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The thing is.

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That's right. Now,
we tried everything on a dummy model.

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We want to test out
also on real use cases.

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Yeah. Meaning useful models.

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So let's test out our implementation
and our quantizer right away

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on models that you can find
on Hugging Face transformers.

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So let's get started.

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So for this demo
we're going to use this model called

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Salesforce Codegen 350 m mono.

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So this is a language model
that has been fine-tuned on code.

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And it has only 350 million parameters.

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Let's use transformers
to load the model together

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with the tokenizer
and get some generation.

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And let's use

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the text generation pipeline
in order to get some text generation.

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So we're going to load pipeline
text generation and pass the model.

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And the tokenizer.

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And then since

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it's a model that has been fine
tuned on code, let's try to battle

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test the model by giving it some code

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completion task, and try to see if we can

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generate some consistent text

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in order to print "Hello World" in Python.

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So the "Hello World"

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print "Hello World",
which seems to be correct.

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And then the model has also suggested
to call the method right after.

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And then,

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well, it ended up with some comments
in perhaps Korean but that's fine.

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Yeah. Overall the model seems good.

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And also bear in mind
that the model is a small model.

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It's, 350 million parameters.

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You might get more impressive results
with larger models, but anyway, yeah.

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Let's just out of curiosity,
print the model before quantization.

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So we're going to shrink the model,
quantize it in eight-bit

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and try to get some generations
on the quantize model.

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So yeah this
this is how the model looks like.

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You have a lot of linear layers because
it's a transformer based architecture.

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So most of the most of the weights come
from, some linear layers on the model.

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And let's call our

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quantization API

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replace linear
with target and quantize model.

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Target class.

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And as I said we're not going to quantize
the language model head

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because since the model
is an autoregressive model, it uses

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the output from the previous iteration
to get the output of the next iteration.

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If you quantize the language
model head, a lot of errors might

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might be accumulating
over the generation steps.

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And you will most likely end up,
having some gibberish after some tokens.

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And though this method modifies
the model in place.

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So we're just going to inspect

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pipe dot model and check
if the model has been correctly quantized.

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So yeah, we can confirm,
the model has been quantized by checking

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that the linear layers has been replaced
with the, W8A16 in our layers.

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We can also confirm that the language
model had is still a torch and then linear

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which is expected intended.

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So let's
do some generation and see the results.

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Perfect. So it's able to generate,

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correct methods of printing

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"Hello world" in Python.

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It's also try to call Hello world
in the main Python file

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but somehow the model has commented
the code to the method,

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but I guess that's fine.
For generative models,

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since the model generates
output from past inputs.

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So it's, it's an autoregressive model.

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All the rounding errors can sum up once
you start generating a lot of tokens,

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until maybe all of these errors
get super large so that it affects,

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the model's performance.

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This specifically affects larger models.

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So maybe greater
than 6 billion parameters.

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And the the whole no performance
degradation

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quantization for LLMs
is a whole exciting topic.

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And, it has been also addressed
by many recent papers,

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such as LLM.int8,
SmoothQuant, GPTQ, QLoRA,

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AWQ and so on, and

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probably many,
many more that I forgot to mention.

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And we're also going to briefly,

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explain a little bit the insights
behind these papers in the next lesson.

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All right.

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We can also try out to quantize models
from other modalities.

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I wanted to show you how to call the
quantizer on an object detection model.

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So the workflow is going to be typically
the same.

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We're going to call the API

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that we have designed on a model
that we will load from transformers.

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So we will use this class Detr for object
detection.

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So Detr is an architecture
that has been designed by Facebook AI

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that is used for object detection.

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So you can inspect the

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the model card on the hub
in order to get the code snippets

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on how to run the model.

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So this is the way to load the processor
and the model, from Hugging Face hub.

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And we're going to call our quantizer
on the model itself.

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Okay.

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So before quantizing the model
we can get the memory footprint

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of the model before quantizing it.

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So we just have to call model.

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get_memory_footprint.

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And the size of the model
should be something around 170MB.

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Perfect. Yeah.

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I want to quickly test the model before
quantizing it and visualize some results.

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So we recently went to

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dinner altogether
and we took a nice picture.

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So we're going to use this picture
and try to detect as many

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as many objects as possible
using the model.

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Perfect. Yeah.

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So if you if you check out the model
card of the model, you can get an idea of,

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what is the code snippet
to, to use in order to run the model?

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So we're just going to use that
and call plot results on the results.

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So very cool.

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it was able to detect,

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all the people here.

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So with the correct class person,

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it was also able to detect, the table
here on the left, the phone,

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the cups, the knife, and also the card
that is on the background.

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And even this laptop,
there is a bit far in the image.

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That's really cool.

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Yeah.

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So let's

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try to quantize the model and visualize
the results of the quantized model.

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So, before doing that let's quickly
inspect the model.

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So the impact of the quantization

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is going to be a little bit lower
than a language model.

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Because as you can see here.

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So there are many convolutional
based layers.

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So these layers
are not going to be quantized.

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But if you go down

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the layers that are going to be quantized
are going to be,

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the linear layers
that are here in the encoder and decoder

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here.

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And yeah,
so we're still going to keep that practice

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of trying to not quantize the last layer.

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So the bounding box predictor we're going
to keep it in its original precision.

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So we're going to call replace
linear layer model target class 012.

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So that we don't quantize these layers

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and the class labels classifier as well.

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And everything is specified here.

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Perfect. Let's inspect the model again.

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So yeah as expected
the convolution layers are still the same.

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And the encoder
and decoder layers seem to be

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correctly quantized in int8.

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Perfect.

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And of course we kept the last modules,
in their original precision.

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Let's visualize the results
with the quantize model.

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Perfect. Yeah.

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So yeah, I think we got
pretty much the same results.

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I think we were able to detect
the same instances.

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Yeah.

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Even the computer

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that you can see on the background,
the chairs and the car as well.

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Let's also try to have, a good idea
of how much memory did we managed to save.

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So this is the new memory footprint.

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So yeah, we were able to save around 50MB.

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So before we were around 160, 170.

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So yeah, it's a reduction of maybe around
25 to 30%.

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So that's that's not bad.

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And we managed to keep

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most of the capabilities of the model.

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So yeah.

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Now, I invite you to pause the video
and you can also try out,

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this approach
on other models, other modalities.

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You can also try to maybe, break
the quantizer and see what went wrong.

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Maybe, I don't know, try to also quantize
the last module to see

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how does it affect
the model's performance.

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And yeah, you can also try that out
on as many modalities as you want.

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You can try it on a vision model.

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You can also try it on an audio model,
on a multimodal model.

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So yeah, feel free to pause the video.

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Try out the API we designs together

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on other models.

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And also bear in mind
that, the API modifies the model in place.

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So once you have loaded the model
and called the quantizer on the model,

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you need to reload the model if you want
to compare it with its, original version.