Mind Readings: DeepSeek Week Part 2 – Understanding the Different DeepSeek Versions

Welcome to part two of Deep Seek Week, if you will, on uh on the channel here and on the blog. Uh in the first part we talked about why Deep Seek is such a big deal, why everyone's paying so much attention to it. Today we're going to talk about the different versions that are available to you and me and the kind of results that we get out of them. Um so there's going to be three different flavors of the Deep Sea Tech seek technology. There is V3, which is their chat model, it's which is the equivalent of ChatGPT's GPT 4.0.

You talk to it, you know how to use it, you've used Chat GPT forever. It's the same exact thing. There is R1, their reasoning model, which is the advanced thinking model. And then they have these distilled models that they've made of uh other high performing AI models that they tuned with their processes. These three sort of, I guess, categories are what's available to us, and we can run them in a bunch of different ways.

This is something that makes DeepSeq very different than Chat GPT or Google's Gemini or Anthropic Cloud. With those companies, you have to use their services. You cannot run Google Gemini uh on your own computer. You cannot download uh OpenAI's 01. With Deep Seek, you can use their services, um, which are provided through their company's website and stuff like that.

And we'll talk in the next part about whether that's a good idea or not. Um, but you can, if you have the hardware, you can download and run these things yourself. So let's briefly take a look at the way it performs, and we're gonna use their interface to start. Now, I do want to emphasize read the privacy policy, because it is very permissive for them, um, which means that your data is going to be uh going to be used. So just being aware of that, that that is that's what's going on here.

Um let's start with a um let's start with a a standard prompt. I'm gonna have deep seek the street the strip chat version. Um try to compose some song lyrics. I'm gonna compose some song lyrics. I've got a very long prompt here that says we're gonna write a uh a song about social media, X threads and Blue Sky, um and so on and so forth.

Uh actually I'm gonna make a quick modification to this. So we've got our long uh our our guidance for lyrics. Let's see how Deep Seek itself performs. Um so this is going to be the uh the V3 chat. So it's got uh, you know, it it's no different than GPT-4.0, uh, which means that your prompting structure.

Uh I'm using a fairly naive prompt because I just want to see if we can make a fun song about blue sky. Uh and it came up with uh you know decent pretty decent scrolling through the cast, it's a digital fight. Elon's got the Nazis and Zach's got the right threads, it's just an echo where the same thoughts repeat. Blue sky's got the vibe, it's a retro tweet treat. That'd be tough to sing.

But it did a pretty credible job there. Now let's take that exact same prompt, start a new chat. I'm gonna turn on reasoning mode. So this turns it switches it from V3 to R1, their reasoning model. Let's see what happens with this.

We give it that, and now it starts to do chain of thought aloud. So it's saying, hey, I think I need to start doing this, I need to be it needs to be this, I'll break down the key elements. I have got this is what this means, these are the jabs. And it comes out with just quickly eyeballing this, a a better result, right? Elon's X marks the spot where brain cells decay, a rocket man tweeting aisle on a mean display while Tony Clark cosplay runs.

Oh man. It it it did a decent job. I can scroll clearly now. The trash is rinsed out. That's that's okay.

That's pretty darn good. So this is R1. You can see that what R1 did was it took some time to basically sort of have a meeting with itself to come up with some options before spitting out its output. This is this is pretty darn good. Now, the third model that we'd want to use is we're going to use one of the meta llama models, but we're going to use the one that Deepseq tuned.

So this is one that runs on your computer if your computer has enough memory. So I'm running a MacBook M4 with 128 gigabytes of RAM. This model occupies about 50 gigabytes of memory for the model itself, plus it requires um like five or six gigabytes for the context window. So we can see already just on the on on what it's doing, it's got its own chain of thought. So it's saying, here's what I think I need to do, and now I'm gonna start doing the thing.

This is obviously you can see also considerably slower, but because this is locally hosted, uh, it is completely private. So if I put this to the side here, we can actually see the the llama, uh the deep seek version of llama churning away, trying to figure out uh what words, what probabilities to do. So it's going in through this chain of thought first, and then it will assemble the lyrics. So it's not much different procedurally from this version of Deep Seek where you could see the the chain of thought. This version, because it's a much larger model, uh, is capable of more advanced thought, more thorough thought.

But this is still not bad. So let's wait for this to finish up. So what you're seeing here from the the llama version that Deep Seek tuned is not bad. I would I would rate its performance roughly on par with maybe GPT 4.0 or maybe the previous version, GPT 4 V. Uh, it's not as good.

You can tell just from a a purely qualitative perspective, it's not quite as good as the more advanced model, but that's expected from a model that you can run on your laptop as opposed to a model that requires a data center. So these are the the versions of Deep Seek that are available to us. Again, if you have the hardware and it for running V3 or R1, you need a lot of hardware. Um, when Nvidia's project digits come out, the $3,000 computer that has 120 gig of shared memory, um, you're going to need three of them tied together, uh, which it can do to run Deep Seek uh R1 locally. However, that would give you state of the art high speed output for under 10 grand, um, which for a small to mid sized business is reasonable, it's not great, but it's reasonable for a large enterprise, it's terrific.

And for anyone who wanted state-of-the-art performance, I mean, that's a lot cheaper than buying in uh an H200 at $50,000. So those are the versions that are available to you. Now you'll note that the the web version doesn't require anything other than for you to create an account and log in. The downloadable versions do require extra effort to put together. But the trade-off is that a downloadable version that you're running is completely private.

You can do whatever you want with it, and there's no difference between that and any other app that's on your computer that doesn't call home. So I think that's a great place to stop because we want to transition into part three, uh, which is is deep seek safe, especially given some of the hype that's being discussed right now. So we'll we'll come back and talk about that in the next part. See you then. If you enjoyed this video, please hit the like button.

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