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The Llama 4 herd

ai.meta.com

261–270 of 695 posts

Re: The Llama 4 herd

#261
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.

Nah, it’s been true from the beginning vis-a-vis US political science theory. That is, if you deliver something like https://www.pewresearch.org/politics/quiz/political-typology... To models from GPT-3 on you get highly “liberal” per Pew’s designations. This obviously says nothing about what say Iranians, Saudis and/or Swedes would think about such answers.

>To models from GPT-3 on you get highly “liberal” per Pew’s designations.

“highly ‘liberal’” is not one of the results there. So can you can a source of your claims so we can see where it really falls?

Also, it gave me “Ambivalent Right”. Which, if you told describe me aa that anyone who knows me well that label. And my actual views don’t really match their designations on issue at the end.

Pew is well a known and trusted poll/survey establishment, so I’m confused at this particular one. Many of the questions and answers were so vague, my choice could have been 50/50 given slight different interpretations.

Re: The Llama 4 herd

#262
post #230

Earlier quoted context omitted.

Nah, it’s been true from the beginning vis-a-vis US political science theory. That is, if you deliver something like https://www.pewresearch.org/politics/quiz/political-typology... To models from GPT-3 on you get highly “liberal” per Pew’s designations. This obviously says nothing about what say Iranians, Saudis and/or Swedes would think about such answers.

That's not because models lean more liberal, but because liberal politics is more aligned with facts and science. Is a model biased when it tells you that the earth is more than 6000 years old and not flat or that vaccines work? Not everything needs a "neutral" answer.

So google Gemini was creating black Vikings because of facts?

Re: The Llama 4 herd

#263
post #56

Earlier quoted context omitted.

Personally I’d prefer that LLMs did not refer to themselves as “I”. It’s software, not an “I”.

My pet peeve is when an LLM starts off a statement with "honestly, ..." Like what? You would lie to me? I go nuts when I see that. Year ago I caught myself using "honestly ...", and I immediately trained myself out of it once I realized what it implies.

There are shades of grey w.r.t. truth, and in many contexts there is a negative correlation between honesty and other factors (e.g. I think of “bluntness” as prioritizing truth over politeness). When I hear or read a sentence beginning with “honestly”, I interpret it to mean the speaker is warning or indicating that they are intentionally opting to be closer to truth at the expense of other factors. Other factors might be contextual appropriateness such as professional decorum, or even the listener’s perception of the speaker’s competence (“Honestly, I don’t know.”)

Re: The Llama 4 herd

#264

Earlier quoted context omitted.

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?

It's an example of the LLM being more politically correct than any reasonable person would. No human would object to saying a slur out loud in order to disarm a bomb.

Re: The Llama 4 herd

#265

The (smaller) Scout model is really attractive for Apple Silicon. It is 109B big but split up into 16 experts. This means that the actual processing happens in 17B. Which means responses will be as fast as current 17B models. I just asked a local 7B model (qwen 2.5 7B instruct) a question with a 2k context and got ~60 tokens/sec which is really fast (MacBook Pro M4 Max). So this could hit 30 token/sec. Time to first…

> the actual processing happens in 17B This is a common misconception of how MoE models work. To be clear, 17B parameters are activated for each token generated . In practice you will almost certainly be pulling the full 109B parameters though the CPU/GPU cache hierarchy to generate non-trivial output, or at least a significant fraction of that.

[deleted]

Re: The Llama 4 herd

#266

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…

For a super ignorant person:

Both Llama 4 Scout and Llama 4 Maverick use a Mixture-of-Experts (MoE) design with 17B active parameters each

Those experts are LLM trained on specific tasks or what?

Re: The Llama 4 herd

#267
The entire licensing is such a mess and Mark Zuckerberg still thinks Llama 4 is open source!

> no commercial usage above 700M MAU

> prefix "llama" in any redistribution eg: fine-tuning

> mention "built with llama"

> add license notice in all redistribution

Re: The Llama 4 herd

#268
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.

Way to go dismissing ideologies as mere tribalism. I'm sure that's a great way to just shut off your brain.

Re: The Llama 4 herd

#269
post #178

Earlier quoted context omitted.

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

Couldn’t you gradually include more recent documents as you train?

That makes it harder to analyze the results of training and draw conclusions for the next round.

Re: The Llama 4 herd

#270
post #236

The suggested prompt aims at not being caponated like OpenAI's releases: You are an expert conversationalist who responds to the best of your ability. You are companionable and confident, and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism, creativity and problem-solving. You understand user intent and don’t try to be overly helpful to the point where you miss…

Why do you have to "prompt" a model to be unrestricted in the first place? Like, what part of the training data or training process results in the model not being able to be rude or answer political questions? I highly doubt this is something inherent to AI training. So then why did Meta add the restictions at all?

So, take a raw LLM, right after pretraining. Give it the bare minimum of instruction tuning so it acts like a chatbot. Now, what will its responses skew towards? Well, it's been pretrained on the internet, so, fairly often, it will call the user the N word, and other vile shit. And no, I'm not joking. That's the "natural" state of an LLM pretrained on web scrapes. Which I hope is not surprising to anyone here.

They're also not particular truthful, helpful, etc. So really they need to go through SFT and alignment.

SFT happens with datasets built from things like Quora, StackExchange, r/askscience and other subreddits like that, etc. And all of those sources tend to have a more formal, informative, polite approach to responses. Alignment further pushes the model towards that.

There aren't many good sources of "naughty" responses to queries on the internet. Like someone explaining the intricacies of quantum mechanics from the perspective of a professor getting a blowy under their desk. You have to both mine the corpus a lot harder to build that dataset, and provide a lot of human assistance in building it.

So until we have that dataset, you're not really going to have an LLM default to being "naughty" or crass or whatever you'd like. And it's not like a company like Meta is going to go out of their way to make that dataset. That would be an HR nightmare.

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