I'm a Norwegian, and I use the national library almost every day for searching through texts. They have truly one of the best working user interfaces (and functionality) for searching through the massive amounts of text.
It's really fantastic. I just wished there were fewer restrictions on the content that is accessible.
(a lot is only accessible from Norwegian IP addresses, so it's one of the main reasons I maintain a VPN as I'm Norwegian but live in the UK; a second set is only available from the IP addresses of libraries or research institutions - still huge amounts that are generally available, though)
How true is this statement:
"He asserted that any country with its own language that did not have a sovereign LLM trained in that language was at a disadvantage as a globally trained, English-speaking LLM would not know about that country’s history, news and culture that was described in the local language."
I thought all big players already train on basically everything remotely available to them no matter the language or quality, so his take sounds like an opinion formed in the early days of generally available LLMs.
> The Olivia system is an HPE Cray Supercomputing EX system, with 448 GPUs and 64,512 CPU cores.
Training a sovereign LLM with this meager hardware as opposed to a LORA on some open source model seems like a huge mistake and a potential red flag.
There is no way these people have the resources to train a fully fledged LLM, so claiming that is their goal makes me think they don't intend for the LLM to be useful.
Which begs the question, whose money are they wasting - and why?
It may not be useful to anyone outside, but it's possible that one of the goals is institutional learning (that is, embedding the knowledge in how to build LLMs in an organization).
Even though it's nominally the national library behind this, they were probably chosen (as per the article) because they legally own and can use all NO material for this end. I'd guess researchers from related entities like unis will be involved in the process.
They successfully have made PoC finetunes before, so the next step is training fully fledged LLMs.
I don’t think they aim to anything worthwhile. The finetunes were incredibly broken. I’m guessing it’s more about having the method to do it. I’m not convinced it’s super useful but I’m not one to decide who gets to do what with the research funds.
One finetune I tried did make fun of humans expressing their feelings in the chat. Often.
One other finetune did hallucinate that it was a doctor and my baby had terrible diseases, every time I just wrote "hei" (with a generic neutral system prompt that likely triggered this behaviour though).
I think Olivia is big enough for what it’s used for. In my opinion it’s better to stay up to date and not waste too much money on hardware at the moment.
i18n language models are not area something frontier labs are focusing ton of resources on? ( certainly not in Norwegian)
The corpus of content in Norwegian - may not require very large clusters, or even if it does, this is best that the library could do, it would be certainly more than anyone else is investing in Norwegian models
SOTA models do not have the access to the quality of content that the national library does? The article mentions licensing with newspapers specifically, and the library has access to its own content archive.
English and Norwegian are not closely related language families, perhaps LoRA is not best approach?
I am curious if there is published research on how well localization works with LoRA depending on how far off the target language grammar/vocabulary is from English.
Projects like this typically have more than one objective and are not only building SOTA project, but is also to build/train foundational local talent , similar to universities launching satellites .
> English and Norwegian are not closely related language families, perhaps LoRA is not best approach?
Yes, they are. English is a West Germanic language. Norwegian is a North Germanic language. The French vocabulary in English obscures it a bit, but the two languages have similar grammar and the vocabulary has a huge number of close cognates.
E.g. day -> dag, ship -> skip, apple -> eple, cow -> ku (which makes more sense when you pronounce them correctly out loud), bairn (child; mostly Scotland and Northern England) -> barn, hop -> hopp, yule -> jul just to give a random selection of English Germanic words.
But more than that, the frontier models both a) knows Norwegian quite well, b) certainly knowns German and Dutch well, and there's a continuum of language transfer around the North sea especially when accounting for sounds rather than modern orthography, e.g. to take a couple of examples from above: ship -> schip -> Schiff -> skib -> skip; day -> dag -> Tag -> dag). The "jump" to Dutch already weeds out most of the French. A lot of modern Norwegian orthography comes from Danish, which again shares more than modern Norwegian does with German.
Knowing any of these helps a lot with learning Norwegian and vice versa. E.g. I'm Norwegian, I've never learnt Dutch, but I have learnt English and German, and I can read Dutch fairly well from that alone.
The largest problem is available training data actually.
They have already done experiments with dittrent sub 10b models with both fine-tuning and fully from scratch. And last I check the fully from scratch captured the language in a better way.
DeepSeek claims to have trained on something like 2k H800, this is ~0.5k GH200 … it’s not nothing. Sure they’re not going to _serve_ it at scale, but that’s not the point?
Also the line between “finetuning a base model” and “man this is a real good initialization” gets pretty blurry at scale.
"Norway's sovereign wealth fund, officially known as the Government Pension Fund Global, is the world's largest sovereign wealth fund with assets exceeding \(\$2\) trillion. Established in 1990 and managed by Norges Bank Investment Management, it was created to channel surplus petroleum revenues into long-term global investments to benefit future generations."
And this is before anybody ever thought about optimizing the training process. (Currently it's just pytorch analyst-as-coder slop, with extremely overprovisioned quantizations, etc.)
The frontier models know Norwegian just fine. They can also adapt to Norwegian dialects, and even ape old Norwegian fairly well.
E.g. I had Claude describe the novel "De knyttede næver" from 1911 in Norwegian orthography ca. 1911, as it's a novel I've read, and it does a good job.
What it lacks is an understanding of Norwegian literature, culture and history. It had to look up "De knyttede næver", which was one of the best-selling Norwegian novels of its time before I'd get anything out of it (ChatGPT does better; in thinking mode in particular it gives a detailed summary).
While not exactly well known today, the author was a prominent newspaper journalist for decades, and the novel series is well enough known that e.g. there's a Norwegian singer that took his stage name after the protagonist, and it was covered in Norwegian papers and books for decades (partly because of controversy over the authors political views and how they coloured his novels), so it does feel like a reasonable test that reveals a quite significant knowledge gap.
I do agree with you that it'd be better if the data set from the national library was made more accessible, though it seems a major addition here is that they have a deal to train on copyrighted data locked away in their archives that they have limitations on the use of.
But even just making the out of copyright data in their collections would be a great start.
This can’t be right. 2 PB of flash is like $200k. It’s within reach of many individuals. Then again I guess you don’t need that much storage so maybe it is.
Boy pricing is pretty nuts these days. I have half a petabyte in Seagate enterprise drives myself and I didn’t pay anything close to that to acquire it. Such a pity about the flash storage. 2 years ago we built 200 TiB or something of flash using Samsung PM1633 or something and it was a fraction of the cost per gigabyte that $1m would imply.
What's special about it is not the flash but training an LLM based on the content, much of which is still in copyright and which the library has restrictions on how they are allowed to use (irrespective of the legal position of training on it) and which required an agreement with the copyright holders.
As a Norwegian this sounds like a mistake. Who will use this LLM? Where? For what? The underlying data could be made more easily searchable and digestible for agents in general if the goal is better knowledge of Norwegian culture.
That said, they are quite limited in what they are allowed to share of in-copyright works, and nb.no is a fantastic resource as it is (though you'll need a Norwegian IP address for too much of it - it's one of th main reasons I maintain a VPN) - if they are allowed to make it accessible there, it'd be great.
But they also have vast amounts of out-of-copyright data that I hope they'd make more easily accessible...
Exactly, if there's one thing transformers are good at it's translation. One I've found particularly nice: any question ChatGPT can answer in English it can answer in French. I'm assuming Norwegian too. So there's no point.
The point is that norway willl have its own LLM. And will not have dependencies to another state or private company. The goal is not to be the best model. But to have a model that include more Norwegian data then other LLM and that it's not screwed against other sources.
Yes transformers are great at translation as that is their purpose.
LLMs are not great at preserving cultural uniqueness and diversity. Take how “delve” has reentered the lexicon because the human assessors for pre training dialect of English uses “delve” a lot.
There is a lot of benefits to training specifically for a unique culture with unique norms to preserve the culture as we increasingly rely on LLMs.
Both Claude and ChatGPT can translate into minor dialects of Norwegian they will have seen very few works in because very few printed works exist in them.
E.g. I've tested both my local spoken dialect, which is rarely written, and a sociolect used by a 1970's Maoist group consiting of a few hundred people, where most of the printed material consists of novels from a couple of ex-members that became authors.
In the latter case, it claimed to not know, but was able to get a good match from just a description.
I also just had it ape Norwegian orthography from the 1910's by having it look up the rules and translate a text it had first translated from English to modern Norwegian, and it did just fine.
They will have seem some work in these dialects, but mostly it transfer really well to know related languages (English, Dutch, German, Swedish, Danish, roughly form a continuum from least in common to most in common with modern Norwegian; they all share vocabulary and significant parts of grammar with Norwegian), and then a relatively limited exposure to Norwegian itself is sufficient to do fairly well.
They're also really good at "style transfer" of text in the form of tweaking orthography, word order, and minor grammar changes from descriptions and examples.
(incidentally, the latter is one way of getting an LLM to sound a lot less like an LLM)
>As Husnes put it; Norway is a small country solving a problem every non-English-speaking nation will face: how do you build AI that reflects your language, your culture and your history? AI needs custodians, not just builders.
I'm afraid the answer is, mostly you don't.
Such a thing requires strong political will that, at least in my environment, seems basically impossible to align.
The costs are prohibitive, but beyond that, the type of person who cares about local representation like that is either completely fine with letting foreign companies implement it (after all, you can use ChatGPT in Basque if you want to) or is against the idea of AI altogether.
I guess it's subject to debate whether the cost indeed is prohibitive in the case of Norway. They are a small but extremely wealthy country - after all, they currently hold the equivalent of 1,5% of all the listed companies globally through the investments of their sovereign wealth fund.
I'm sure if Norway approached the American labs with goal of making a curated datasets for training, they would absolutely get in the training door, and those models would likely run circles around anything that could be domestically done.
That being said though, I can feel you cringing through the screen.
That's about 350MB per capita. Humans can produce 2-6kb per hour. That's 13 years of non-stop typing. Wonder where it all comes from. I guess it's websites that aren't compressed / extracted.
It's a legal deposit library, same as e.g. Library of Congress. Which means almost every published book, magazine, and newspaper and many other works published in Norway, as well as large collections of Norwegian works published abroad (such as thousands of Norwegian-language newspapers published by the Norwegian immigrant communities in the US) for many decades and a large proportion of the same from the last 200+ years are stored there.
They do also crawl websites (or at least did) in the .no tld.
5x 400gbit running to a 2U box whoa, the PCI lanes must have heat shielding.
More seriously there is a sensibility limit on extreme density where it's not needed. The idea that you're just going to magically get 2 TBit/s out of those ports seems unlikely even with tweaked software, and you're stuck with a power and comms hotspot that's liable to dictate the remainder of your network design.
At max utilisation that 2U would take 12 hours to drain, and only 12 hours assuming peak and likely unachievable throughput and the box otherwise being completely out of service. Not a great start
if you read the article 2pb is available as flash storage in the data pipeline, used to dedupe, clean, normalize, etc, for training from 60pb of raw data.
> He asserted that any country with its own language that did not have a sovereign LLM trained in that language was at a disadvantage as a globally trained, English-speaking LLM would not know about that country’s history, news and culture that was described in the local language.
I don’t know this is true. But whatever sounds true enough and gets funding seems to be what flies these days.
Can confirm that. Norway may have a small population, but if you live there you'll think it's truly the center of the world (aside from the US. Norwegians love America)
(a lot is only accessible from Norwegian IP addresses, so it's one of the main reasons I maintain a VPN as I'm Norwegian but live in the UK; a second set is only available from the IP addresses of libraries or research institutions - still huge amounts that are generally available, though)
I thought all big players already train on basically everything remotely available to them no matter the language or quality, so his take sounds like an opinion formed in the early days of generally available LLMs.
Training a sovereign LLM with this meager hardware as opposed to a LORA on some open source model seems like a huge mistake and a potential red flag.
There is no way these people have the resources to train a fully fledged LLM, so claiming that is their goal makes me think they don't intend for the LLM to be useful.
Which begs the question, whose money are they wasting - and why?
Even though it's nominally the national library behind this, they were probably chosen (as per the article) because they legally own and can use all NO material for this end. I'd guess researchers from related entities like unis will be involved in the process.
I don’t think they aim to anything worthwhile. The finetunes were incredibly broken. I’m guessing it’s more about having the method to do it. I’m not convinced it’s super useful but I’m not one to decide who gets to do what with the research funds.
One finetune I tried did make fun of humans expressing their feelings in the chat. Often.
One other finetune did hallucinate that it was a doctor and my baby had terrible diseases, every time I just wrote "hei" (with a generic neutral system prompt that likely triggered this behaviour though).
I think Olivia is big enough for what it’s used for. In my opinion it’s better to stay up to date and not waste too much money on hardware at the moment.
> they wasting - and why?
i18n language models are not area something frontier labs are focusing ton of resources on? ( certainly not in Norwegian)
The corpus of content in Norwegian - may not require very large clusters, or even if it does, this is best that the library could do, it would be certainly more than anyone else is investing in Norwegian models
SOTA models do not have the access to the quality of content that the national library does? The article mentions licensing with newspapers specifically, and the library has access to its own content archive.
English and Norwegian are not closely related language families, perhaps LoRA is not best approach?
I am curious if there is published research on how well localization works with LoRA depending on how far off the target language grammar/vocabulary is from English.
Projects like this typically have more than one objective and are not only building SOTA project, but is also to build/train foundational local talent , similar to universities launching satellites .
Yes, they are. English is a West Germanic language. Norwegian is a North Germanic language. The French vocabulary in English obscures it a bit, but the two languages have similar grammar and the vocabulary has a huge number of close cognates.
E.g. day -> dag, ship -> skip, apple -> eple, cow -> ku (which makes more sense when you pronounce them correctly out loud), bairn (child; mostly Scotland and Northern England) -> barn, hop -> hopp, yule -> jul just to give a random selection of English Germanic words.
But more than that, the frontier models both a) knows Norwegian quite well, b) certainly knowns German and Dutch well, and there's a continuum of language transfer around the North sea especially when accounting for sounds rather than modern orthography, e.g. to take a couple of examples from above: ship -> schip -> Schiff -> skib -> skip; day -> dag -> Tag -> dag). The "jump" to Dutch already weeds out most of the French. A lot of modern Norwegian orthography comes from Danish, which again shares more than modern Norwegian does with German.
Knowing any of these helps a lot with learning Norwegian and vice versa. E.g. I'm Norwegian, I've never learnt Dutch, but I have learnt English and German, and I can read Dutch fairly well from that alone.
They have already done experiments with dittrent sub 10b models with both fine-tuning and fully from scratch. And last I check the fully from scratch captured the language in a better way.
Also the line between “finetuning a base model” and “man this is a real good initialization” gets pretty blurry at scale.
Altogether a pretty presumptuous take.
What do you suggest, that they stop and wait until they have the right HW?
"Norway's sovereign wealth fund, officially known as the Government Pension Fund Global, is the world's largest sovereign wealth fund with assets exceeding \(\$2\) trillion. Established in 1990 and managed by Norges Bank Investment Management, it was created to channel surplus petroleum revenues into long-term global investments to benefit future generations."
Qwen was made on a cluster about that size.
And this is before anybody ever thought about optimizing the training process. (Currently it's just pytorch analyst-as-coder slop, with extremely overprovisioned quantizations, etc.)
Seems like making the frontier models know Norwegian and their culture is a better (or additional!) way to reach the end they are going for here.
E.g. I had Claude describe the novel "De knyttede næver" from 1911 in Norwegian orthography ca. 1911, as it's a novel I've read, and it does a good job.
What it lacks is an understanding of Norwegian literature, culture and history. It had to look up "De knyttede næver", which was one of the best-selling Norwegian novels of its time before I'd get anything out of it (ChatGPT does better; in thinking mode in particular it gives a detailed summary).
While not exactly well known today, the author was a prominent newspaper journalist for decades, and the novel series is well enough known that e.g. there's a Norwegian singer that took his stage name after the protagonist, and it was covered in Norwegian papers and books for decades (partly because of controversy over the authors political views and how they coloured his novels), so it does feel like a reasonable test that reveals a quite significant knowledge gap.
I do agree with you that it'd be better if the data set from the national library was made more accessible, though it seems a major addition here is that they have a deal to train on copyrighted data locked away in their archives that they have limitations on the use of.
But even just making the out of copyright data in their collections would be a great start.
If you go with HDD arrays probably $50k
That said, they are quite limited in what they are allowed to share of in-copyright works, and nb.no is a fantastic resource as it is (though you'll need a Norwegian IP address for too much of it - it's one of th main reasons I maintain a VPN) - if they are allowed to make it accessible there, it'd be great.
But they also have vast amounts of out-of-copyright data that I hope they'd make more easily accessible...
LLMs are not great at preserving cultural uniqueness and diversity. Take how “delve” has reentered the lexicon because the human assessors for pre training dialect of English uses “delve” a lot.
There is a lot of benefits to training specifically for a unique culture with unique norms to preserve the culture as we increasingly rely on LLMs.
https://www.scientificamerican.com/article/chatgpt-is-changi...
Both Claude and ChatGPT can translate into minor dialects of Norwegian they will have seen very few works in because very few printed works exist in them.
E.g. I've tested both my local spoken dialect, which is rarely written, and a sociolect used by a 1970's Maoist group consiting of a few hundred people, where most of the printed material consists of novels from a couple of ex-members that became authors.
In the latter case, it claimed to not know, but was able to get a good match from just a description.
I also just had it ape Norwegian orthography from the 1910's by having it look up the rules and translate a text it had first translated from English to modern Norwegian, and it did just fine.
They will have seem some work in these dialects, but mostly it transfer really well to know related languages (English, Dutch, German, Swedish, Danish, roughly form a continuum from least in common to most in common with modern Norwegian; they all share vocabulary and significant parts of grammar with Norwegian), and then a relatively limited exposure to Norwegian itself is sufficient to do fairly well.
They're also really good at "style transfer" of text in the form of tweaking orthography, word order, and minor grammar changes from descriptions and examples.
(incidentally, the latter is one way of getting an LLM to sound a lot less like an LLM)
I'm afraid the answer is, mostly you don't.
Such a thing requires strong political will that, at least in my environment, seems basically impossible to align.
The costs are prohibitive, but beyond that, the type of person who cares about local representation like that is either completely fine with letting foreign companies implement it (after all, you can use ChatGPT in Basque if you want to) or is against the idea of AI altogether.
That being said though, I can feel you cringing through the screen.
They do also crawl websites (or at least did) in the .no tld.
Dell just launched a 2U that fits almost 10 petabytes in it. It's probably not 384 core capable but that is very doable right now, Epyc chips are 192 cores each! https://www.techradar.com/pro/dell-launches-record-shatterin...
More seriously there is a sensibility limit on extreme density where it's not needed. The idea that you're just going to magically get 2 TBit/s out of those ports seems unlikely even with tweaked software, and you're stuck with a power and comms hotspot that's liable to dictate the remainder of your network design.
At max utilisation that 2U would take 12 hours to drain, and only 12 hours assuming peak and likely unachievable throughput and the box otherwise being completely out of service. Not a great start
It's still a weird article, to highlight a "big" storage appliance. Having all that NVMe local feels like it would be much much much much faster.
I don’t know this is true. But whatever sounds true enough and gets funding seems to be what flies these days.