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Okay, so it seems like everyone's talking about large language models these days, especially

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those massive ones like chat GPT.

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But what about their more, I guess you could say efficient cousins, the small scale LLMs

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or SLMs as they're called, they're gaining traction because, well, for one, they're more

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affordable and they don't need as many resources, which is a pretty big deal, especially for

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things like mobile devices.

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It really is fascinating how much attention the big models get, but SLMs are kind of quietly,

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you know, becoming the workhorses of AI, especially when you need something that's really efficient.

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Exactly.

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And that's kind of what we're looking at today.

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We're doing a deep dive into a research paper that explores the training of these SLMs.

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It's like a behind the scenes look at how researchers are making AI more accessible and, importantly,

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less expensive to work with.

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Yeah, the paper's called Computational Bottle Necks of Training Small Scale Large Language

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Models, and it really digs into the nuts and bolts of how to make these smaller models

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work.

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Maybe for anyone who's new to all this AI stuff, can you break down what SLMs are and

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why they're becoming so important?

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Sure.

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SLMs are language models with a smaller number of parameters.

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We're talking like two billion or less.

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I know, it might sound like a lot, but compared to models with hundreds of billions of parameters,

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it's definitely small scale.

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And there are three big reasons why they're getting so popular.

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First, they're just more cost effective to train and run, which is huge.

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Second, they don't need as much computing power so they can run in more places.

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And third, they're often really fast and efficient at what they do.

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So it's not just about making the model smaller, right?

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I mean, there are other tricks that researchers use to make these smaller models perform well.

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You're right, model size is important, but there are other things that come into play,

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like techniques called pruning, distillation, and quantization.

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Basically these are ways to refine and optimize the model.

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And the results can be impressive.

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The paper gives a cool example.

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A two billion parameter model called Gemma2B actually beats a much larger model,

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OPT175B, which has 175 billion parameters.

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Wow, so bigger isn't always better, huh?

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That's a really encouraging takeaway, especially if you don't have access to a ton of computing power.

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Now, this research is specifically about the training process of SLMs.

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What were some of the things the researchers were looking at?

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What makes their approach different?

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Well, one thing they did that was interesting was they didn't just measure the training time,

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they also looked at things like loss per dollar and tokens per second.

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These metrics give you a much more practical view of how efficient the training process is.

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I'm really interested in that loss per dollar idea.

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It just seems like a really practical way to think about AI training,

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especially if you don't have a huge budget.

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Can you explain a little more about why these metrics are more useful

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than just looking at training time or the number of iterations?

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Yeah, loss per dollar tells you how much improvement you get in the model's accuracy

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for every dollar you spend on training.

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So it's about getting the most bang for your buck.

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And tokens per second basically measures how quickly the model is processing language,

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you know, words or parts of words.

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So it tells you how fast and efficient it is.

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So it's not just about speed, it's about being smart with your resources

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and getting the most out of them.

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What specific models were the researchers working with?

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Did they just pick one?

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They focus on the Lama architecture, which is a pretty popular base for building SLMs.

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They tweaked it to create models of different sizes, you know, 100 million parameters,

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500 million, one billion and two billion.

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That let them see how different factors affected training across different scales.

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That makes sense.

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Now, one of the things that caught my eye in the paper was this thing called flash attention.

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What is that exactly?

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And why does it seem to make such a big difference, especially for these smaller models?

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Flash attention is a more efficient way to handle attention mechanisms within the model.

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Attention basically means figuring out which words are the most important to understanding the sentence.

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Flash attention helps to make this process a lot smoother and faster.

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So it's like streamlining the way the model thinks.

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And the researchers found this was especially helpful for SLMs.

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Why is that?

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Smaller models might not have as many calculations to do,

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but moving data around can actually be a bottleneck.

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Flash attention really helps with that, which makes training much more efficient.

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And faster usually means less expensive to run.

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Speaking of which, did they need some super fancy expensive hardware to train these SLMs effectively?

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What did they learn about the hardware side of things?

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That's where it gets interesting.

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They actually tested different types of GPUs, you know, the A140 Jiggy, A180 Jiggy,

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and even the powerful H180 Jiggy, and what they found might surprise you.

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All right, I'm all ears.

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What's the ideal setup?

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Well, it turns out that the best choice depends on the size of the model and how many GPUs you're using.

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That powerful H100 is great for big models, but it's not always the best choice if you're working with something smaller.

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So you don't always need the biggest to most expensive option.

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That's good news for smaller research teams or companies on a budget.

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What did they find that worked well for these smaller models?

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For a lot of SLM scenarios, the A140 Jiggy actually did the trick without sacrificing performance.

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It's a more affordable option too.

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That's a great takeaway.

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Now, besides flash attention of the type of GPU, what other factors did the researchers find were important for training SLMs effectively?

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One key thing was how the multiple GPUs communicate with each other during training.

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They compared two approaches, distributed data parallel, or DDP, and fully sharded data parallel known as FSDP.

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And what was the verdict?

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Which one was better?

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Well, it wasn't a clear winner.

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It really depends on the size of the model again.

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I'm sensing a theme here.

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It seems like a lot of these optimization choices are context dependent.

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So tell me more about what they found with these communication schemes.

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For smaller models, DDP was more efficient because it doesn't require as much communication.

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However, for that largest two billion parameter model, FSDP worked better, especially when training with larger batches of data.

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So finding the right tool for the job.

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Anything else in the research that stood out to you?

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You know, there was one finding that kind of challenged a common assumption in the field.

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Ooh, I love when research debunks conventional wisdom.

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Tell me more.

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Well, there's this idea that you should always try to max out your GPU memory utilization to train models as efficiently as possible.

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You know, use every bit of memory you have.

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But the researchers actually found that this isn't always the case, especially with SLMs.

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Really?

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Why would using less memory be better?

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That doesn't seem very intuitive.

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It comes down to the balance between doing calculations and communicating.

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If you cram as much data as you can onto a GPU, you might speed up the calculations,

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but you can also create a bottleneck when it comes to the GPUs talking to each other.

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They end up spending more time communicating than actually crunching numbers.

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So it's like giving the GPUs a little breathing room, even if it means not using all of the memory.

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Yeah.

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And that's just one of the interesting takeaways from this paper that we'll be looking at further.

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It's really clear that training these SLMs effectively is a complex process.

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Welcome back. We're continuing our deep dive into these smaller, large language models.

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Yeah, picking up where we left off.

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Still trying to figure out the best ways to train these compact, but powerful SLMs.

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And remember, we're looking at this research paper that digs into all the challenges researchers run into

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when they're training these models.

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It has been pretty eye-opening so far.

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Flash attention, different GPUs, balancing memory and communication speed.

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It's a lot to take in.

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But let's dive into some of the specific findings.

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What did the researchers actually discover, especially in terms of those practical metrics you mentioned earlier?

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Well, one thing that stood out was just how much flash attention improved the tokens per second metric,

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especially for the smaller models.

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That makes sense, right?

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Given how flash attention kind of streamlines that whole attention mechanism

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and gets rid of those data movement bottlenecks.

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Exactly.

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And it was even more noticeable when they were working with smaller models and smaller batches of data.

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Why is that?

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Are smaller models just more sensitive to those bottlenecks or something?

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I think it's more about the relative cost.

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In those cases, the time it takes to move data around can actually be a bigger problem

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than the time it takes to actually do the calculations.

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Flash attention helps reduce that bottleneck, so training is faster.

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So it's all about optimizing that flow of information within the model.

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And of course, as we said, that's your training usually means saving money.

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What about those GPU findings?

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Did the researchers have any advice on which ones to use?

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They did, and you might be surprised.

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While the really powerful GPUs like the H100 are considered the gold standard,

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for a lot of these SLM situations, they found that the A140GB,

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which is less expensive, could do the job just as well.

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So you don't always have to go for the most expensive hardware.

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That's great news for smaller research teams or companies

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that might not have a ton of money to throw around.

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Totally.

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It makes things a lot more accessible.

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Yeah.

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Let's talk about how those multiple GPUs talk to each other during training.

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I know they were looking into that.

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What did they find out?

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They focused on two main methods, distributed data parallel,

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DDP, and fully sharded data parallel, or FSDP.

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And again, the best choice really depended on how big the model was.

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OK, so when is one better than the other?

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For the smaller models, DDP was more efficient because it doesn't involve

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as much back and forth communication.

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But for that biggest model they tested, the 2 billion parameter one,

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FSDP was the way to go, especially with those larger batches of data.

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It's like choosing the right communication style for the situation.

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Anything else jump out at you from the research?

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Oh, yeah.

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They also looked at this idea that you always have to max out the GPU memory

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to get the most efficient training.

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But they found that might not always be the case,

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at least for these smaller models.

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We talked about that before.

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It seemed weird that using less memory could actually be better.

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Can you remind me why that is again?

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It's kind of like this.

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When you put a ton of data onto one GPU,

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yeah, it might speed up the calculations,

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but then you can run into problems with the GPUs communicating with each other.

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They spend more time passing data back and forth instead of processing it.

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So sometimes it's better to give them a little space to breathe,

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even if it means they're not using every last bit of memory?

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Exactly.

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And that's just one of the many interesting things we're going to unpack further.

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It's clear that training these SLMs effectively,

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well, it's not simple task.

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No, it's not.

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So, zooming out a bit,

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what do you think all of this means for the future of AI,

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especially in areas where resources might be limited?

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That's a great question.

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I think this research could make AI development a lot more accessible.

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I mean, if you don't always need the most expensive hardware and the biggest models,

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it opens up opportunities for smaller teams, startups,

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people who might not have had those resources before.

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It levels the playing field.

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I love that.

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Exactly.

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And that could lead to so much more creativity and innovation in the field.

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I'm also thinking about what this means for AI on our phones.

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As these SLMs became easier to train and more efficient,

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we could see a whole wave of new AI-powered apps and features, right?

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Absolutely.

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Imagine language models running right on your phone,

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personalized features without needing the cloud,

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better translations, smarter voice assistants,

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maybe even AI-powered tools for creating things.

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The possibilities are huge.

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That's a future I'm definitely excited about.

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But like with any new technology,

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we need to think about the potential downsides, too.

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What are some of the challenges we need to be aware of

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as these models become more common?

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I think one concern is making sure these models are used responsibly and ethically.

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The more accessible they become,

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the more important it is to think about how they could be misused.

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That's a really important point.

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We can't get so caught up in the possibilities

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that we forget about the ethical side of things.

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Exactly.

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And that's what makes research like this so important.

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By understanding how these models work and what makes them tick,

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we can start to think critically about their impact, both good and bad.

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This has been a great conversation so far.

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We've covered a lot, but it feels like we've just scratched the surface.

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There's always more to learn.

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That's what makes AI so interesting.

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It's always changing and pushing us to think in new ways.

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Speaking of pushing boundaries,

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let's go back to that question about the implications of this research for inference.

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How does all this affect the way these models are actually used

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in the real world on devices like phones?

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That's a key point.

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The study focused on training efficiency,

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but the findings definitely matter for inference, too.

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For example, if flash attention can speed up training,

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it makes sense that it could also lead to faster, more efficient inference,

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especially on devices with limited resources.

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So those same optimizations that make training better

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could also translate into a smoother user experience in the real world.

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Exactly.

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It opens up a lot of exciting possibilities,

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especially for that mobile AI future we were talking about.

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The researchers were looking at training efficiency,

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but how do these smaller models actually perform compared to the big ones?

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Can they do the same things?

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Are they as accurate?

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That's the big question, isn't it?

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This particular study didn't really go into that in depth,

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but there's more and more research coming out

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that suggests these SLMs can actually do really well.

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In some cases, they even outperform the larger models.

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Really? Any specific examples of that?

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Well, they mentioned one in the paper.

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Gemma2b, that two billion parameter model,

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actually beat OPT175B, which is a much larger model

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with 175 billion parameters.

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So it's not always about just making the model bigger.

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It's about using the right techniques and optimizing the heck out of it.

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Another underdog story in the AI world.

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It seems like we keep seeing that.

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But I wonder if there's a trade-off between efficiency and performance.

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At what point does making things more efficient

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start to hurt the accuracy or the capabilities of the model?

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That's a good question.

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It's something researchers are still working on.

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There's a balance to strike.

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We want efficiency, especially for things like phones,

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but we don't want to sacrifice too much performance.

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Like finding that sweep spot between speed and accuracy.

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Exactly. And that sweet spot might be different

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depending on what you want the model to do.

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It's definitely a complex area,

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but it's amazing to see all the progress.

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I have a feeling these SLMs are going to be a big deal moving forward.

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I agree.

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They have the potential to change everything about how we use AI.

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And as we move towards that future,

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we need to remember to be careful and thoughtful.

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We want to make sure these technologies are being used ethically

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and for the benefit of everyone.

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Absolutely.

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That's why conversations like this are so important.

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We need to explore these new developments

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and really think about their impact.

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And with that, we're going to take a quick break

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and come back to wrap up our deep dive into the world

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of small scale, large language models.

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And we're back.

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Back for the final part of our deep dive

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into the world of small scale, large language models or SLMs.

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Yeah, it's been a fascinating journey.

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We've gone from flash attention to GPU selection

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and uncovered some really interesting insights from this research.

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We've talked about how important these smaller models are becoming,

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especially with that focus on efficiency

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and making AI more accessible.

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Especially as AI starts making its way onto mobile devices,

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it's incredible to see how researchers are making

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these powerful models more compact and less resource intensive.

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It really shows how creative people are in this field.

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And this research is a great example of that.

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It gives us a glimpse into how they're optimizing

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the whole SLM training process, highlighting those factors

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that can really make or break a model.

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Before we wrap up, I want to circle back to something

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you mentioned earlier about the impact of all of this on inference.

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Actually using a trained model in the real world.

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Right.

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It's one thing to train a model efficiently,

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but how does that translate to real world performance,

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especially on devices like phones?

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That's a big question.

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Exactly.

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The study we've been discussing was all about training efficiency.

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But what about inference efficiency?

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Do the choices you make during training

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affect how well these models actually perform

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on devices with limited resources?

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That's definitely something that needs more research.

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We know that things like flash attention can speed up training,

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but could they also lead to faster and more efficient inference?

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It seems likely, but it's something that needs to be explored.

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It would be incredible to have an SLM on your phone

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that could translate languages instantly,

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or give you personalized recommendations

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without draining your battery.

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That's the dream.

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And this research suggests that it might not be that far off.

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It's exciting to think about the possibilities,

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but as we've been discussing, we also need to be careful.

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Of course, any powerful technology

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comes with potential risks and challenges.

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One thing we talked about was bias in these models.

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If an SLM is trained on bias data,

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it's going to reflect those biases in its output,

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maybe even amplify them.

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Absolutely.

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And that could have serious consequences,

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especially in areas like health care or criminal justice.

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We need to be very aware of these risks

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and make sure we're developing ways to reduce bias,

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both in the data and the models themselves.

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It's a responsibility we all share, researchers, developers,

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and users, to make sure that these technologies

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are used ethically and fairly.

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Well said.

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So as we wrap up this deep dive,

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what are your final thoughts on the potential of SLMs

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and the future of AI?

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You know, I'm really optimistic about the future of SLMs.

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I think they have the potential to change

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how we interact with technology in a fundamental way,

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bringing AI into our daily lives in ways

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we haven't even imagined yet.

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I completely agree.

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It's been fantastic exploring this research with you,

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and I hope our listeners have found it insightful as well.

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If you're interested in learning more about SLMs,

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I encourage you to check out the research we've been discussing

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and see where it takes you.

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And remember, the world of AI is constantly evolving,

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so keep learning, keep exploring, and keep asking questions.

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Thanks for joining us on this deep dive

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into the world of small-scale, large-language models.

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Until next time, stay curious.