I noticed ChatGPT can show me some example code but it's far from working (eg. Variables not defined...).

My project: I try to track fishes in a pond filmed from above and calculate their speed to see health issues if their movementspeed changes. For training I have videos of different daytimes.

ChatGPT gave me code but never told me really how to train a model on the PC that will work good enough on a Raspberry Pi 3 or 4. Is there any "known to work" code or tutorial that I can use to start my project from? I did some stuff in Python on the Pi, but I'm far from a programmer. Help?

Did you use something like bitsandbytes for the 8bit inference?

How did you implement it?

https://github.com/TimDettmers/bitsandbytes

>GPT-NeoX should fit in 24GB VRAM with 8bit, for inference.

GPT-NeoX20B It will fit in 24GB vram but it will almost instantly go out of memory when context will get a bit bigger than starting page of sentences.

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GPT-NeoX should fit in 24GB VRAM with 8bit, for inference.

I managed to run GPT-J 6B on a 3060 w/ 12GB and it takes about 7.2GB of VRAM.

LOL, I don't know what to say. I personally don't have anything smart to say about this question currently, it's as if you ask me if there is external life. Sure, I would watch it on Netflix if I have time, but generally speaking, it's way out of my field of interest. When you say snake oil, do you mean AI ExPeRtS? Why would you care about it? I think it's good that ML becomes mainstream.

Dude the first image classification or recognition program used perceptrons, the first model of a neuron. In other words, image classification has been neural networks ever since the beginning

I definitely think diffusion based generative ai models are a great idea. And whole heartedly agree that training GANs can be very painful. Head over to the hugging face diffusers library and you should be able to find a few models that are able to do unconditional image generation. They also have cookie cutter scripts that you can just execute to start training your model from the get go. They also have detailed instructions for how you can set up your own training data.

Although I have been working with these models for a while and I think training diffusion models can be very computationally intensive. Do you have access to a GPU cluster? If not, I’d recommend a U-Net based approach which you could train on GPU/TPUs on Google colab.

I have been using these class of models for my masters thesis and I would be happy to help in case you have any questions. Good luck! :)

So if I understand you correctly, you want to learn ML specifically to solve this one problem?

https://github.com/GeorgLegato/Txt2Vectorgraphics

and since it is simple black and white drawings you could use my vectorise extension to produce thousands of svg. so no scaling issues for large canvas or use at any smaller resolutions on mobiles etc

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Please feel free to leave a comment if you're interested or if you know someone who's related to these fields. I would greatly appreciate your response and time. Thank you so much!

I am trying to find tasks that use discrete tokens as inputs to do classification. E.g. some NLP classification tasks operate on a set of tokens (words, characters, special token sets, etc), and malware classification can operate on raw bytes. Is there any other domain that uses discrete sequences of tokens as inputs?

You're not likely to get much help there, unfortunately. With SD, your best bet would probably be Dreambooth, which you can get with the Huggingface diffusers library. It might be overcomplicating matters, if the site is representative of your training data, though. GANs can be notoriously difficult to train but it's probably worth a shot here -- it's a pretty basic use case. You might look into data augmentation and try a u-net with a single-channel output.

A slightly more advanced option might be ProGAN. Here's a good video tutorial if that's your thing.

You could definitely do this with StableDiffusion embeddings.

Video has even less information density, since frames are similar to each other! Video codecs can get crazy compression rates like 99% on slow-moving video.

But you still have to process a lot of pixels, so text-to-video generators are held back by memory requirements.

About the intuition that it would produce responses further from the human ones (in fact, we see that for this variant, the BLEU is lower) - in a way, it could work as a regularization to produce more diverse responses and prevent some overfitting. That loss mostly affects the additional head's weights which are removed during inference, but we also multiply it by an optimal constant to be sure it doesn't affect the whole architecture too much. I've sent you a PM if you wish to receive some more details or empirical insights.

Will it be able to approach 1 somewhat effectively as well?

The two model approach is the original setup :). I'm just looking for potential alternatives.

That it's Bulgaria is probably why it's possible at all. Notice 'high school of mathematics'.

Some ex-Soviet/ex-Warsaw pact countries have functioning maths education.

Thanks for the interest! You can follow me on Twitter: https://twitter.com/radi_cho

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Thanks for the tip!

Maybe ask over at /r/stablediffusion and check out aesthetic gradients over there. Might be able to replicate your art style and scale it to the thousands of images you'll need to generate.

AISTATS tend to be more popular these days, probably due to the conference timing. If you don't want to submit to AAAI, AISTATS is the other option. Also, the review process is much less noisy due to the better focus, and you get 5 reviews in general. In terms of content, they have slightly different flavors. Traditionally, people doing Bayesian nonparametrics have favored UAI, and it still somewhat seems to be the case.