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World model on million-length video and language with RingAttention

largeworldmodel.github.io

21–30 of 62 posts

Re: World model on million-length video and language with RingAttention

#21
This looks really promising!

Other than this sentence:

> We curated a large dataset of videos and languages from public book and video datasets, consisting of videos of diverse activities and long-form books.

I didn’t see any other mention of datasets used, is this on intentional?

Re: World model on million-length video and language with RingAttention

#23
post #22

What does "Million-Length" mean?

It means Millions of tokens. Token in this context means either text token (as in tokenization https://en.wikipedia.org/wiki/Large_language_model#Probabili...) or video token where the paper describes them as "each frame in the video is tokenized with VQGAN into 256 tokens." (p. 6)

Re: World model on million-length video and language with RingAttention

#24
post #21

This looks really promising! Other than this sentence: > We curated a large dataset of videos and languages from public book and video datasets, consisting of videos of diverse activities and long-form books. I didn’t see any other mention of datasets used, is this on intentional?

the sentence itself should be alarming.

the claim is that a dataset was created. of words and of videos, and that it was created from public datasets of books and videos, those datasets containing books, and videos.

it takes too many words to say almost nothing.

nothing to see here.

if that isn’t the intent, then the authors need to do better.

Re: World model on million-length video and language with RingAttention

#26
post #9

Earlier quoted context omitted.

Look at how RingAttention is implemented: it's blockwise attention distributed among many GPUs, in other words bruteforce parallelization. For inference they use TPUs v4-128, not running this at home any time soon.

What is this month's best choice to run at home?

Depends on what you're looking for. Check reddit.com/r/localllama, but I'm guessing it's still Mixtral for most things, with Yi-34 fine tunes being useful in some cases since Mixtral fine tuning is still not there yet.

Re: World model on million-length video and language with RingAttention

#27
Amazing that you can just shove a ton of multimodal data into a big transformer and get a really good multimodal model. I wonder where things will top out. For many years a lot of people (including me) were saying "you can't just take existing architectures, scale them up, feed them a lot of data, and expect something mpressive", but here we are.

Re: World model on million-length video and language with RingAttention

#29
post #21

This looks really promising! Other than this sentence: > We curated a large dataset of videos and languages from public book and video datasets, consisting of videos of diverse activities and long-form books. I didn’t see any other mention of datasets used, is this on intentional?

They have at least some of the dataset uploaded:

https://huggingface.co/LargeWorldModel

And the model page specifically mention Books3

Re: World model on million-length video and language with RingAttention

#30
post #21

This looks really promising! Other than this sentence: > We curated a large dataset of videos and languages from public book and video datasets, consisting of videos of diverse activities and long-form books. I didn’t see any other mention of datasets used, is this on intentional?

the sentence itself should be alarming. the claim is that a dataset was created. of words and of videos, and that it was created from public datasets of books and videos, those datasets containing books, and videos. it takes too many words to say almost nothing. nothing to see here. if that isn’t the intent, then the authors need to do better.

The information is in the model card though:

Books3 dataset 700B text-image pairs from Laion-2B-en, filtered to only keep images with at least 256 resolution 400M text-image pairs from COYO-700M, filtered to only keep images with at least 256 resolution 10M text-video pairs from WebVid10M 3M text-video pairs from a subset of InternVid10M 73K text-video chat pairs from Valley-Instruct-73K 100K text-video chat pairs from Video-ChatGPT

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