Free for now or free for an hour as the pricing tab indicates?

Is it psychotic to use a GPT based model for what could be treated as image segmentation?

For my task, I trained a GPT model to predict a mask for an Input Integer Matrix with certain rows being entirely a spurious value. Where the mask is replacing the spurious integers with X's. It is a text based model for what could be considered an image task.

I'm a super learning noob, but is there a reason you can't substitute large amounts of VRAM with large amounts of DRAM?

I know RAM bandwidth is important, but does it make that much of a difference if I got 256GB of quad channel DRAM and only 8GB VRAM? Compared to a more typical 32GB DRAM and 24GB VRAM?

So far I have tried BLOOM Petals (a distributed LLM), inference took me around 30s for a single prompt on a 8GB VRAM gpu, but not bad!

The tl;dr for all GPU questions is that CUDA is the answer. There are no other even 'kinda' contenders.

I'm not happy about the monopoly, but that's where we're at, and there is nothing on the horizon pointing otherwise, either.

Two opposite concepts

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Their benchmark was done on a 16GB T4 which is anything but beefy. The T4 maxes out at 80W power consumption, and was primarily marketed toward model inference. The T4 is the cheapest GPU offered by google cloud.

Thank you!

That is very interesting!

The paper is not yet on GitHub, but I'm assuming the hardware requirements are as mentioned one beefy consumer GPU (3090) and a whole bunch of DRAM (>210GB) ?

I've played with opt-175b and with a bit of twiddling it can actually generate some Python code :)

This is very exciting as it gets these models into the prosumer range hardware!

Is there a way to do it with single precision?

It might not be unreasonable to think maybe OP primarily wants the functionality of current LLMs, and if something can provide that more efficiently (or has promise to in the near future), s/he may want to know about it too.

Depends on what you’re trying to do but just use OpenAI APIs. Your effort/time is also expensive.

I do not, sorry.

Interesting stuff. The research is actually a couple of years old which is surprising, I expected it to be like months old and not years old. Are you involved with the project?

DECA looks cool and it looks viable for my project. I'll look into it a bit deeper. Thanks for the suggestion.

That's super cool. Do you know what kind of techniques they are using?

Anytwo would be cheaper. One person is standing still and the other person is moving around them making photos or a video with a phone. Turntables large enough to hold a person and actuators for moving the phone up and down sound expensive.

Try luma AI https://lumalabs.ai/

I have been doing my best to beat them back in peer-review, but I can only do so much...

NVAE: A Deep Hierarchical Variational Autoencoder

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The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)

I would assume that someone who is capable of programming a GAN and go through all the steps of Parameter Tuning at some Point should realize that the randomness shouldnt do anything.

The “line down the middle” is where the “predicted” shark is the true shark (the type of shark was guessed correctly. The color of the square represents the certainty of the guess. *How confident is the software that it made the “right” guess? In these cases, it’s 100% confident since it is the right one!)

Pretty cool if you ask me!

You can match them up to see for yourself.

I want to let users upload a 3D model of themselves onto an app and try out different clothing on their model. I can tell you more over message if you're interested in hearing more

You can use langchain with open source models like Flan-T5 or GPT-J as well. Just need to deploy them as an API endpoint and point to it.