Many papers today, since open
sourcing these large language models,

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decided to tackle this specific challenge
on how to deal

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with outlier features for large language
models.

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Again, outlier features simply means
hidden states with large magnitude.

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So there are some interesting papers,
such as Int8, SmoothQuant,

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AWQ, and I wanted to give

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a brief explanation of each paper
to just give you some insights of

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what could be the potential solutions
to address this specific issue.

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So LLM.int8

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proposes to decompose
the underlying

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matrix multiplication
of the linear layers in two stages.

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So if you consider the input hidden states

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that you can see in the big matrix here,

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it is possible
to decompose the matmul in two parts.

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So the outlier part,
all the hidden states that are greater

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than certain threshold
and the non outlier part.

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The idea is very simple.

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So you decompose the input into

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perform the non outlier

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part matrix multiplication in Int8.

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So you quantize you do
the matmul in eight-bit and then you do

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dequantize using the scales so that you get
the final results in the input datatype.

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And the second part, you do it
classically,

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with the original dtype
of the hidden state.

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So usually in half precision.

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And then you combine both results.

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So this way
it has been proven that you can,

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retain the full

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performance of the model
without any performance degradation.

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Another very interesting approach
is called SmoothQuant.

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SmoothQuant specifically applies
to A8W8 schemes.

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Meaning
we also want to quantize the activations.

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So meaning both the activation and
the weights are in eight bit precision.

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So the paper also tackles this issue of
outlier features in large language models.

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And they proposed to mitigate that

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by smoothening
both the activation and the weights.

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Given a factor that you determine
based on the input activation

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to migrate the quantization difficulty
in both during

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the quantization of the activations,
but also quantization of the weights.

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So that way you transfer the quantization
difficulty or all over to the weights

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equally to the weights
and to the weights and the activation.

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And that way you can also retain
the full capabilities of the model.

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A more recent paper called AWQ,

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also treats
the outlier feature in a special way.

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So the paper, which came out also from
the same lab as the SmoothQuant paper,

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proposes to first iterate over a dataset

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that we are going to call
a calibration dataset

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to get detailed idea of which channel

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in the input weights

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could be responsible of generating
outlier features called salient weights.

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And the idea is,
to use that information

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to scale the model weights
before quantization,

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and also use that scale during inference
to rescale the input as well.

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So these are just a few of them.

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There are numerous other papers
that specifically address

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this issue for an effective and efficient
large language model

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quantization.

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So here is a non-exhaustive list
of those quantization techniques.

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But perhaps you can find much more
at the time we speak.

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So yeah, if you are curious about this,
I invite you to read these papers

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in detail.

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And, you know, just dive into them
and try to understand these papers.

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These are one of the challenges
when it comes to quantizing

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large language models,
because the models are quite large.

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You can get some surprising behavior.

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There are also other challenges.

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So it seems the Quantization,
Aware Training field

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seems to be a little bit
maybe underexplored today.

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So training models in low bit

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could be also
an interesting topic to dive into.

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there is also this challenge on limited
hardware support.

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So right now for this course
we only focused on W8A16 scheme,

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meaning the weights are in eight bit
but the activations are in 16 bits.

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But for a more efficient
quantization scheme, you may

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be also interested in other schemes
such as W8A8 as well.

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But not all hardwares
do support eight bit operations.

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There is also this challenge around
calibration dataset.

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So for some quantization
methods, you need to have

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a calibration dataset
to perform some sort of,

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model pre-processing
to make the quantization model better.

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And also in terms of distribution
packing and unpacking.

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So yeah, if you are really interested
about this topic,

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I invite you to do some further
reading through

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for example,
the state of the art quantization papers.

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There is also a lab called MIT Han lab,

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which made some of these, state
of the art quantization papers.

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So they have also good resources on
which you can learn more about this topic.

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You can also check out the

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Hugging Face Transformers
quantization documentation and blog post.

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You can also,
have a look at the llama.cpp repository

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discussions where you can find really
some insightful experiments and talk.

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You can also check out Reddit.

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So there is a subreddit called r/LocalLlama

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where they share a lot of cool insights
about quantization

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and you can also you can also learn more
about the new method that come up

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and so on.

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And then of course,
probably missing many more resources.

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but yeah,
these are the ones that, that I know.

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So that's it for this lesson.

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So I hope you learned a lot, through this
course and that you can use,

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the things that we have showed, to you,

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for your work or for your projects
and that all of this

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could give you some ideas of cool things
that you can do around you.

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So, yeah,
we're going to move on to the next video.

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Yeah.

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We're we'll say thank you for,

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going through this course
and suggest potential next steps.

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See you there.