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MiniGPT-4

minigpt-4.github.io

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Re: MiniGPT-4

#231

Post by eternauta3k »

thewarrior wrote: post #182
sho wrote: post #14

Earlier quoted context omitted.

> they're doing something really simple -- take BLIP2's ViT-L+Q-former, connect it to Vicuna-13B with a linear layer, and train just the tiny layer on some datasets of image-text pairs Oh yes. Simple! Jesus, this ML stuff makes a humble web dev like myself feel like a dog trying to read Tolstoy.

I've only been reading ML stuff for a few months and I kind of understand what it's saying. This stuff isn't as complex as its made out to be. It's just a bunch of black boxes AKA "pure functions". BLIP2's ViT-L+Q-former AKA //I give you a picture of a plate of lobster it will say "A plate of lobster". getTextFromImage(image) -> Text Vicuna-13B AKA //I give you a prompt and you return completion ChatGPT style getComp…

Interesting, so the LLM is "just" getting your question plus a normal text description of the image (as vectors)?

Re: MiniGPT-4

#232

Post by idiotsecant »

akiselev wrote: post #194
craigching wrote: post #173

Earlier quoted context omitted.

If you want to understand the theory, that's true. If you want to develop an intuitive understanding without having to understand all the nuts and bolts (and I understand that can be a big ask for how some people learn/understand), give this a try: https://karpathy.ai/zero-to-hero.html

The irony is Karpathy presents the limit/epsilon definition of derivatives in the first half hour (quite well IMO and he never actually says “epsilon”) which is very much a nuts and bolts kind of explanation in calculus. That said, when most people say differential equations they’re usually thinking of analytical solutions which is very much not necessary for practical ML.

I would say the limit epsilon derivative is exactly the sort of thing grandparent post is talking about. It's quite intuitive and doesn't require hardly any mathematical foundation at all, other than basic geometry and algebra. You can understand topics that build on that simple concept without understanding the more formal derivative definitions.

Re: MiniGPT-4

#234

Post by xiphias2 »

simonw wrote: post #122

Hate to be the person complaining about the name, but we already saw how this plays out with DALL-E mini: if you name your project directly after something else like this, no matter how much extra explanatory text you attach to it a large number of people will assume it's an "official" variant of the thing it was named after. Eventually you'll have to rename it, either to resolve the confusion or because OpenAI press…

,,So better to pick a less confusing name from the start.''

I'm not sure if that's better from a marketing standpoint though....it works, you still remember DALL-E mini

Re: MiniGPT-4

#235

Post by HarHarVeryFunny »

sandkoan wrote: post #217
HarHarVeryFunny wrote: post #192

Earlier quoted context omitted.

I got the 1T GPT-4 number from here - this is the video that goes with the Microsoft "Sparks of AGI" paper, by a Microsoft researcher that had early access to GPT-4 as part of their relationship with OpenAI. https://www.youtube.com/watch?v=qbIk7-JPB2c

Bubeck has clarified that the "1 trillion" number he was throwing around was just a hypothetical metaphorical—it was in no way shape or form implying that GPT-4 has 1 trillion parameters [0]. [0] https://twitter.com/SebastienBubeck/status/16441515797238251...

OK - thanks!

So we're back to guessing ...

A couple of years ago Altman claimed that GPT-4 wouldn't be much bigger than GPT-3 although it would use a lot more compute.

https://news.knowledia.com/US/en/articles/sam-altman-q-and-a...

OTOH, given the massive performance gains scaling from GPT-2 to GPT-3, it's hard to imagine them not wanting to increase the parameter count at least by a factor of 2, even if they were expecting most of the performance gain to come from elsewhere (context size, number of training tokens, data quality).

So in 0.5-1T range, perhaps ?

Re: MiniGPT-4

#236

Post by [deleted] »

lhl wrote: post #175
lxe wrote: post #154

Why Vicuna over something like oasst-pythia? Would love to see a table comparing all the new models side by side.

Fabrice Bellard has run a standard set of benchmarks w/ lm-eval on a big chunk of open models here: https://bellard.org/ts_server/ - Flan T5 XXL and GPT-NeoX 20B both outperform Pythia 12B on average (LLaMA 13B+ tops the charts).

[deleted]

Re: MiniGPT-4

#237

Post by mftb »

HansHamster wrote: post #216

Just tried it with a random schematic: > The image shows a block diagram of a power amplifier circuit with a power supply and a load resistor. The power supply is connected to the input of the amplifier, which is then connected to the output. The load resistor is connected to the output of the amplifier and the power supply. The amplifier is designed to amplify the input signal to a higher level, which is then sent t…

I also got middling results. I gave it a novel NASA picture of the moon passing in front of the earth, taken by the Deep Space Climate Observatory Satellite, so an unusual vantage point. First it was just wrong, and said that it was a picture of a crescent moon taken from a mountain top, or something like that. Not terrible at least it recognized the moon, but wrong. I tried to give it a chance and corrected it with the additional info that the picture was taken from space. It then did better, but on the last line it threw in that it was not a photograph, but a rendering of some sort, which again, is just wrong.

More and more I get the impression that people are so taken with the chat interface that they are grading these models more favorably than is warranted.

Re: MiniGPT-4

#238

Post by intelVISA »

SanderNL wrote: post #201
brycedriesenga wrote: post #147

Earlier quoted context omitted.

Hey, guys. Hey. Ready to talk plate processing and residue transport plate funneling? Why don't we start with joust jambs? Hey, why not? Plates and jousts. Can we couple them? Hell, yeah, we can. Want to know how? Get this. Proprietary to McMillan. Only us. Ready? We fit Donnely nut spacing grip grids and splay-flexed brace columns against beam-fastened derrick husk nuts and girdle plate Jerries, while plate flex tan…

Talk dirty to me!

runtime polymorphism

Re: MiniGPT-4

#239

Post by intelVISA »

SanderNL wrote: post #203
MattPalmer1086 wrote: post #193

Earlier quoted context omitted.

Would you say that this satisfies the spirit of the company policy? Or is it a bit of a hack to get around it? I ask because we are about to produce a similar policy at work. We can see the advantages of it, but likewise, we can't have company data held in their systems.

If I use it I also make sure it’s something completely non-core business, like an arcane piece of sorting or ugly rxjs construction. I get the IP angst, but some companies think their GetGenericObjectFromDB() REST bs is secret sauce.

To the average VC a computer switching on is secret sauce enough, the rest is really just an implementation detail.

Re: MiniGPT-4

#240

Post by HansHamster »

mftb wrote: post #237
HansHamster wrote: post #216

Just tried it with a random schematic: > The image shows a block diagram of a power amplifier circuit with a power supply and a load resistor. The power supply is connected to the input of the amplifier, which is then connected to the output. The load resistor is connected to the output of the amplifier and the power supply. The amplifier is designed to amplify the input signal to a higher level, which is then sent t…

I also got middling results. I gave it a novel NASA picture of the moon passing in front of the earth, taken by the Deep Space Climate Observatory Satellite, so an unusual vantage point. First it was just wrong, and said that it was a picture of a crescent moon taken from a mountain top, or something like that. Not terrible at least it recognized the moon, but wrong. I tried to give it a chance and corrected it with…

And after pointing out all the mistakes in the description, it took a very ... interesting approach to address my remarks:

> I apologize for the confusion. The image shows a block diagram of a basic amplifier circuit with a power supply, but it does not show any input stages, output stages, or load resistors. [...] The input stage is made up of an operational amplifier, which is used to amplify the input signal. The output of the input stage is then connected to the power supply, which provides the necessary voltage to operate the amplifier. The amplifier is designed to amplify the input signal to a higher level, which is then sent to the load resistor to power the load.

So it acknowledges the remarks, describes everything that is _not_ in the image and then puts the stuff back into the description anyway :)

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