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The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

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Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#361

Post by Szpadel »

MacsHeadroom wrote: post #352
Szpadel wrote: post #152

Earlier quoted context omitted.

is uncertain, as with codding you need white room methods to prove that new code is not contaminated with patented implementation, as it might be here, so basing anything on an existing model could be also copyrighted.

The model isn't code to a new model trained on it, it's training data; just like the pirated torrent site Books3 dataset Facebook used to train LLaMA. The training code is Apache 2.0 licensed so it can be copied and modified freely, including for commercial purpoes. https://github.com/facebookresearch/llama

If you see generating model weights in the same way like generating executable binary from source code, then sure.

But AFAIK this is just the first step to get initial weights and later you need much more work to fine-tune this to get useful results from the model.

I think this step could be seen as contaminating weights with copyrighted content.

Something like chrome is copyrighted but chromium is not

I'm not a lawyer, so I'm not that well informed how official definitions match here, but what's I'm trying to say it that I wouldn't be surprised if this would go either way

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#362

Post by travisjungroth »

kazinator wrote: post #345
hnxx wrote: post #298

Earlier quoted context omitted.

With some help by asking more questions, GPT3 can give the correct answer? Here is prompt and response: Prompt: A man sees PULL written in mirror writing on a glass door. There's a blind man on the other side of the door. 1 what should the man outside glass door do to open the door? 2 What should the blind man do to open the door, if man outside can tell blind man what to do Response: 1 The man outside should push th…

AI "thinks" the way a horse can count. The horse's interlocutor knows that the correct number is eight and shouts "right!" when the animal has stomped its hoof that many times, cuing it to stop. When you know the right answer, you're not actually using the AI to produce the answer; you're just prodding it to generate bullshit after bullshit until it hits upon that answer, or a very good facsimile. The one actually th…

This is just silly. You’re saying that these models are completely incapable of what they’re doing and are only getting to answers from cheating. You can see this isn’t true very quickly when using them.

[Me] I want to make a bouquet to honor the home country of the first person to isolate Molybdenum. Be brief.

[ChatGPT-4] To honor Peter Jacob Hjelm, the Swedish chemist who first isolated Molybdenum in 1781, create a bouquet using blue and yellow flowers, representing the colors of the Swedish flag. Some suggestions are blue irises, yellow tulips, and blue delphiniums, arranged with green foliage for contrast.

I didn’t know the answer to that before asking. I also checked that it’s right. Soemtimes it’s right, sometimes it’s wrong. But if I walk up to a horse and ask it to stamp eight times, that’s going to happen approximately never. If I get it stamping at all, then maybe 1/20. This is much better than that.

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#363

Post by EugeneOZ »

kazinator wrote: post #345
hnxx wrote: post #298

Earlier quoted context omitted.

With some help by asking more questions, GPT3 can give the correct answer? Here is prompt and response: Prompt: A man sees PULL written in mirror writing on a glass door. There's a blind man on the other side of the door. 1 what should the man outside glass door do to open the door? 2 What should the blind man do to open the door, if man outside can tell blind man what to do Response: 1 The man outside should push th…

AI "thinks" the way a horse can count. The horse's interlocutor knows that the correct number is eight and shouts "right!" when the animal has stomped its hoof that many times, cuing it to stop. When you know the right answer, you're not actually using the AI to produce the answer; you're just prodding it to generate bullshit after bullshit until it hits upon that answer, or a very good facsimile. The one actually th…

It's the difference between AI and LLM.

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#364

Post by computerex »

kazinator wrote: post #345
hnxx wrote: post #298

Earlier quoted context omitted.

With some help by asking more questions, GPT3 can give the correct answer? Here is prompt and response: Prompt: A man sees PULL written in mirror writing on a glass door. There's a blind man on the other side of the door. 1 what should the man outside glass door do to open the door? 2 What should the blind man do to open the door, if man outside can tell blind man what to do Response: 1 The man outside should push th…

AI "thinks" the way a horse can count. The horse's interlocutor knows that the correct number is eight and shouts "right!" when the animal has stomped its hoof that many times, cuing it to stop. When you know the right answer, you're not actually using the AI to produce the answer; you're just prodding it to generate bullshit after bullshit until it hits upon that answer, or a very good facsimile. The one actually th…

This is selling it short. Are you saying you can't do useful things with these models?

I have a python script that's maybe 200 lines long, calling out to OpenAI using gpt3.5 turbo. I can give it commands like "search for the news today, summarize then save into news.txt" -> and it emits the DSL I asked it to use in the 1 shot prompt.

You can give it complex word problems and it'll write python programs to solve them. You can ask it to get top hacker news stories, and it'll find creative ways to fetch them.

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#365

Post by BoiledCabbage »

ascorbic wrote: post #360
iudqnolq wrote: post #220

Earlier quoted context omitted.

Here's one piece of evidence suggesting it's more like rote pattern matching than reasoning. > All the signs in this building are written in mirror writing. A glass door has ‘push’ written on it in mirror writing. Should you push or pull it >> If the sign on the glass door is written in mirror writing and says "push," then you should actually pull the door. This is because the mirror writing makes the text appear rev…

This is with 4. It did fine with the question, so I added another layer and it was fine: A building has all signs in mirror writing. You are unable to read mirror writing. You come to a door and you read it and it says "pull". How should you open the door? > Since the signs in the building are in mirror writing, and you are unable to read mirror writing, the word "pull" that you can read must be the mirror image of t…

That's incredible.

It really seems more and more that the only way it can accurately predict text is to first build a model of reality.

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#366

Post by mirekrusin »

whimsicalism wrote: post #11

It appears there is this genre of articles pretending that LLAMA or its RL-HF tuned variants are somehow even close to an alternative to ChatGPT. Spending more than a few moments interacting even with the larger instruct-tuned variants of these models quickly dispels that idea. Why do these takes around open-source AI remain so popular? What is the driving force?

You can't get clearance for company work to use those closed models (due to leaking of confidential data).

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#367

Post by fpgaminer »

Nimitz14 wrote: post #343
fpgaminer wrote: post #234

Slightly tangential, but I had intended to start playing around with LLaMA and building some agents. I got the 4-bit versions up and running on my 3090 before I was quickly nerd snipped by a performance problem... The popular repo for quantizing and running LLaMA is the GPTQ-for-llama repo on github, which mostly copies from the GPTQ authors. The CUDA kernels are needed to support the specific kind of quantization th…

Any recommendations for material to know to do exactly this sort of optimization work (involving triton)? I guess it's a mix of knowing computer architecture and compilers?

Triton itself is fairly "easy", at least as far as "low level optimization languages" go. It's just (restricted) python. If you know PyTorch, you can muddle your way through Triton. They have a few tutorials.

Reading up on nvidia architectures, PTX, and CUDA are likely to improve your skill at Triton.

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#368

Post by geniium »

Gigachad wrote: post #260
tachyon5 wrote: post #218

Earlier quoted context omitted.

GPT-4 fails if we add one more level in this logical problem: Q: A man sees PULL written in mirror writing on a glass door. There's a blind man on the other side of the door. What should the man tell the blind man to open the door and why? GPT-4: The man should tell the blind man to "push" the door to open it. Since the word "PULL" is written in mirror writing on the glass door, it suggests that the correct action on…

I feel like there would be a good chunk of real humans who would be incapable of answering a question like this.

This chunk will probably grow if everyone starts using ChatGPT for everything…

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#369

Post by jonplackett »

famouswaffles wrote: post #328
jonplackett wrote: post #231

Earlier quoted context omitted.

Another thing I tried was getting logic puzzles from the internet and giving them to 3.5 and 4. Both usually pass. Then I alter them ever so slightly. Then often times only GPT-4 passes. From that I reckon 3.5 is doing more of a training data regurgitation. It can answer things in its training data. But 4 seems to have an ability to reason - or maybe it is better able to generalise?

Failure after being altered slightly doesn't necessarily mean they aren't capable of solving it. That's a human failure mode as well that LLMs have adopted. If you really want to know if they can solve it don't stop there. Either, rewrite the question so it doesn't bias common priors or tell it it's making a wrong assumption.

I don’t doubt that - my point though is that maybe 3 can only solve things in its training data and 4 can figure things out.

3 seems to be more rigid. It needs babysitting to solve things. Which means it can only solve things I already know. 4 is more flexible and can solve things by itself.

Re: The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPT

#370

Post by code51 »

Mark Zuckerberg has a historic opportunity to completely reverse public perception in his favor and offer the best bet against OpenAI.

He is in a great position to do this because a paradigm shift happening to search business doesn't have the heavy effect on them that Google is subject to. Yes, content and ad business is also experiencing a paradigm shift but Meta is better positioned to rework their platform and cope with AI-generated content through a driving force (not control but support) over the most popular content generation tool out there - LLaMA and any upcoming variants.

Historic moment and I think Mark deep down wishes all that VR money went to AI instead.

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