51–60 of 125 posts
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If you’re doing research JAX makes some sense. Probably some Google bias in there too.
To be honest, most researchers in applied ML in the bay say the opposite. If you are trying to be nimble and prototype, use pytorch. If you're trying to gain some optimizations as you near deployment, rewrite in Jax.
It's worth taking a second to note that the author just assumes that readers understand "the wilderness" to mean "not Google". This post gives a lot of credit to Google's infra and hardware teams, and I'd love to read a perspective from one of those insiders who then went on to do related work elsewhere.
In my humble opinion, we never had failures of GPU even for large scale training. Our current training batch job is a 20GB json file which takes 6 hours just to load and has been running for more than 15 days with not a hiccup. And we are using the older Tesla T4.
GPUs have memory constraint issues but if you can plan and work around it, I havent seen it crash in real life.
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It has many meanings. Terrorism, weaponry and destruction are one meaning. Here are two others: > The center of rapid or intense development or change. noun > The starting point or most basic level. The author used it correctly even though you got triggered by the word. https://www.wordnik.com/words/ground%20zero
And I think that the meaning "most basic level" is wrong- the dictionaries might be simply recording a rare and wrong usage of the expression.
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Sure, that's also a factor but I'd say it reinforces my main point.
Good point, so the only real differentiator would be the size & quality of the data being fed and the fine tuning done on the model? I wonder what else differentiates LLMs from each other
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(update: i submitted this yesterday and it didnt get traction, i guess @dang must’ve merged the old submission in here. you really didnt have to, but its a nice gesture. thanks dang!!)
Great too see you on here. Love Latent Space podcast.
no events planned near term but come to the big shindig in june https://ti.to/software-3/ai-engineer-worlds-fair . last year's summit was the first time i really understood how much of a reach we have and how many good AI people we've managed to gather as friends.
Earlier quoted context omitted.
To be honest, most researchers in applied ML in the bay say the opposite. If you are trying to be nimble and prototype, use pytorch. If you're trying to gain some optimizations as you near deployment, rewrite in Jax.
Where does Tensorflow stand in this?
So essentially a startup in this context has a small number of people and a large amount of money for training clusters. The article describes many operation leasing servers - that you assume to go many startups (or existing firms). So it seems like you have the various LLM creators all doing roughly the same sort of thing (training with text and image data) with similar hardware and similar data. Each of these natur…
Earlier quoted context omitted.
If you’re doing research JAX makes some sense. Probably some Google bias in there too.
To be honest, most researchers in applied ML in the bay say the opposite. If you are trying to be nimble and prototype, use pytorch. If you're trying to gain some optimizations as you near deployment, rewrite in Jax.
So essentially a startup in this context has a small number of people and a large amount of money for training clusters. The article describes many operation leasing servers - that you assume to go many startups (or existing firms). So it seems like you have the various LLM creators all doing roughly the same sort of thing (training with text and image data) with similar hardware and similar data. Each of these natur…
Keep in mind that this is also chaff to distract people from the real secret sauce. I imagine that just as many startups are hiring writers and photographers to create extremely well labelled uncontaminated data for training.
One only need to look at the perverts over at civitai to see how far you can go with intensive labeling on a tiny compute budget.
Earlier quoted context omitted.
Sure, that's also a factor but I'd say it reinforces my main point.
Good point, so the only real differentiator would be the size & quality of the data being fed and the fine tuning done on the model? I wonder what else differentiates LLMs from each other
Golliath 120b is still the best open source model and no one knows why since it's just two llama2 60b glued together.