Update on Llama adoption
ai.meta.com
ai.meta.com
Looking forward to continued updates and releases of Llama (and SAM!) from Meta.
[1] https://www.theverge.com/2024/8/28/24229068/california-sb-10...
It is disheartening so see the damage capacity in the hands of a couple of paranoic people who perhaps read the wrong scifi and had lots of power to influence others. If California passes this law, in a few years the world economy will be very different.
Sure they will. Just like how the auto manufacturers stopped trying to sell in CA after stricter emissions regulations and just like how companies stopped trying to serve European visitors after GDPR came into force. California is a $4T GDP, placing it just behind Japan and ahead of India in global rankings. I am sure they are happy to tell non-complaint companies to piss off and not let the door hit them on the way out. This is the canary in the coal mine for cowboy AI companies, once EU regulators get into the game their days of playing fast and loose with this tech will be over.
Here are some counter-points:
Regulation:
- Very little effort is made to evaluate risk of over-regulating, regulatory capture and counterproductive wrong regulation
- The downside of under-regulating is vastly overemphasized, most arguments boil down to "we have to act FAST now or x BAD thing might happen"
- The risk of over/wrongly regulating is vastly under emphasized with the same FUD reasons.
- according to one of the many straw-men argument in the pod I'm a libertarian against any and all regulation because I criticize possible regulatory capture, I would enthusiastically support regulation that foundation models have to be:
-- given freely to public researchers/academics for in-depth independent safety-research
-- open weighted after a while (e.g. after ~ a year, safety concerns should be mostly ruled out and new generations are out so ROI is already likely there. [e.g. there's NO safety reason at all for ClosedAI to not release gpt-3.5, llama3 is better already])
Proposed FLOP cut-off of SB 1047:
- according to the pod, the cut off is much more advanced than anything currently released.
- The 10^26 FLOP cutoff is way to low, llama-405b is ~4×10^25 FLOPs
- 405B is maybe 20% smarter than 70B, while taking over an order of magnitude more FLOPS to train, the cutoff itself is very likely not much smarter than the current SOTA.
- IMO none of the current SOTA models are very dangerous, but kill switch regulation is.
Kill-Switches:
- SB 1047 is (non-explicitly) calling for kill-switches over the cut-off due to liability of the model creators and market dynamics
- Any kill-switch regulation means a complete dead-end to any advanced open-weights AI. This means that huge corporations and governments will control any and all advanced AI-development. This is top-down control of the maths you are allowed to run on your computer IMO that is Orwellian as fuck.
China:
- mentioning china is FUD 101, it's basically AI's "think of the children"
- If they think they can stop china from building their own advanced LLMs, they're delusional. This regulation might even help them to get there faster. They don't even need to steal, there maybe a year or two behind the SOTA and catching up fast.
I just don't get how so many people on a site with "hacker" in the name want to make it impossible to hack on these things for anyone not employed by the big corporate AI research labs.
Disturbing approach to mitigating AI harms imo, this bill basically hopes it can limit the number of operators of an arbitrary model type so as to allow easier governance of AI model use. This ignores the reality that we already have large models openly released and easily modifiable (outside CA jurisdiction) which likely are capable of perpetuating “critical harms”, or that the information requirements to achieve the defined “critical harms” could be realized by an individual by simply reading a few books. There’s also no reason to simply assume that future models will require millions of dollars of compute to create; fulfilling the goals of this regulatory philosophy long term almost certainly requires the banning of general purpose compute to come close to the desired outcome of a supposed reduction in probability of some “critical harm” being perpetrated. We should be focusing on hardening society to the realities of the “critical harms” identified by this bill, rather than implicitly assuming the only reason we don’t see them as much irl is because everyone is stupid. The current paranoia wave around LLMs is just a symptom of people waking up to the fragility of the world we live in.
My company is building an application for an university client, regarding the examination of research data written in "human language" (mostly notes and docs).
Due the high confidentiality of the subjects, as often they deal with non-patented information, we couldn't risk using frontier models, as it could break the novelty of the invention, therefore losing patentability.
Now with Llama3.1, we can simply run these models locally, on systems that is not even connected to the internet. LLMs are mostly good in examining massive amount of research papers and information, at least for the application we are aiming at, saving thousands of hours of tiresome (and very boring) human labour.
I am trying to endorse Meta or Zuckerberg or anything like that, but at least in this aspect, I think Llama being "open-source" is a very good aspect.
Is the concern that prompts could be re-used for training by the provider and such knowledge become part of the model?
Open-Knowledge source with an Open-Inteligence that can guide you through the entire massive digital library of its own brain. Semantic data light-years beyond a Search Engine.
Kagi’s LLM can already do that. I believe so can Perplexity’s. Citing sources isn’t something only open models can do.
Sure. The point is the thing you said only an open-source model can do, it can do. Plenty of proprietary LLMs can cite sources.
The plain truth is most of the benefits of open models are not on the consumer side. (Or at least, I haven't seen any articulated.) They're on the producers'. Open models are better for those of us training models. That's partly why the open data debate is academic--very few people are training large foundation models because the compute and electricity costs are prohibitive.
Genuinely blown away the EU isn't doing this.
In the U.S., the solution may be in carving a legal safe harbor for companies that release their models per the OSI's draft definition of open source.
This sounds like the usual AI marketing with the word "open" thrown in. It's not articulating something youc an only do with an open source LLM (and doesn't define what that means).
I'm personally not thrilled with how locked down LLMs are. But we'll need to do a better job at articulating (a) a definition and (b) the benefits of adhering to it versus the "you can run it on your own metal" definition Facebook is promulgating. Because a model meeting Facebook's definition has obvious benefits over proprietary models run on someone else's servers.
I believe our world fights to destroy ideas like this because our economy drives our entire life.
>> No
>>> You can't imagine it
You haven’t articulated the idea you claim the “world fights to destroy”. (Just throwing around the word open without elaboration isn’t an idea.)
No. But you haven’t articulated why making everyone’s Facebook chats public is a net good. What does opening that data up confer in practical benefits?
Given what we know about LLMs, one trained only on public-domain data will underperform one trained on that plus proprietary data. If you want source data available, you have to either concede the "open" models will be structurally handicapped or that all data must be public.
Facebook says no, at least for Llama [1].
[1] https://itlogs.com/facebook-uses-user-data-to-train-ai-but-l...
We need dramatic reforms on copyright, as we’ve really let corporate interests crowd out our rights to human culture and ideas. While I alone cannot decide what we as a country should find reasonable, I can say I find 20 years + 5 years extension is perfectly reasonable and that corporations should have never been able to pay off politicians to get what they wanted. Let alone Sonny Bono, that bastard, signing in bills that specifically benefited him.
So, to reiterate, the idea I feel that corporations want to destroy is the idea that we, as a people, have rights to the works that form our popular culture and that no one man, let alone a faceless corporation, should be able to profit from a singular work for hundreds of years.
Also just to be clear, if you want to set up a RAG with an open weight model and a large dataset there's nothing stopping you. Download Red Pajama and Llama and give it a try.
OTOH why you want the data is not clear. You don't need it to run Meta'a models for free, or to fine-tune them for your own needs. The only thing the data would allow you is to pre-train from scratch, in other words to obtain the exact same set of weights that Meta is giving you for free.
I agree that for Llama, things are weird and they want to cover their bases, and that its better than nothing, but the specific use of “open source” is a long-running corporate dilution of what open source really means and I am tired of it.
The model is a static data file like a word doc.
Meta open sourced the code to run inference on the model (ie the code for Microsoft word reading a doc file).
They also open sourced the code to train/fine tune the model. (Ie the code for Microsoft word writing a doc file)
Then they released a special doc (the llama 3 model), but didn’t include the script they used to create that doc.
Anything else and you're just open-source-washing your proprietary technology.
Llama did not license its training data. It’s almost impossible to prove a particular LLM was used to generate any particular text, and there’s likely a bunch of illegal content within the dataset used to train the model (as is the case for most other LLMs)…
So why should I care about following a license? They have no mechanism to enforce it. They have no mechanism to detect when it’s being violated. They themselves indicated hostilities to other licenses, so why not ignore it?
i.e. high temperature, exotic samplers (i.e. typicality sampling), using lora/soft prompts/representation engineering, using it as part of a chain on top of other AI, etc
I don't care if every lawyer on earth is hired by Meta. Show me evidence that any particular LLM can be trivially fingerprinted based on its outputs. No, that "red token green token" paper on watermarking (https://arxiv.org/pdf/2301.10226) is not an example of this because of how trivial it is to defeat.
Edit: I can't reply to the comment saying "Subpoena" but this commentator seems to think that using any LLM at all is grounds for a court to issue a Subpoena requiring you to disclose which LLM you're using. If this actually happened, you'd see a massive chilling effect. Also, what stops someone from silently replacing the model with a non infringing one the moment someone starts asking questions?
I'm pretty sure that most courts aren't capable of getting expert testimony which is good enough to deduce that I silently swapped out my blarg_3.1 model which was made using a 1/3 llama3 merge with something else with gloop_1.5 which is no longer infringing.
Like seriously, I again ask, given the idea of courts with warrants and Subpoena's, why should I care about meta's licensing?
Edit2: If you're afraid of an employee leaking this info, don't tell your employees. Good thing clever model merging leaves no traces if you delete metadata!
Subpoenas.
https://www.forbes.com/sites/alexandralevine/2023/12/20/stab...
https://www.airforcetimes.com/news/your-air-force/2024/03/26... (i.e. as source of where LLMs get their classified data from)
If you scrape a large enough part of the internet, you're naturally going to get extremely illegal training data that you won't effectively filter out. I guarantee you that at least a tiny bit of highly classified information was not filtered out of most LLM training data (it wasn't found during the filter step), and it's quite remarkable that this and the above revelations have not led to anyone being Subpoena'd or related in regards to it.
So no, I think that folks will be literally the opposite of litigious on this issue. You want to play that game Zuck? Let's see what happens when I hire my researchers to find the dirt on your models dataset. We will see then who "settles out of court".
Meanwhile, all those lawyers need is to grab your text messages, emails, and slack messages. There's no need to look at code. If you have been up to something it's likely to come out pretty quickly.
p.s., Hiding your tracks after a subpoena is issued is a good way to end up in jail, at least in the US.
We don't have a commonly-accepted definition of what open source means for LLMs. Just negating Facebook's doesn't advance the discussion.
The open-source community is fractured between those who want it to mean weights available (Facebook); weights and transformer available; weights with no use restrictions (I think this is you); and weights, transformer and training data with no restrictions (obviously not workable, not even the OSI's proposed definition goes that far [1]).
In a world where the only LLMs are proprietary or Llama and the open-source community either remains fractured or chooses an unworkable ask, the latter wil define how the term is used.
[1] https://opensource.org/deepdive/drafts/open-source-ai-defini...
If you distribute the output of bison (say, foo.c) and not the foo.y sources, you would get pushback.
Then there is the EULA which makes it closed source right from the start.
This makes an LLM (emphasis on large) that is open source per this definition legally impossible. In every jurisdiction of consequence.
People like the term open source. It will get used. It is currently undefined. If the choice is an impractical definition and a bad one, we'll get stuck with the bad one. (See: hacker v cracker, crypto(currency) v crypto(graphy), et cetera.)
Maybe. But this isn't how language works. Particularly not English.
A corollary of No true Scotsman [1] is the person administering that purity test rarely gets to define a Scotsman.
When you see a MIT-licensed repository, do you call a screenshot, icons or other image assets "open pictures"? That would be laughable. We don't need non-ambiguity outside of context.
Second, the purism argument is just silly. Is GCC not open source because there's no LaTeX for IA-32 manual, and its x86 machine instruction generator is clearly distilled from it?
Even your reply to this one lacks nuance that I brought up. Where do you draw a line for the things that need "source" vs the ones that don't?
It has nothing to do with natural language evolution.
LLMs were entirely proprietary. In that context, Facebook put forward a foundational model one can run on their own hardware and called it open.
At the time, nobody had defined what an open-source LLM was. People came out to say Llama wasn't open. But nobody rigorously proposed a practical definition. The OSI has started, but they're being honest about it being a draft. In the meantime, people are organically discussing open versus proprietary models in a variety of contexts, most of which aren't particularly concerned with the OSI's definitions.
one is pragmatic, ergonomic, and motivated by advancing relationships between communicating persons in a naturally occurring way because the common denominator is a quick race to mutual understanding.
the other is manufactured, and not motivated by relation and advancing communication, but by how the shift in understanding benefits the one pushing for it, and often involves telling people how to think but is doing it through subversion.
Sort of. I'm claiming the meaning of open source when applied to AI is unsettled. There are guiding principles that seem to imply Llama is not open source. But merely pointing that out without offering a practical alternative definition almost guarantees that the intentionally-misused definition Facebook is promulgating becomes the accepted one.
however i do not think the alternatives need to be proposed at this moment in time because right now, the discussion is about holding people who intentionally reframe and misuse words accountable for their "double-speak" given the term's precedent.
conventionally, and by historical collective understanding, it is not open source.
i get you are attempting to highlight AI perhaps means this should be a definition reconsidered, but the irony here is the message itself is distorted due to the conflation, hence why consistency in language to me seems self-evident as a net good.
there is most certainly a difference between naturally occurring (which our brains reeeally support in terms of language development and symbolic communication), and manufactured (and therefore pushing for a word, or more aptly, a perspective's adoption).
i'd rather words manifest through a common need to reach mutual understanding as a means to relate to one another and this world, rather than having someone who stands to benefit from the change in definition, tell me what it means, and then expect me to just "agree", while they campaign around that and pretend it's the established definition (and not actually their own revised version).
it'd be one thing if people who were throwing the term around so loosely would be transparent: "Hey, we know this isn't historically what everyone means by OSS, but... that's OSS your OSS this is OSS, everything is OSS
instead a lot of these narratives are standing on the shoulders of the original definition and context of what it means to be OSS, and therefore the pedigree, implications (and whatever else for PR spin/influence), and simultaneously diluting what it means in the process as the definition gets further and further obfuscated by those influencing the change, and its pedigree is relied on as a distraction away from what is being done, or actually said.
They could use the more correct Open Weights (which is still a euphemism because of the EULA).
But they do not, and they know perfectly well what they are doing. They are the ones responsible for these discussions, but they double down and blame the true OSS people.
Of course it is. Look at this thread. Look at the policy discussions around regulating AI. Hell, look at the OSI's draft definition [1].
Pretending something is rigorously defined the way you want it to be doesn't make it so.
[1] https://opensource.org/deepdive/drafts/open-source-ai-defini...
"Open-source" represents a cluster of concepts but at the core of it there is a specific definition in spirit at least -- you can see the source for yourself, and compile it for yourself.
If the source is not available, why would you want to call it open-source? Just call it something else. As simple as that.
Definitions flow out of usage. The definition clarifies how the word is used and what people mean when they do use it.
You are, in a very literal sense, doing what Orwell, et al was so desperately against by actively controlling how language is permitted to be used.
Nobody can "force" someone else to use the correct definitions of words, but when people disregard their established meanings they risk communication breaking down and the confusion and misunderstandings that follow. If I went around speaking nonsense or making up my own invented definitions for established words I shouldn't expect to be understood and others would be perfectly right to correct me or ask that I stick to using the well understood and documented meaning of words if I expect to have a productive conversation.
It's also perfectly fair to call out people who twist the meaning of words intentionally so that they can lie, mislead, and manipulate others. When it comes to products, companies can't just say "Words can mean anything I say they do! There are no rules!" to get away with false advertising.
The meaning of words drifts in every living language.
> perfectly fair to call out people who twist the meaning of words intentionally
We don't have consensus around what open source means for LLMs. Facebook is pretending we do. But so is everyone in this thread claiming there is a single true definition of an open source LLM.
And it does result in a lot of confusion and misunderstanding until gradually people are taught the new definitions and how they are used. There are also groups of people who deliberately and continuously redefine words because they don't want to be widely understood. Some want to develop a means to signal to and identify others within their in-group, and some want to keep outsiders from understanding them so they can speak more openly in mixed company.
> We don't have consensus around what open source means for LLMs.
There are people who will argue about what open source means for anything. It's okay that open source means different things to different people, but it does result in confusion in discussions until people make their definitions clear.
I don't think that Facebook has earned the benefit of the doubt, in fact they've more than earned our skepticism, so it's very reasonable to see their new definition of "open source" as being nothing but marketing rhetoric at best, or at worst, as an attempt to twist our still developing consensus on what open source means into something that violates the philosophy/spirit of the open source movement.
Re-definition example: War is peace, freedom is slavery.
Since the new euphemism for downloadable models is a re-definition, Orwell would have been 100% against it. In fact the new use of "open source" is an Orwellian term.
i'm inclined to believe Orwell would have disagreed with OP, and would be asking himself -- why is there such a distinct push by those who benefit from the reframing, to reframe what Open Source means (compared to its already established meaning).
Orwell highlighted and warned against the consequences of people in influential positions of power intentionally distorting the collective associations with their new, updated versions of existing words, campaigning around those distortions, and intentionally reframing associations over time such that, the associations are polarizing and obfuscated, motivated by manipulation to benefit a select few, not motivated by advancing communication -- it certainly was not an example of society and its language naturally evolving "definitions" through usage.
and the novel wasn't a criticism against slang, or association/vernacular changing/evolving over time throughout collective use, nor was it a stance on requiring fixed, permanent, unwavering definitions -- it only emphasized how important it is to have consistent meaning.
he just wanted to encourage people to be skeptical of those pushing for the "different" or updated meaning of words, that clearly had a well-defined context, and association, previously -- why are they so dedicated and determined to "push" for a new meaning to get accepted, when there is a previously established and well accepted meaning already.
that doesn't sound natural to me, that sounds manufactured.
GPLv3 defines "source code" as the preferred form for making changes.
For most normal software that is identical to what you'd use to recreate it... but the way to make changes to an LLM isn't to rebuild it, but is to run fine-tuning on it.
Once again: for those who are new here. There is Free Software, which has a usefully strict definition.
And there is Open Source, the business-friendly -- but consequently looser -- other thing.
You can like one or both, they both have advantages and drawbacks.
But you cannot insist that "Open Source" has a very strict definition. It just doesn't. That's why the whole Free Software thing is needed, and IMHO, more important.
Statements like this remind me that I really need to KEEP GOING with this.
One could argue that the OSI should have gotten a trademark on the term. But the FSF doesn't have a trademark on the term "free Software" either, so the terms have approximately equal legal protections.
Meta using the term "open source" to apply to their model data when their license isn't an open source license is dishonest at best.
Free Software has the GPL, and all its related, healthy controversy. It's not perfectly clear, but it's far more battle-tested than the much more nebulous "Open source."
People who like "free software" put in work, and better understood that, to some extent, you can't have your cake and eat it too. "Open Source" is much more about a whole lot (to me, naive) wishful thinking.
(The OSI is a bunch of companies in a trenchcoat, the creators of the GPL were more principled.)
I just noticed they are currently discussing what "Open Source AI" should mean. You can join in and add your thoughts tot he discussion.
using it now on desktop (I am in China, so no OpenAI here) and in cloud cluster on project.
Locally it's actually quite easy to setup. I've made an app https://recurse.chat/ which supports Llava 1.6. It takes a zero-config approach so you can just start chatting and the app downloads the model for you.
What's your reservation about running it locally?
As a "web builder" I do think these tools will be very useful for accessibility (eventually), specifically generating descriptive alt tags for images.
What? There are so many open source projects from huge companies these days.
VSCode, .NET, typescript from MS
Angular, flutter, kubernetes, go, android, chromium from Google
Yes it is still better than not being able to access the weights at all, but calling these weights open source is not correct.
[0] https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/mai...
But many legal minds close to this issue have moved to the position that there is no meaningful distinction, at least when it comes to licenses like GPL.
For example: https://writing.kemitchell.com/2023/10/13/Wrong-About-GPLs
I don't think Facebook/Meta is the beacon of open-source goodness you think it is. The main reason they created yarn instead of iterating on npm is to use their own patent-friendly license they wanted to use with React (before the community flipped out and demanded they re-license it as MIT). Early Vue adoption seemed mostly driven by that React licensing fiasco.
You can benefit a lot from it, and I have... but do be sure you know what you are ferrying on your back before you decide to offer it a ride across the river.
Also it's bad when HN is downvoting fking GWERN
Not only is "a lot" of FOSS not released like this, both free software and Meta's models cannot be monetized post-release. If Meta decides to charge money for Llama4, then everyone with access to the prior models can keep their access and even finetune/redistribute their model. There is no strategic flip Meta can attempt here without shotgunning their own foot off.
For two - they can't do shit. Every one of those businesses can try to pivot into being the next OpenAI, but even OpenAI can't be fucked to turn a profit. They can't retract their models (remember Llama 1.0? hahahahahahahahaha) and they can't piss and moan to the authorities when their weights leak because 99% of them contain unlicensed copyrighted material in the first place.
The real twist to this Gordian knot? Open source is the only model you can release conventional AI under, because anything else precludes a Fair Use defense. Everyone that has "proprietary" models only manages it by playing keep-away with the community.
> Only a five-year-old child is either surprised or angered by this turn of events.
Five year old children know that they can keep their toys once they stop selling them at Wal-mart. Pulling a model off HF doesn't stop new finetunes or even redistribution and re-quantization of the old model. Mistral and Runway can do whatever they want, all their models are belong to us.
Zero privacy?
I want it to mean something!
They are synonym and mean the same thing.
Also open source means the license used should be something standard not proprietary, without restrictions on how you can use it.
Not at all. They're only similar in the sense that both are a build artifact.
> Without visibility into the training data, curation / moderation decision, the training code, etc Llama could be doing anything and we wouldn’t know.
"could be doing anything" is quite the tortured phrase, there. For one, model training is not deterministic and having the full training data would not yield a byte-perfect Llama retrain. For two, the released models are not turing-complete or filled with viruses; you can open the weights yourself and confirm they're static and harmless. For three, training code exists for all 3 Llama models and the reason nobody uses them is because it's prohibitively expensive to reproduce and has zero positive potential compared to finetuning what we have already.
> Also open source means the license used should be something standard not proprietary, without restrictions on how you can use it.
There are very much restrictions on redistribution for nearly every single Open Source license. Permissive licensing may not mean what you think it means.
Now seriously, by Llama being "sort of" open source, it does not seem to be something someone can fork and develop/evolve it without Meta, right? If one day Meta comes and says "we are closing Llama and evolving it in a proprietary mode from now on" would this Llama indie scene continue to exist?
If this is the case wouldn't this be considered a dumping strategy by Meta, to cut revenue streams from other platforms (Gemini/OpenAI/Anthropic) and contain their growth?
Sharing the end product, but not the tools and resources used to produce it, is how open source has always worked. If I develop software for a commercial operating system using a commercial toolchain, and distribute the source code under GPL, we would call that software open source. Others who get the code don't automatically get the ability to develop it themselves, but that's kind of beside the point. I don't have the rights to publicly redistribute those tools, anyway; the only part I can put under an open source license is the part for which I have copyright.
Training data for a LLM like Llama works similarly when it comes to copyright law. They don't own copyright and/or redistribution rights for all of it, so they can't make it open, even if they want to.
If that seems unsatisfying, that's because it is. Unfortunately, though, I don't think the Free Software community is going to get very far by continuing to try to fight today's openness and digital sovereignty battles using tactics and doctrine that were developed in the 20th century.
Considering how ludicrously expensive it would be to even attempt a ground-up retrain (as well as how it might be impossible), weights are enough for 99% of people.
They are never going to stop saying this or show us the actual source data. Imagine if they did... Do they even entertain the idea? Can they really not imagine Open Source AI being possible because of all the personal data they train on?
> By making our Llama models openly available we’ve seen a vibrant and diverse AI ecosystem come to life [...]
They all use the same model and the same transformer algorithm. The model has an EULA, you need to apply for downloading it, the training data set and the training software are closed.
> Open source promotes a more competitive ecosystem that’s good for consumers, good for companies (including Meta), and ultimately good for the world.
So the "competitive" system means that everyone uses LLama and PyTorch.
> In addition to Amazon Web Services (AWS) and Microsoft’s Azure, we’ve partnered with Databricks, Dell, Google Cloud, Groq, NVIDIA, IBM watsonx, Scale AI, Snowflake, and others to better help developers unlock the full potential of our models.
Sounds really open.
Everything in that sentence is false except the training data part.
>So the "competitive" system means that everyone uses LLama and PyTorch.
This sentence shows you don't understand the LLM landscape and it's also false.
>Sounds really open
Correct. They partner with practically every vendor available for inference, which, isn't even needed if you run their models locally.
Meta has done a lot of wrong things over the years. How they are approaching LLMs is not one of them.
You do need to apply on Huggingface to download the model.
> This sentence shows you don't understand the LLM landscape and it's also false.
PyTorch definitely is the most used ML framework.
You need to provide an email address and click a license agreement. Then you get a download link that expires after a day. I do not have to do this with the Linux kernel. Perhaps you are downloading from within Meta and are not exposed to these issues?
I am confused - I grabbed Ollama and pulled down some of these models. I don't recall having to go through any legal agreements. I just type:
ollama pull llama3.1
"To download the model weights and tokenizer, please visit the Meta Llama website and accept our License.
Once your request is approved, you will receive a signed URL over email. Then, run the download.sh script, passing the URL provided when prompted to start the download.
Pre-requisites: Ensure you have wget and md5sum installed. Then run the script: ./download.sh.
Remember that the links expire after 24 hours and a certain amount of downloads. You can always re-request a link if you start seeing errors such as 403: Forbidden."
You may come away surprised.
https://ollama.com/library/llama3.1/blobs/0ba8f0e314b4
> By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
I think my lawyer would have a few things to say about automatic legal agreements hidden somewhere in source control.
Is the ollama project part of Meta? Is that what's going on here?
You are free to quibble over how truly "open source" these models are, but I am very thankful that Meta has released them.
I am grateful to these developers. I am not grateful for a half open release and the redefinition of established terms. Which, judging by the downvoting in this thread, are now spread with fire and sword.
Projects like Python were completely usable then. But the corporations came, infiltrated existing projects and added often useless things. Python is not much better now than in 2010.
So you have perhaps React and PyTorch. That is a tiny bit of the huge OSS stack. Does Meta pay for ncurses? for xterm? Of course not, it only supports flashy projects that are highly marketable and takes the rest for granted.
So no, only a tiny fraction of the really important OSS devs are employed by FAANG.
Should they? Both of those are client-side software that aren't even really being monetized or profited-off by Meta. You could maybe get mad at Meta's employees for not donating to the software they rely on, but in the case of ncurses and xterm they're both provided without cost. They're not even server-side software, much less a deliberate infrastructure decision.
There's an oddly extremist sect of people that seem to entirely misunderstand what GNU and Free software is. It does not exist to stop people from charging money for software. It does not exist to prevent private interests or corporations from contributing to projects. It does not exist to solicit donations from it's users. All of these are options that some GNU or FOSS projects can choose to embody, not a static rule that they must all abide by. Since Cathedral and the Bazaar was published, people have been scrutinizing different approaches to Free Software and contrasting their impacts. We don't have to champion one approach versus the other because they ultimately coexist and often end up stimulating FOSS development in the long run.
> Python is not much better now than in 2010.
C'mon, now. Next you're going to tell me about how great Perl is in 2024.
At least Meta is shows its true colors here. It must have hurt that the OSS position has arrived at the Economist yesterday, so everyone is circling the wagons.
Again, there are arguments you can make that have weight but this isn't one of them. Every person with connection to wireless internet is running a firmware blob on their "open source" computer, it doesn't mean they're unable to bootstrap from source. Similarly, people that design Open Source infrastructure around Meta's binary weights aren't threatening their business at all. An "open" release of Llama wouldn't help those end-users, isn't even guaranteed to build Llama, and is too large to effectively fork or derive from. There's a good reason engineers aren't paying attention to the dramatic and insubstantial exposes that get written in finance rags.