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PaLM 2 Technical Report [pdf]

ai.google

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Re: PaLM 2 Technical Report [pdf]

#111

Post by int_19h »

sgt101 wrote: post #95
int_19h wrote: post #94

Earlier quoted context omitted.

In their official blog post today, Google says this: "PaLM 2’s improved multilingual capabilities are allowing us to expand Bard to new languages, starting today. Plus, it’s powering our recently announced coding update." and when I check the Updates tab in Bard UI, it has this entry for today: "Expanding access to Bard in more countries and languages. You can now collaborate with Bard in Japanese and Korean, in addi…

it claims to run on LaMDA at the moment

If you mean asking it what it's running on, it just hallucinates. As others have noted in the comments here, you can get it to say that it runs on PaLM 3 quite easily.

Re: PaLM 2 Technical Report [pdf]

#113

Post by akiselev »

dontupvoteme wrote: post #80
thewataccount wrote: post #20

Earlier quoted context omitted.

Yeah 1 to 2 trillion is the estimates I've heard. Given the 25 messages / 3 hour limit in chatGPT, I don't think they've found a way to make it cheap to run.

1. there's no reason to think OpenAI wouldn't also be going the artificial scarcity route as have so many other companies in the past 2. Microsoft may not like them using too much azure compute and tell them to step off. Rumor has it they're trying to migrate github to it and it's seemingly not going ideal. And they're certainly nothing more than another microsoft purchase at this point.

OpenAI has a 40k token per minute rate limit on their GPT4 API too so I doubt it's artificial scarcity.

Re: PaLM 2 Technical Report [pdf]

#114

Post by kristianp »

sanxiyn wrote: post #109
jumpCastle wrote: post #105

Earlier quoted context omitted.

The name 3.5-turbo sounds to me like it implies distillation. The release notes at the time also hinted at it IIRC.

Well, that's why I said public. Personally, I don't think release notes https://help.openai.com/en/articles/6825453-chatgpt-release-... hinted at any such thing, and I think quantization is more likely than distillation.

Does the turbo API being 10 times cheaper than davinci imply anything? It implies more than just quantisation to me.

Re: PaLM 2 Technical Report [pdf]

#115

Post by ChatGTP »

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What if it’s legal elsewhere ? Too bad?

Re: PaLM 2 Technical Report [pdf]

#116

Post by int_19h »

DashAnimal wrote: post #97
int_19h wrote: post #94

Earlier quoted context omitted.

In their official blog post today, Google says this: "PaLM 2’s improved multilingual capabilities are allowing us to expand Bard to new languages, starting today. Plus, it’s powering our recently announced coding update." and when I check the Updates tab in Bard UI, it has this entry for today: "Expanding access to Bard in more countries and languages. You can now collaborate with Bard in Japanese and Korean, in addi…

In their presentation, they talked about multiple sizes for the PaLM 2 model, named Gecko, Otter, Bison and Unicorn, with Gecko being small enough to run offline on mobile devices. I can't seem to find any info on what size model is being used with Bard at the moment.

Indeed, it's likely that they're running a fairly small model. But this is in and of itself a strange choice, given how ChatGPT became the gateway drug for OpenAI. Why would Google set Bard up for failure like that? Surely they can afford to run a more competent model as a promo, if OpenAI can?

Re: PaLM 2 Technical Report [pdf]

#117

Post by KeplerBoy »

dontupvoteme wrote: post #76
sebzim4500 wrote: post #47

Earlier quoted context omitted.

Given Reddit's inability to keep their website functioning (unless you use the far superior old.reddit.com) I find it hard to believe they would be able to stop a motivated developer from scraping the whole site.

this is about the time that i expect sites to begin returning intentionally corrupt/incorrect/perhaps outright garbage (subtle or not, probably better subtle so they don't realize it until it's far too late) data in order to intentionally poison enemy wellscraping. where "ethics" dissolve into the inherent raw cannibalistic laws of capitalist ventures. then you can sell them back the TBs they scraped at a 1000x marku…

You might be right. What a dystopian future that will be. Make a few requests too many and the webserver might think you're scraping data so it gaslights you into reading bullshit.

Re: PaLM 2 Technical Report [pdf]

#119

Post by nr2x »

jxy wrote: post #23

So how do we actually try out the PaLM 2? The links in their press release just link to their other press release, and if I google "PaLM API" it just gives me more press release, but I just couldn't find the actual document for their PaLM API. How do I actually google the "PaLM API" for a way to test "PaLM 2"?

They’ve shut down and/or changed prices on APIs so many times as long as it isn’t 100x lower performance than an alternative I can’t see myself investing building a stack that relies on it.

Re: PaLM 2 Technical Report [pdf]

#120

Post by haldujai »

espadrine wrote: post #6

Surprisingly, their scaling law analysis still focuses on training FLOPs instead of training + inference FLOPs. That said, they do mention this: > The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. [A] smaller but higher quality model significantly improves inference efficiency, reduces serving cost, and enables the model’s downstream…

Depends on your goal, if it's to overtake OpenAI as having the best model overall it makes sense to optimize for training loss alone (assuming a fixed upfront compute budget).

Optimizing for inference to achieve the same loss would require more compute overall so you're either paying upfront with higher training costs or kicking the can down the road to inference.

News articles estimates of GPT4 cost seem to peg it at ~8 months of inference to achieve 1:1 cost with training. Life span of these models is TBD but it's a pretty safe bet we'll have new ones by then. Of course GPT3.5 is still getting used but probably won't cross 2:1ish in its lifetime.

Might as well roll the dice and kick the can down the road if you're Google, I imagine they would happily pay an extra 500k/day in inference compute to be market leaders, whats 183mill for them? But if they don't get any real market share or the model sucks they saved substantially on training.

> It makes me think they are Chinchilla-optimal,

They elaborate in the appendix but they empirically determine PaLM-optimal, which concurs with Chinchilla-optimal (more or less).

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