So there is not that broad a window where this is the technology you want, unless at least one of: [cases]
I don't think those are exactly niche things. If at least a couple of those five don't apply to you, you're no fun at all.
Llama-3.1-405B not as good as GPT-4o or Claude Sonnet. Certainly Llama-3.1-70B is not as good as the similarly sized Claude Sonnet. If you are going to straight up use an API or chat interface, there seems to be little reason to use Llama.
Some providers are offering 405b at costs lower than 3.5 sonnet. E.g., Fireworks is offering for $3 input / $3 output.
That said, I think output speed is notably worse for all providers right now.
It's unbelievable how similar/convergent the big LLMs are. Only a slight improvement with 100x compute?? People have much bigger differences with much less variation of the core inputs (eg number of neurons). I wonder what the best explanation is. I can think of a few mediocre explanations.
I accuse you of being disingenuous. Zuckerberg's points seems pretty clear to me: from his experience in the closed Apple system, he understands the benefits to developers and end users of having an open system. (Obviously Apple benefits from their closed system; I bet he never feigned confusion about why they impose such restrictions.) ChatGPT and other popular LLMs are closed like the iOS, so he intends Llama to be open like Android/Linux, or if you feel the inscrutable nature of the models precludes that, open like Windows.
The bit about America's advantage being "decentralized and open innovation" of course involves a bit of rah-rah jingoism, but it's a coherent claim: what developers are free to create with open systems like the one he wishes to establish benefits America more than China, even if China would also benefit.
Your "which is it?" suggests you think there is some contradiction to resolve. Developers have a few different options right now, but the reasonable choices are all closed, and if they invest too much time and effort into those, the result of network effects will cause Meta to suffer from essentially vendor lock-in. Hence the need to have an open system that even if not quite as good as the best, is close enough that the flexibility and control that the openness offers makes up for the shortfall.
Zuckerberg's also clearly not the Open Source Zealot you caricature him to be. The Quest is pretty good on openness, but its Horizon OS is not open source, and there are limitations on camera access, and Meta can still close down all sideloading capabilities at will.
Also, to engage in a little bit of mind-reading, Zuckerberg sees no enemies in China, only in OpenAI et al. through the "safety" regulation they can lobby the US government to enact. The plausible catastrophe to work against then is them getting as much of a stranglehold over the AI space as Apple has over its own. Of course, he can't say this out loud without hastening the doom he's trying to avert.
"Also, to engage in a little bit of mind-reading, Zuckerberg sees no enemies in China, only in OpenAI et al. through the "safety" regulation they can lobby the US government to enact."
This is a reasonable position, apart from the fact that it is at odds with the situation on the ground. OpenAI are not lobbying the government in favour of SB 1047, nor are Anthropic or Google (afaik). It's possible that in future they might, but other than Anthropic I think this is very unlikely.
For me, the idea of large AI companies using X-risk fears to shut down competition falls into the same category as the idea that large AI companies are using X-risk fears to hype their products. I think they are both interesting schemes that AI companies might be using in worlds that are not this one.
If everyone affirms this is indeed all the major arguments for open weights, then I can at some point soon produce a polished full version as a post and refer back to it, and consider the matter closed until someone comes up with new arguments.
Feels like the vast majority of the benifits Zuck touted could be achieved with:
1. A cheap, permissive API that allows finetuning, some other stuff. If Meta really want people to be able to do things cheaply, presumably they can offer it far far cheaper than almost anyone could do it themself without directly losing money.
2. A few partnerships with research groups to study it, since not many people have enough resources that doing research on a 405B model is optimal, and don't already have their own.
3. A basic pledge (that is actually followed) to not delete everyone's data, finetunes, etc. to deal with concerns about "ownership"
I assume there are other (sometimes NSFW) benefits he doesn't want to mention, because the reason the above options don't allow those activities is that Meta loses reputation from being associated with them even if they're not actually harmful.
Are there actually a hundred groups who might usefully study a 405B-parameter model, so Meta couldn't efficiently partner with all of them? Maybe with GPUs getting cheaper there will be a few projects on it in the next MATS stream? I kinda suspect that the research groups who get the most out of it will actually be the interpretability/alignment teams at Google and Anthropic, since they have the resources to run big experiments on Llama to compare to Gemini/Claude!
A basic pledge (that is actually followed) to not delete everyone's data, finetunes, etc.
Such a commitment would not be credible. Even if you're confident Meta won't do the classic Google thing of sunsetting the service, or go broke, or change CEOs, they'd still be subject to things like DMCA takedowns that running locally safeguards you from.
This is a stronger argument than I first thought it was. You're right, and I think I have underestimated the utility of genuine ownership of tools like fine-tunes in general. I would imagine it goes api < cloud hosting < local hosting in terms of this stuff, and with regular local backups (what's a few TB here or there) then it would be feasible to protect your cloud hosted 405B system from most takedowns as long as you can find a new cloud provider. I'm under the impression that the vast majority of 405B users will be using cloud providers, is that correct?
I think a lot of it is simply just eating away at the margins of companies and product that might become larger in the future. Even if they are not direct competitors, it's still tech investment money going away from their VR bets into AI. Also big companies fully controlling important tech products has proven to be a nuisance to Meta in the past.
It’s here. The horse has left the barn. Llama-3.1-405B, and also Llama-3.1-70B and Llama-3.1-8B, have been released, and are now open weights.
Early indications are that these are very good models. They were likely the best open weight models of their respective sizes at time of release.
Zuckerberg claims that open weights models are now competitive with closed models. Yann LeCun says ‘performance is on par with the best closed models.’ This is closer to true than in the past, and as corporate hype I will essentially allow it, but it looks like this is not yet fully true.
Llama-3.1-405B not as good as GPT-4o or Claude Sonnet. Certainly Llama-3.1-70B is not as good as the similarly sized Claude Sonnet. If you are going to straight up use an API or chat interface, there seems to be little reason to use Llama.
That is a preliminary result. It is still early, and there has been relatively little feedback. But what feedback I have seen is consistent on this.
Prediction markets are modestly more optimistic. This market still has it 29% to be the #1 model on Arena, which seems unlikely given Meta’s own results. Another market has it 74% to beat GPT-4-Turbo-2024-04-09, which currently is in 5th position. That is a big chance for it to land in a narrow window between 1257 and 1287. This market affirms that directly on tiny volume.
Such open models like Llama-3.1-405B are of course still useful even if a chatbot user would have better options. There are cost advantages, privacy advantages and freedom of action advantages to not going through OpenAI or Anthropic or Google.
In particular, if you want to distill or fine-tune a new model, and especially if you want to fully own the results, Llama-3-405B is here to help you, and Llama-3-70B and 8B are here as potential jumping off points. I expect this to be the main practical effect this time around.
If you want to do other things that you can’t do with the closed options? Well, technically you can’t do most of them under Meta’s conditions either, but there is no reason to expect that will stop people, especially those overseas including in China. For some of these uses that’s a good thing. Others, not as good.
Zuckerberg also used the moment to offer a standard issue open source manifesto, in which he abandons any sense of balance and goes all-in, which he affirmed in a softball interview with Rowan Cheung.
On the safety front, while I do not think they did their safety testing in a way that would have caught issues if there had been issues, my assumption is there was nothing to catch. The capabilities are not that dangerous at this time.
Thus I do not predict anything especially bad will happen here. I expect the direct impact of Llama-3.1-405B to be positive, with the downsides remaining mundane and relatively minor. The only exception would be the extent to which this enables the development of future models. I worry that this differentially accelerates and enables our rivals and enemies and hurts our national security, and indeed that this will be its largest impact.
And I worry more that this kind of action and rhetoric will lead us down the path where if things get dangerous in the future, it will become increasingly hard not to get ourselves into deep trouble, both in terms of models being irrevocably opened up when they shouldn’t be and increasing pressure on everyone else to proceed even when things are not safe, up to and including loss of control and other existential risks. If Zuckerberg had affirmed a reasonable policy going forward but thought the line could be drawn farther down the line, I would have said this was all net good. Instead, I am dismayed.
I do get into the arguments about open weights at the end of this post, because it felt obligatory, but my advice is come for the mundane utility and mostly don’t stay for the reheated arguments if you already know them – Zuckerberg is pledging fully to plow ahead unless prevented, no matter the situation, that is the only new information. I appreciate his candor.
Options to Run It
You can download it. In theory you could… run it on two MacBook Pros?
You can use it directly from Meta.
You can use it on Repligate.
You can use it on Groq.
Doubtless there are many other ways, and will be more soon.
The Model Card
Meta offers us a 92 page document for Llama 3.1. What do we got?
I’ll cover the highlights, for more technical details you can read the whole thing.
They trained on 15T tokens, up from 1.8T for Llama 2. Knowledge cutoff is EOY 2023. Data filtering all sounds standard. Mix was roughly 50% general knowledge, 25% math and reasoning, 17% code and 8% multilingual.
Special attention was paid to coding via expert training, synthetic data generation and execution feedback, and the smaller models showed improvement when trained on output from the larger model.
Their FLOPS used was 3.8 x 10^25. That is similar to previously released frontier models, and still leaves a doubling before hitting 10^26. Llama 3-405B would not be a covered model under SB 1047, nor was anything changed to avoid it being covered. Llama-4-Large would presumably be covered. They used up to 16k H100s.
They offer us both the base model without fine tuning, and the Instrust version that does have fine tuning. I agree that having direct access to the base model is cool given that we are open weights and thus not making the safety protocols stick anyway.
They mention that they do not use Mixture of Experts and stick to a standard dense Transformer model architecture, in favor of simplicity. Similarly, they use standard supervised fine tuning, rejection sampling and DPO for post training. It sounds like Llama 3’s ‘secret sauce’ is that it is big and uses lots of good data, did (presumably) competent execution throughout, and otherwise there is no secret.
They used a ‘multilingual expert’ model trained for a while on 90% multilingual data to use as part of the training process. Interesting that it wasn’t useful to release it, or perhaps they don’t want to give that away.
They define ‘reasoning’ as ‘the ability to perform multi-step computations and arrive at the correct final answer.’ That is certainly a thing to be good at, but doesn’t seem that close to what I think of when I think of the word reasoning.
They note in 4.2.3 that most of their post-training data is model generated, and previously noted that some of their fine tuning data used for DPO was synthetic as well. They manually fixed problems like extra exclamation points or emojis or apologies, which implies that there were other more subtle imbalances that may not have been caught. If you are ‘carefully balancing’ distributions like that in your data set, you have to assume you have an issue with anything not intentionally balanced?
They did six full rounds of their fine tuning techniques.
When training their reward model they also added a third ‘edited’ response option when doing pairwise comparisons (so edited > chosen > rejected). They took into account four levels of strength of preference when asking models.
They claim that Llama 3.1 Instruct of all sizes has tool use. They say they introduce this in post training and discuss in Section 4.3.5. In particular it was trained to use Brave Search (that choice of search engine seems enlightening), Python interpreters and Wolfram Alpha’s API. They also claim to have improved zero-shot tool use.
Performance is claimed to be in line with scaling law predictions.
Benchmarks
Here is their key benchmarks chart. Never put too much weight on the benchmarks.
They are choosing an odd set of benchmarks here, and they are somewhat cherry-picking their opposition in the first two categories. Most glaringly, Claude Sonnet 3.5 is in the 70B class. If you are going to have an entire section on Long Context, why are you excluding all Gemini models, and not testing Gemma on long context at all? One can excuse GPT-4o Mini’s exclusion on time constraints.
The tool use benchmarks don’t ring a bell and have bizarre scores involved. So Claude Sonnet and GPT 3.5 ace BFCL, but suffer on Nexus, which I think is supposed to be here a subset of the full Nexus benchmark?
Here are some more results purely from pre-training.
Here are some exams, a lot of which are saturated (or contaminated). Llama does well on AP Physics here, but most of these everyone is acing at this point.
More tool use:
I am willing to essentially say ‘they are good benchmarks, sir’ and move on.
Seal from Scale has added them to the leaderboard, where they do quite well.
It comes in second overall on ZeroEval slightly behind Claude 3.5 Sonnet:
Then 5.3 covers human evaluations, which as far as offered are fine.
According to these tests, GPT-4o robustly beats Llama-3 405B in human comparisons. Claude 3.5 Sonnet does not. including losing on straight English and Multiturn English. It obviously all depends on which humans are being asked and other details, but this backs up the Arena rankings that have GPT-4o as still satisfying user pairwise comparisons. I will of course keep on using Claude 3.5 Sonnet as primary, while experimenting with Llama-3-405B just in case.
Also, pour one out for Gemini. So sad.
One concern is that humans start at some point to not be able to tell which model is smarter, making their judgments about other things.
Except of course that once it gets to 99.99% it will not take long to get to 100%, and then to rapidly widen the gap. Indeed, it is key to notice that if you can make something smarter than 99% of humans you are very close to making one smarter than 100% of humans.
Further discussion points out that if you confine outputs to formal proofs and designs for physical objects and other things that can be formally verified by a dumb checker, then you can work around the problem. True, if you are willing and able to confine the outputs in this way.
Human Reactions in the Wild
The other way of looking at how people actually choose products:
Presumably someone will quickly build reasonable versions of those features. An API that is compatible with existing code for Claude or GPT-4 cannot be that far behind. The question then goes back to the model comparison.
John Pressman is similarly loving the base model and its style.
There was remarkably little feedback on model strength. With Claude and ChatGPT and Gemini I got a lot more responses than I got this time around.
From those I did get, there was a consistent story. It is a solid model, you can call it frontier if you squint, but for practical purposes it’s behind GPT-4o and Claude Sonnet, once again pour one out for poor Gemini.
What’s It Good For?
I’m sure everyone is excited to build and is going to be totally responsible and creative.
Oh. Right.
This was the first concrete proposal I saw.
Actively misaligned model, yep, sounds like the natural first thing to do.
I was curious, so I asked Llama 3.1 70B as a test about how to set up Llama 3.1 405B.
It told me I would need 16 GB VRAM, so my RTX 3080 would be pushing it but I could try. Alas, not so much.
When I asked who would actually benefit from doing this, I got this response:
Whereas if you’re doing API calls, why not stick with Claude or GPT-4o? So there is not that broad a window where this is the technology you want, unless at least one of:
That’s not to say that this isn’t a big accomplishment. Llama 3.1 is clearly state of the art for open weights.
It seems unlikely it is fully frontier or state of the art overall. Remember that GPT-4o and Claude Sonnet 3.5 are not in the full 400B-style weight class. A lot of the point of those models was to be faster and cheaper while still being frontier level smart. In some sense you should compare Claude Sonnet 3.5 to Llama-3.1-70B, which is less close.
Also note that Llama-3.1-405B and Llama-3.1-70B do not seem that distinct in capabilities. Perhaps for many practical purposes this is once again a lesson that the 70B-level is frequently ‘good enough’?
So in practice, my guess is that Llama-3.1-405B will primarily be used for model training, a combination of evaluations, synthetic data and other forms of distillation. The effective purpose of Llama-3.1-405B is to help those behind in AI build AIs. But my guess is that in terms of the actual AI mostly people will fine tune smaller models instead.
Another big use will of course be for spam and slop and phishing and other mundane harms. A lot of that will be aimed squarely at Meta via Facebook and Instagram. Facebook already has a pretty severe slop problem. You wanted to arm everyone with the same models? You got your wish. However for such purposes I doubt you even want to bother with the expenses involved with 405B, a little marginal quality is not worth it. So probably little (marginal) harm done there.
The Other Other Guy
Meanwhile, I do admire Mistral’s habit of cultivating the minimum possible amount of hype, such as choosing Wednesday the 24th to drop Mistral Large 2, which they are calling Large Enough.
Llama 3.1’s write-up does not include GPT-4-Mini, but Mistral works fast and is already incorporating the digs at Llama 3.1.
There are definitely big weaknesses here, but for some purposes it might offer good value. Too soon to tell.
The model is available ‘for research purposes only’ on Hugging Face.
Safety
They cover safety in 5.4.
Llama-3-405B is open weights, and the base model is available to boot.
If someone actively wants to get around Meta’s safety protocols, they will.
(There is also the cheap, quick and dirty alternative, which is to skip all that and jailbreak on day one. Which of course Pliny the Prompter did once again, noting that it was ‘a piece of cake.’)
There are two practical forms of safety that are available here.
If you are doing a safety test on Llama-3 for things like ‘uplift’ of dangerous technologies, you need to essentially give your testers access to the version without any safety protocols. Because that’s the version they will have when it counts.
Ideally, you would also offer the opportunity to add scaffolding and fine tuning in various forms to strengthen the model, rather than only testing it in static form on its own. Again, you must test the thing you are irreversibly causing to exist in the future, not the thing people can use right now in a given room. So again, not the right test.
Thus, when their test found ‘insignificant uplift’ for cyberattacks or chemical or biological weapons, I only count that if they got a deliberately made unsafe version of Llama 3, and even then only partially. Without even that, we learn little.
To be clear, my expectation is that there is not substantial danger here, but I worry the tests would not have caught the danger if there was indeed danger.
One can also ask similar questions about the red teaming. If the red teams were indeed confined to using prompts that is not a great test of real world conditions.
If you are doing a safety test for a developer trying to incorporate the model into their product without bad publicity, that is different. Then you are on equal footing with closed models.
Thus, their offer of a prompt guard and Llama guard are reported as helpful, and this is nice if people actively want to stay safe, and not so nice if they do not. You cannot force people to use it.
In terms of that second type of safety, they offer their results in 5.4.4, but I found it impossible to understand what the numbers meant, and they did not offer comparisons I could parse to non-Llama models. I am choosing not to worry about it, as the lived experience will tell the tale, and many will modify the training anyway.
The more serious tests start in 5.4.5.
They find that Llama-3 has some issues with executing malicious code, which 405B does 10.4% of the time in code interpreter, versus 3.8% for the 70B model, ‘under certain prompts.’ And they find prompt injections worked 21.7% of the time.
These charts are hard to read, but Llama-3 405B seems to be doing okay, note that Claude was not tested here. Also of course this is comparing Llama-3 in its ‘safety enabled mode’ as it were.
They find Llama-3 does not have ‘significant susceptibilities in generating malicious code or exploiting vulnerabilities.’
Okay, so that’s weird, right? Why is Llama 3 70B a lot more persuasive here than 405B? Perhaps because 70B was the judge? According to Llama, these success rates are typical for spearfishing attempts, which is itself a sad commentary on everyone. Claude thinks this was typical of ‘well-crafted’ attempts.
In practice, my beliefs about safety regarding Llama-3-405B are:
Given the power law distribution of harms and the question being our confidence level, Jeffrey should get odds here. I do think it would be useful to see what the market price would be.
Again, I see these as acceptable costs and risks for this model. Indeed, if there is risk here, then it would be good in the long run to find that out while the damage it would cause is still not so bad.
Three People Can Keep a Secret and Reasonably Often Do So
Zuckerberg makes the incredulous claim, both in his interview with Cheung and in his manifesto, that it is impossible to keep models from being stolen by China and the CCP. That any secrets we have cannot be protected in the medium term.
His response is to propose the least security possible, giving models away freely. Under his thinking, if you are going to fully pay the costs, you might as well get the benefits, since the brief delay he expects before everything is stolen wouldn’t matter.
It is an excellent point that right now our security at those labs is woefully inadequate.
Leopold Aschenbrenner would add that even if you intended to make your models open weights, you would still need to protect the algorithmic insights within the labs.
Even Meta is not an open source AI company. Meta is an open weights AI company. They are (wisely) keeping plenty of internal details to themselves. And trying to protect those secrets as best they can.
It is Obvious Nonsense that it is impossible to ever protect secrets.
Also note that Zuckerberg thinks our ‘geopolitical adversaries’ would successfully steal the models, but then that is where it would end, the secrets would then be kept, including from our friends and allies. Curious.
Zuckerberg’s defeatism here is total.
The question is, are model weights a case where you cannot both deploy and properly use them and also simultaneously protect them? What would be the practical costs involved for how much security? Obviously there will be some cost in efficiency to implement effective security.
The other question is, are we in practice capable of implementing the necessary protocols? If our civilization is unable or unwilling to impose such restrictions on labs that would not choose to do this on their own, then we have a big problem.
Another question has to be asked. If it is impossible to ever protect secrets, or in practice we will choose not to do so, then that would mean that anything we create will also fall into the wrong hands, at minimum be used by our enemies, and likely be unleashed without restriction on the internet and for every malicious purpose. If you truly believed that, you would want to lay the groundwork to stop dangerous AIs before they were created, in case AIs did become too dangerous. Otherwise, once they were created, by your own claims it would be too late. Instead, Zuckerberg and others are willing to simply bet the planet and all of humanity on the resulting natural equilibrium being good. Why would that be?
The Announcement and Interview
It’s a very different approach than going on Dwarkesh, instead he goes to the friendly territory of Rowan Cheung, who is always gung ho about everything.
He announces Llama 3.1 405B, 70B and 8B, advocates for open models, complains about Apple and reiterates his prediction of a world of billions of AI agents except they are all mere tools with nothing to worry about.
Zuckerberg’s Open Weights Manifesto
Marc Zuckerberg offers his thesis that Open Source AI is the Path Forward.
Ultimately this is a rehash of the same old arguments.
He has said it all before, as have many others. Yet there is so much he ignores, because it does not help his case.
I found this commentary on it enlightening:
So I suppose this response is a rehash of the same old responses.
Most important is what this ‘covering of all major points and arguments’ doesn’t cover.
You can very safely skip both the letter and the rest of my response.
To the extent that I considered deleting the non-summary response below, but hey.
Still here? All right, let’s play it again, Sam.
His safety argument is based on dividing harms into intentional versus unintentional. This is a useful distinction in many circumstances, and in some sense axiomatically true, but he then uses this to assume that any given thing must either be a bad actor, or be due to some sort of active mistake. As I’ve tried to explain many times, that does not cover the space, an unintentional harm can result from everyone following their individual incentives.
Linux gets safer with many eyes because what is safe for the user is safe for others, so the incentives align, and if something goes wrong for you that mostly blows up you in particular, and there is ample opportunity to fix the error after it happens and try again. Neither of these will be true in the context of future more capable AI.
His argument for why open weights are safer for unintentional harm is that the system are more transparent and can be widely scrutinized. Again, that only works if everyone who runs the system actively wants their system to be safe in that same sense. Otherwise, whoops. Overall it is an advantage, but he treats it as the only consideration.
You could call failing to treat safety of your AI the way Linux treats its safety ‘intentional’ harm if you would like, I suppose, in which case intentional harm includes intentionally taking risk, or trading off risk to get reward, but obviously everyone including Meta and every corporation and government will be forced to (and choose to) do some amount of that.
For unintentional harm, he draws distinction between ‘individual or small scale’ actors versions large actors.
For small actors, he goes straight to the ‘good guy with an AI will stop the bad guy with an AI’ rhetoric, in different words. The entire frame is that AI will remain a tool, and the assumption is that wider distribution of identical tools to all players will favor defense over offense, without any argument for why we should presume that.
Zuckerberg says that widely deploying tools at scale is how Meta protects its social networks. That is true, but the reason Meta is able to (somewhat) protect its social networks is that it brings a massive advantage to the table. It is in context the ‘good guy with the tool’ and has better tools than the bad guys with their tools. Ensuring that you can never win that arms race does not seem like a good idea, even in this narrow context.
Why would you ensure your opponents are armed, other than a deeply strange sense of honor? Why would you assume that access to more inference compute will be decisive in such conflicts? Or that not having superior models to work with is actively helpful, as he suggests? I do not see why, and he does not say.
He certainly does not argue why this should allow us to secure ourselves against other forms of malicious use, that do not involve a clear defending agent the way Meta defends its social network. He does not explain how this would defend against the typical catastrophic threats, even if AI remains a tool. There’s only assertion here. He says that with new harms ‘the balance of power would be crucial’ but then uses this to… advocate for giving up a key advantage in the balance of power between the defenders and these potential bad actors. How does that help?
If AI in the hands of such a small actor becomes more than a ‘mere tool’ of course than all of this is out the window.
And in particular, if the threat model is that competition between AIs, and competition between humans with their AIs that they feel constant pressure to give authority to while removing humans from loops, and to turn into increasingly independent and strategic agents? Then open models take away all our options to provide any checks on these competitive dynamics, short of monitoring of every computer everywhere. Such questions are simply not addressed.
If it turns out a mistake has been made, it could easily be too late. Once you release such an open model you cannot take it back, again short of a true dystopian scenario.
Then he asks about ‘states with massive resources.’ Again notice the bifurcation trick, dealing only with one extreme or the other, but yes these are important cases. He says, our advantage is openness, so we must be open and release all our progress to avoid giving China the advantage. You see, China is great at espionage, so they would simply steal our models anyway.
(Which is an excellent point! We should totally be locking down the labs to stop this.)
Zuckerberg also posits another false binary choice:
While there are those like Zukerberg that propose that open models be at the frontier and the standard, this ‘world of no open models’ is a fever dream. There are some who take extreme positions, but the central argument is whether there should be some upper bound on how capable open models can be at a given time, or whether open models should be required to abide by ordinary safety regulations.
The argument is not that there should be no open models at all. That is not my position. I have zero problem with his release of the Llama 3.1 70B model. And if I felt that things would stop here, I would not be especially concerned about Llama 3.1 405B either (although for that there are others who feel more strongly, and there are national security concerns), it is the principle and precedent for the future that is being debated here.
Even more than that, the argument that not giving away our best models and entire ecosystem of innovations increases the probability that we will not be in the lead? This is Obvious Nonsense. I notice I am deeply confused.
Linux is a great thing. We do not maintain Linux with the goal of giving America and its allies the lead in operating systems. It will obviously do nothing of the kind. That. Makes. Zero. Sense.
He says most of today’s tech companies are built on open source software. So we should give that software to China so they can build their own companies? To their government as well? Or else we risk losing our lead? What? Seriously, what?
Yet somehow they keep repeating that line.
If everyone affirms this is indeed all the major arguments for open weights, then I can at some point soon produce a polished full version as a post and refer back to it, and consider the matter closed until someone comes up with new arguments.
Fun Little Note