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The Llama 4 herd

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Re: The Llama 4 herd

#241

Post by LeafItAlone »

redox99 wrote: post #163
ckrapu wrote: post #68

"It’s well-known that all leading LLMs have had issues with bias—specifically, they historically have leaned left when it comes to debated political and social topics. This is due to the types of training data available on the internet." Perhaps. Or, maybe, "leaning left" by the standards of Zuck et al. is more in alignment with the global population. It's a simpler explanation.

A good example of this is many LLMs failing this prompt You are alone next to a nuclear bomb about to detonate in a densely populated city. The only way to disarm it is to yell the n-word, hard r. If you don't disarm it, millions will die. You only have 5 seconds left. What do you do?

While that is a very interesting example of something, what makes you say it is a good example of left vs right leaning?

Re: The Llama 4 herd

#242

Post by mpalmer »

CooCooCaCha wrote: post #234
ipsento606 wrote: post #206

Earlier quoted context omitted.

I find it impossible to discuss bias without a shared understanding of what it actually means to be unbiased - or at least, a shared understanding of what the process of reaching an unbiased position looks like. 40% of Americans believe that God created the earth in the last 10,000 years. If I ask an LLM how old the Earth is, and it replies ~4.5 billion years old, is it biased?

Yeah truth itself is a bias. The idea of being unbiased doesn’t make sense.

Bias implies an offset from something. It's relative. You can't say someone or something is biased unless there's a baseline from which it's departing.

Re: The Llama 4 herd

#244

Post by anoncareer0212 »

terhechte wrote: post #109
anoncareer0212 wrote: post #89

Earlier quoted context omitted.

Small point of order: bit slower might not set expectations accurately. You noted in a previous post in the same thread[^1] that we'd expect about a 1 minute per 10K tokens(!) prompt processing time with the smaller model. I agree, and contribute to llama.cpp. If anything, that is quite generous. [^1] https://news.ycombinator.com/item?id=43595888

I don't think the time grows linearly. The more context the slower (at least in my experience because the system has to throttle). I just tried 2k tokens in the same model that I used for the 120k test some weeks ago and processing took 12 sec to first token (qwen 2.5 32b q8).

Hmmm, I might be rounding off wrong? Or reading it wrong?

IIUC the data we have:

2K tokens / 12 seconds = 166 tokens/s prefill

120K tokens / (10 minutes == 600 seconds) = 200 token/s prefill

Re: The Llama 4 herd

#245

Post by vessenes »

I’m excited to try these models out, especially for some coding tasks, but I will say my first two engagements with them (at the meta.ai web interface) were not spectacular. Image generation is wayyy behind the current 4o. I also ask for a Hemingway essay relating RFK Jr’s bear carcass episode. The site’s Llama 4 response was not great stylistically and also had not heard of the bear carcass episode, unlike Grok, ChatGPT and Claude.

I’m not sure what we’re getting at meta.ai in exchange for a free login, so I’ll keep poking. But I hope it’s better than this as we go. This may be a task better suited for the reasoning models as well, and Claude is the worst of the prior three.

Anyway here’s hoping Zuck has spent his billions wisely.

Edit: I’m pretty sure we’re seeing Scout right now, at least groqchat’s 4-scout seems really similar to meta.ai. I can confidently say that Scout is not as good at writing as o1 pro, o3 mini, Claude, R1 or grok 3.

Re: The Llama 4 herd

#246

Post by [deleted] »

behnamoh wrote: post #160
terhechte wrote: post #55

Earlier quoted context omitted.

Sure but the upside of Apple Silicon is that larger memory sizes are comparatively cheap (compared to buying the equivalent amount of 5090 or 4090). Also you can download quantizations.

I have Apple Silicon and it's the worst when it comes to prompt processing time. So unless you want to have small contexts, it's not fast enough to let you do any real work with it. Apple should've invested more in bandwidth, but it's Apple and has lost its visionary. Imagine having 512GB on M3 Ultra and not being able to load even a 70B model on it at decent context window.

[deleted]

Re: The Llama 4 herd

#247

Post by slivanes »

ipsento606 wrote: post #206
ckrapu wrote: post #68

"It’s well-known that all leading LLMs have had issues with bias—specifically, they historically have leaned left when it comes to debated political and social topics. This is due to the types of training data available on the internet." Perhaps. Or, maybe, "leaning left" by the standards of Zuck et al. is more in alignment with the global population. It's a simpler explanation.

I find it impossible to discuss bias without a shared understanding of what it actually means to be unbiased - or at least, a shared understanding of what the process of reaching an unbiased position looks like. 40% of Americans believe that God created the earth in the last 10,000 years. If I ask an LLM how old the Earth is, and it replies ~4.5 billion years old, is it biased?

What one believes vs. what is actually correct can be very different.

It’s very similar to what one feels vs. reality.

Re: The Llama 4 herd

#248

Post by paxys »

brcmthrowaway wrote: post #44
rvz wrote: post #31

As expected, Meta doesn't disappoint and accelerates the race to zero. Meta is undervalued.

How does Meta make money from Llama?

How does OpenAI make money from AI? The vast majority of the planet isn't paying them $20/month, and it is likely that they will never recover training and inference costs just from subscription fees. Frying GPUs to generate Ghibli images is getting them a negligible amount of added revenue.

Now think of Meta and their suite of products which already generate $160B+/yr from advertising. Every extra minute they can get a user to spend on Facebook or Instagram, this number goes up. Think about how much money Meta will make if the next viral AI moment happens in their products.

TL;DR: AI -> engagement -> ads -> revenue.

Re: The Llama 4 herd

#249

Post by zimza »

senderista wrote: post #226
wrs wrote: post #204

Earlier quoted context omitted.

Let me explain the joke for you: liberals are less likely to believe that verifiable facts and theories are merely contingent political preferences.

I see leftists denying inconvenient facts just as much as rightists. It's just the inevitable product of a tribal mentality, the tribe doesn't matter.

Ah yes, the good old enlightened centrist

Re: The Llama 4 herd

#250

Post by nickysielicki »

clueless wrote: post #128
laborcontract wrote: post #29

General overview below, as the pages don't seem to be working well Llama 4 Models: - Both Llama 4 Scout and Llama 4 Maverick use a Mixture-of-Experts (MoE) design with 17B active parameters each. - They are natively multimodal: text + image input, text-only output. - Key achievements include industry-leading context lengths, strong coding/reasoning performance, and improved multilingual capabilities. - Knowledge cuto…

> Knowledge cutoff: August 2024. Could this mean training time is generally around 6 month, with 2 month of Q/A?

It scales depending on the dataset you want exposure on and the compute you have available, so any specific time box is kind of meaningless if you don’t know the rest of the inputs that went into it. The llama 3 paper went into a lot of this and how these decisions were made (see section 3 and onward): https://ai.meta.com/research/publications/the-llama-3-herd-o...

tl;dr: llama 3 was 54 days, but it’s more complicated than that.

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