MisterManuscript

MisterManuscript t1_jdhtan0 wrote

Reply to comment by Rishh3112 in Cuda out of memory error by Rishh3112

You probably have a memory leak somewhere in youe training loop. Either that or your model or batch size is way too big and occupies a lot of vRAM.

Addendum: There's a difference between RAM and vRAM (your GPU's RAM), I hope the 14GB you're talking about is vRAM and not the RAM of your AWS vm.

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MisterManuscript t1_jdawa9m wrote

Reply to [R] Introducing SIFT: A New Family of Sparse Iso-FLOP Transformations to Improve the Accuracy of Computer Vision and Language Models by CS-fan-101

Feels like the authors are trying to piggyback on the pre-existing fame of Scale-Invariant Feature Transform. Out of all other names that could have been chosen, why try to override an existing name?

Addendum: if you're lucky, Google just might cut you some slack. If not, then expect their lawyers to come at you with a cease-and-desist.

Addendeum 2: from a deleted reply from one of the authors/person from Cerebras asking why Google might come after them with a cease-and-desist: SIFT's patent is owned by Google. They may consider trademark violation, or something similar.

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MisterManuscript t1_jcxo0zv wrote

Reply to How noticeable is the difference training a model 4080 vs 4090 by Numerous_Talk7940

You don't need the 40 series, they're designed with providing ML solutions to games. You're paying extra just to have an in-built optical-flow accelerator that you're not gonna use for model training.

The optical flow accelerator is meant for computing dense optical flow fields as part of many inputs to the DLSS feature that most new games use.

You're better off with the 30 series or lesser.

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MisterManuscript t1_jcry6cj wrote

Reply to comment by MysteryInc152 in [R] ChatGLM-6B - an open source 6.2 billion parameter Eng/Chinese bilingual LLM trained on 1T tokens, supplemented by supervised fine-tuning, feedback bootstrap, and RLHF. Runs on consumer grade GPUs by MysteryInc152

That's not what bootstrapping is, it is a resampling technique used to create multiple datasets of the same size from the original dataset using random sampling with replacement. It is done to get the estimate of the standard deviation of a desired variable.

Here's the link to the ISLR textbook. The bootstrap chapter will verify what it is.

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MisterManuscript t1_jcrwvc8 wrote

Reply to comment by Temporary-Warning-34 in [R] ChatGLM-6B - an open source 6.2 billion parameter Eng/Chinese bilingual LLM trained on 1T tokens, supplemented by supervised fine-tuning, feedback bootstrap, and RLHF. Runs on consumer grade GPUs by MysteryInc152

I tried googling it, it's is a nonexistent terminology in the realm of statistics. I know what bootstrapping is, but not this version of it.

It's better to ask the GitHub authors about this to make sure they're not just spitting out pseudostatistical terminology.

Addendum: another guy did query the authors regarding this terminology in the issues tab, they did not respond.

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MisterManuscript t1_isosuxa wrote

Reply to comment by Overall-Importance54 in [D] What is the deal with breast cancer scans? by Overall-Importance54

That's a different use case. I work in healthcare (computer vision solutions) and I have yet to see a use-case for text-to-video generation.

So far most of the use-cases are either: classification or segmentation, mostly for diagnosing diseases. There's also biomedical informatics, but I'm not familiar enough with medical tabular data to comment on it.

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MisterManuscript t1_isobcjb wrote

Reply to [D] What is the deal with breast cancer scans? by Overall-Importance54

This isn't unique to your case. A lot of applications of machine learning in healthcare suffer from one drawback: a lot of patient data is sensitive and is very hard to get your hands on. You don't just query them to your liking; there is a lot of bureaucracy in place before you get your hands on even a small sample of required data.

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