533 karma · joined May 24, 2017
It's more akin to scientific research where everyone is using the same molecules, but depending on the process you put the molecules through, you get a different outcome.
So does Meta and it's the reason they aren't actually releasing this as a product. They just showed us where they're at and gave us an idea on where they want to go in the next few years.
Everything in that sentence is false except the training data part.
>So the "competitive" system means that everyone uses LLama and PyTorch.
This sentence shows you don't understand the LLM landscape and it's also false.
>Sounds really open
Correct. They partner with practically every vendor available for inference, which, isn't even needed if you run their models locally.
Meta has done a lot of wrong things over the years. How they are approaching LLMs is not one of them.
As a layer.
"Which data specifically? Gerstenhaber wouldn’t disclose, but he implied that Claude 3.5 Sonnet draws much of its strength from these training sets."[0]
[0]https://techcrunch.com/2024/06/20/anthropic-claims-its-lates...
They specifically stated it required iPhone 15 Pro or higher and anything with a m1 or higher.
One example is in finance, you have a lot of 45 page PDFs laying around and you're pretty sure one of them has the Reg, or info you need. You aren't sure which so you open them one by one and do a search for a word, then jump through a bunch of those results and decide it's not this PDF. You do that till you find the "one". There are a non trivial amount of Executive level jobs that pretty much do this for half of their work week.
RAG purports to let you search one time.
That's exactly what they do. Don't anthropomorphize a computer program.
There's a 0-60 in 3 seconds one. Specifically about deep learning, which is a subset of ML. Deep learning is what is used to build LLMs ala ChatGPT.