Okay, so it seems like everyone's talking about large language models these days, especially
those massive ones like chat GPT.
But what about their more, I guess you could say efficient cousins, the small scale LLMs
or SLMs as they're called, they're gaining traction because, well, for one, they're more
affordable and they don't need as many resources, which is a pretty big deal, especially for
things like mobile devices.
It really is fascinating how much attention the big models get, but SLMs are kind of quietly,
you know, becoming the workhorses of AI, especially when you need something that's really efficient.
Exactly.
And that's kind of what we're looking at today.
We're doing a deep dive into a research paper that explores the training of these SLMs.
It's like a behind the scenes look at how researchers are making AI more accessible and, importantly,
less expensive to work with.
Yeah, the paper's called Computational Bottle Necks of Training Small Scale Large Language
Models, and it really digs into the nuts and bolts of how to make these smaller models
work.
Maybe for anyone who's new to all this AI stuff, can you break down what SLMs are and
why they're becoming so important?
Sure.
SLMs are language models with a smaller number of parameters.
We're talking like two billion or less.
I know, it might sound like a lot, but compared to models with hundreds of billions of parameters,
it's definitely small scale.
And there are three big reasons why they're getting so popular.
First, they're just more cost effective to train and run, which is huge.
Second, they don't need as much computing power so they can run in more places.
And third, they're often really fast and efficient at what they do.
So it's not just about making the model smaller, right?
I mean, there are other tricks that researchers use to make these smaller models perform well.
You're right, model size is important, but there are other things that come into play,
like techniques called pruning, distillation, and quantization.
Basically these are ways to refine and optimize the model.
And the results can be impressive.
The paper gives a cool example.
A two billion parameter model called Gemma2B actually beats a much larger model,
OPT175B, which has 175 billion parameters.
Wow, so bigger isn't always better, huh?
That's a really encouraging takeaway, especially if you don't have access to a ton of computing power.
Now, this research is specifically about the training process of SLMs.
What were some of the things the researchers were looking at?
What makes their approach different?
Well, one thing they did that was interesting was they didn't just measure the training time,
they also looked at things like loss per dollar and tokens per second.
These metrics give you a much more practical view of how efficient the training process is.
I'm really interested in that loss per dollar idea.
It just seems like a really practical way to think about AI training,
especially if you don't have a huge budget.
Can you explain a little more about why these metrics are more useful
than just looking at training time or the number of iterations?
Yeah, loss per dollar tells you how much improvement you get in the model's accuracy
for every dollar you spend on training.
So it's about getting the most bang for your buck.
And tokens per second basically measures how quickly the model is processing language,
you know, words or parts of words.
So it tells you how fast and efficient it is.
So it's not just about speed, it's about being smart with your resources
and getting the most out of them.
What specific models were the researchers working with?
Did they just pick one?
They focus on the Lama architecture, which is a pretty popular base for building SLMs.
They tweaked it to create models of different sizes, you know, 100 million parameters,
500 million, one billion and two billion.
That let them see how different factors affected training across different scales.
That makes sense.
Now, one of the things that caught my eye in the paper was this thing called flash attention.
What is that exactly?
And why does it seem to make such a big difference, especially for these smaller models?
Flash attention is a more efficient way to handle attention mechanisms within the model.
Attention basically means figuring out which words are the most important to understanding the sentence.
Flash attention helps to make this process a lot smoother and faster.
So it's like streamlining the way the model thinks.
And the researchers found this was especially helpful for SLMs.
Why is that?
Smaller models might not have as many calculations to do,
but moving data around can actually be a bottleneck.
Flash attention really helps with that, which makes training much more efficient.
And faster usually means less expensive to run.
Speaking of which, did they need some super fancy expensive hardware to train these SLMs effectively?
What did they learn about the hardware side of things?
That's where it gets interesting.
They actually tested different types of GPUs, you know, the A140 Jiggy, A180 Jiggy,
and even the powerful H180 Jiggy, and what they found might surprise you.
All right, I'm all ears.
What's the ideal setup?
Well, it turns out that the best choice depends on the size of the model and how many GPUs you're using.
That powerful H100 is great for big models, but it's not always the best choice if you're working with something smaller.
So you don't always need the biggest to most expensive option.
That's good news for smaller research teams or companies on a budget.
What did they find that worked well for these smaller models?
For a lot of SLM scenarios, the A140 Jiggy actually did the trick without sacrificing performance.
It's a more affordable option too.
That's a great takeaway.
Now, besides flash attention of the type of GPU, what other factors did the researchers find were important for training SLMs effectively?
One key thing was how the multiple GPUs communicate with each other during training.
They compared two approaches, distributed data parallel, or DDP, and fully sharded data parallel known as FSDP.
And what was the verdict?
Which one was better?
Well, it wasn't a clear winner.
It really depends on the size of the model again.
I'm sensing a theme here.
It seems like a lot of these optimization choices are context dependent.
So tell me more about what they found with these communication schemes.
For smaller models, DDP was more efficient because it doesn't require as much communication.
However, for that largest two billion parameter model, FSDP worked better, especially when training with larger batches of data.
So finding the right tool for the job.
Anything else in the research that stood out to you?
You know, there was one finding that kind of challenged a common assumption in the field.
Ooh, I love when research debunks conventional wisdom.
Tell me more.
Well, there's this idea that you should always try to max out your GPU memory utilization to train models as efficiently as possible.
You know, use every bit of memory you have.
But the researchers actually found that this isn't always the case, especially with SLMs.
Really?
Why would using less memory be better?
That doesn't seem very intuitive.
It comes down to the balance between doing calculations and communicating.
If you cram as much data as you can onto a GPU, you might speed up the calculations,
but you can also create a bottleneck when it comes to the GPUs talking to each other.
They end up spending more time communicating than actually crunching numbers.
So it's like giving the GPUs a little breathing room, even if it means not using all of the memory.
Yeah.
And that's just one of the interesting takeaways from this paper that we'll be looking at further.
It's really clear that training these SLMs effectively is a complex process.
Welcome back. We're continuing our deep dive into these smaller, large language models.
Yeah, picking up where we left off.
Still trying to figure out the best ways to train these compact, but powerful SLMs.
And remember, we're looking at this research paper that digs into all the challenges researchers run into
when they're training these models.
It has been pretty eye-opening so far.
Flash attention, different GPUs, balancing memory and communication speed.
It's a lot to take in.
But let's dive into some of the specific findings.
What did the researchers actually discover, especially in terms of those practical metrics you mentioned earlier?
Well, one thing that stood out was just how much flash attention improved the tokens per second metric,
especially for the smaller models.
That makes sense, right?
Given how flash attention kind of streamlines that whole attention mechanism
and gets rid of those data movement bottlenecks.
Exactly.
And it was even more noticeable when they were working with smaller models and smaller batches of data.
Why is that?
Are smaller models just more sensitive to those bottlenecks or something?
I think it's more about the relative cost.
In those cases, the time it takes to move data around can actually be a bigger problem
than the time it takes to actually do the calculations.
Flash attention helps reduce that bottleneck, so training is faster.
So it's all about optimizing that flow of information within the model.
And of course, as we said, that's your training usually means saving money.
What about those GPU findings?
Did the researchers have any advice on which ones to use?
They did, and you might be surprised.
While the really powerful GPUs like the H100 are considered the gold standard,
for a lot of these SLM situations, they found that the A140GB,
which is less expensive, could do the job just as well.
So you don't always have to go for the most expensive hardware.
That's great news for smaller research teams or companies
that might not have a ton of money to throw around.
Totally.
It makes things a lot more accessible.
Yeah.
Let's talk about how those multiple GPUs talk to each other during training.
I know they were looking into that.
What did they find out?
They focused on two main methods, distributed data parallel,
DDP, and fully sharded data parallel, or FSDP.
And again, the best choice really depended on how big the model was.
OK, so when is one better than the other?
For the smaller models, DDP was more efficient because it doesn't involve
as much back and forth communication.
But for that biggest model they tested, the 2 billion parameter one,
FSDP was the way to go, especially with those larger batches of data.
It's like choosing the right communication style for the situation.
Anything else jump out at you from the research?
Oh, yeah.
They also looked at this idea that you always have to max out the GPU memory
to get the most efficient training.
But they found that might not always be the case,
at least for these smaller models.
We talked about that before.
It seemed weird that using less memory could actually be better.
Can you remind me why that is again?
It's kind of like this.
When you put a ton of data onto one GPU,
yeah, it might speed up the calculations,
but then you can run into problems with the GPUs communicating with each other.
They spend more time passing data back and forth instead of processing it.
So sometimes it's better to give them a little space to breathe,
even if it means they're not using every last bit of memory?
Exactly.
And that's just one of the many interesting things we're going to unpack further.
It's clear that training these SLMs effectively,
well, it's not simple task.
No, it's not.
So, zooming out a bit,
what do you think all of this means for the future of AI,
especially in areas where resources might be limited?
That's a great question.
I think this research could make AI development a lot more accessible.
I mean, if you don't always need the most expensive hardware and the biggest models,
it opens up opportunities for smaller teams, startups,
people who might not have had those resources before.
It levels the playing field.
I love that.
Exactly.
And that could lead to so much more creativity and innovation in the field.
I'm also thinking about what this means for AI on our phones.
As these SLMs became easier to train and more efficient,
we could see a whole wave of new AI-powered apps and features, right?
Absolutely.
Imagine language models running right on your phone,
personalized features without needing the cloud,
better translations, smarter voice assistants,
maybe even AI-powered tools for creating things.
The possibilities are huge.
That's a future I'm definitely excited about.
But like with any new technology,
we need to think about the potential downsides, too.
What are some of the challenges we need to be aware of
as these models become more common?
I think one concern is making sure these models are used responsibly and ethically.
The more accessible they become,
the more important it is to think about how they could be misused.
That's a really important point.
We can't get so caught up in the possibilities
that we forget about the ethical side of things.
Exactly.
And that's what makes research like this so important.
By understanding how these models work and what makes them tick,
we can start to think critically about their impact, both good and bad.
This has been a great conversation so far.
We've covered a lot, but it feels like we've just scratched the surface.
There's always more to learn.
That's what makes AI so interesting.
It's always changing and pushing us to think in new ways.
Speaking of pushing boundaries,
let's go back to that question about the implications of this research for inference.
How does all this affect the way these models are actually used
in the real world on devices like phones?
That's a key point.
The study focused on training efficiency,
but the findings definitely matter for inference, too.
For example, if flash attention can speed up training,
it makes sense that it could also lead to faster, more efficient inference,
especially on devices with limited resources.
So those same optimizations that make training better
could also translate into a smoother user experience in the real world.
Exactly.
It opens up a lot of exciting possibilities,
especially for that mobile AI future we were talking about.
The researchers were looking at training efficiency,
but how do these smaller models actually perform compared to the big ones?
Can they do the same things?
Are they as accurate?
That's the big question, isn't it?
This particular study didn't really go into that in depth,
but there's more and more research coming out
that suggests these SLMs can actually do really well.
In some cases, they even outperform the larger models.
Really? Any specific examples of that?
Well, they mentioned one in the paper.
Gemma2b, that two billion parameter model,
actually beat OPT175B, which is a much larger model
with 175 billion parameters.
So it's not always about just making the model bigger.
It's about using the right techniques and optimizing the heck out of it.
Another underdog story in the AI world.
It seems like we keep seeing that.
But I wonder if there's a trade-off between efficiency and performance.
At what point does making things more efficient
start to hurt the accuracy or the capabilities of the model?
That's a good question.
It's something researchers are still working on.
There's a balance to strike.
We want efficiency, especially for things like phones,
but we don't want to sacrifice too much performance.
Like finding that sweep spot between speed and accuracy.
Exactly. And that sweet spot might be different
depending on what you want the model to do.
It's definitely a complex area,
but it's amazing to see all the progress.
I have a feeling these SLMs are going to be a big deal moving forward.
I agree.
They have the potential to change everything about how we use AI.
And as we move towards that future,
we need to remember to be careful and thoughtful.
We want to make sure these technologies are being used ethically
and for the benefit of everyone.
Absolutely.
That's why conversations like this are so important.
We need to explore these new developments
and really think about their impact.
And with that, we're going to take a quick break
and come back to wrap up our deep dive into the world
of small scale, large language models.
And we're back.
Back for the final part of our deep dive
into the world of small scale, large language models or SLMs.
Yeah, it's been a fascinating journey.
We've gone from flash attention to GPU selection
and uncovered some really interesting insights from this research.
We've talked about how important these smaller models are becoming,
especially with that focus on efficiency
and making AI more accessible.
Especially as AI starts making its way onto mobile devices,
it's incredible to see how researchers are making
these powerful models more compact and less resource intensive.
It really shows how creative people are in this field.
And this research is a great example of that.
It gives us a glimpse into how they're optimizing
the whole SLM training process, highlighting those factors
that can really make or break a model.
Before we wrap up, I want to circle back to something
you mentioned earlier about the impact of all of this on inference.
Actually using a trained model in the real world.
Right.
It's one thing to train a model efficiently,
but how does that translate to real world performance,
especially on devices like phones?
That's a big question.
Exactly.
The study we've been discussing was all about training efficiency.
But what about inference efficiency?
Do the choices you make during training
affect how well these models actually perform
on devices with limited resources?
That's definitely something that needs more research.
We know that things like flash attention can speed up training,
but could they also lead to faster and more efficient inference?
It seems likely, but it's something that needs to be explored.
It would be incredible to have an SLM on your phone
that could translate languages instantly,
or give you personalized recommendations
without draining your battery.
That's the dream.
And this research suggests that it might not be that far off.
It's exciting to think about the possibilities,
but as we've been discussing, we also need to be careful.
Of course, any powerful technology
comes with potential risks and challenges.
One thing we talked about was bias in these models.
If an SLM is trained on bias data,
it's going to reflect those biases in its output,
maybe even amplify them.
Absolutely.
And that could have serious consequences,
especially in areas like health care or criminal justice.
We need to be very aware of these risks
and make sure we're developing ways to reduce bias,
both in the data and the models themselves.
It's a responsibility we all share, researchers, developers,
and users, to make sure that these technologies
are used ethically and fairly.
Well said.
So as we wrap up this deep dive,
what are your final thoughts on the potential of SLMs
and the future of AI?
You know, I'm really optimistic about the future of SLMs.
I think they have the potential to change
how we interact with technology in a fundamental way,
bringing AI into our daily lives in ways
we haven't even imagined yet.
I completely agree.
It's been fantastic exploring this research with you,
and I hope our listeners have found it insightful as well.
If you're interested in learning more about SLMs,
I encourage you to check out the research we've been discussing
and see where it takes you.
And remember, the world of AI is constantly evolving,
so keep learning, keep exploring, and keep asking questions.
Thanks for joining us on this deep dive
into the world of small-scale, large-language models.
Until next time, stay curious.