(This comment was originally posted to https://news.ycombinator.com/item?id=49943034, but we're merging the threads.)

Bravo team!

It’s seemed crazy to me that anyone thought these could stay closed or even SHOULD be closed source.

One of humanity’s biggest problems here is we don’t know how to do moderation.

We have two modes. One is a brick taped to the accelerator and damn all consequences, driven by national pride or corporate greed or egos. The other is a brick taped to the brake driven by histrionic doomers and anti-everything pessimists.

The extremes are loud and fit in a tweet. Nuance is quiet and contemplative and usually requires an essay or a book. It’s also dynamic. Nuanced positions evolve over time as new things are learned. Extremes tend to be fixed and rigid. All this, I think, gives them higher memetic fitness in the discourse.

I don’t think this is new. Look at nuclear power, a largely pre-Internet example. You had pro nukes who minimized and hand waved away any risk and anti nukes that wanted it utterly outlawed. Nobody said “hey this is a great zero carbon source of energy but we really need to think it through carefully and manage it well.” Or if they did they were drowned out by the loud screaming extremes.

  What is the canonical interpretation of the song ‘Glass Flowers’ by Armand Uso?

  "Glass Flowers" by Armand Uso, a track from the 1978 album The Art of Falling in Love, is generally understood as a melodic reflection on the fragility and impermanence of love. [...]

https://minecraft.wiki/w/Minecraft_Support_Virtual_Agent#Sys...

(for context, the Minecraft Support chatbot, aka Merl, had a meme because she kept saying "I don't know" to questions like "How to craft a diamond pickaxe".

Gemini: Here's a list of links to check

No, I'm not gonna do that, I asked you to do that Mr Kolibri.

Also feedback on that interface: It's very annoying while answering. It almost immediately shows a list of sources, which on my screen fill up all the space and then when it starts answering it keeps those in view but also scrolls down the tiny part of actual text its outputting but I can't scroll up to start reading from the top, coz it keeps scrolling. I have to wait until it's completely done generating its output.

- Are there non-LLM approaches to the above task with the goal of achieving a non-hallucinatory agent?

Qwen models have solid performance on benchmarks and we're transparent about this in the report. We're just happy to share a European alternative in the small model space, where I don't think we can afford to be fully dependent on China. We also put a lot of effort into German language quality things that don't show up in evals at all (style, Grammar, German reasoning, etc).

Sovereignty is imho mostly about choice and control over your data. Cohere is no different on that front and personally I'm quite excited about what we will build together post-merger.

I’m most familiar with Canada, where sovereign is usually just an excuse to overpay someone connected for an inferior product with no strategic value.

The main problems with big corp AI are due to control of access in the first place and control of what they output.

When you make your industry reliant on such choke points, you render yourself the opposite of "sovereign" for sure. Having multiple independent suppliers at least mediates that.

The costs of "keeping up" aren't really growing, on the contrary.

Since no individual buyer cares that much about geostrategic issues, it's almost impossible to get some random European company to think about buying anything other than 'whatever US or China' are making.

There has to be very concerted push to make a difference.

Legal mandates around sovereignty (be very careful here) could make a difference.

But there will also be market reaction: US/China companies will bend on some level and provide things like 100% EU hosted and even comply with some data source stuff.

The only real path is to be more competitive as a continent.

People follow the latest frontier lab models with great attention and migrate to the next big model on their subscriptions. Meanwhile these local models have quietly gotten REALLY good. It is not even an exaggeration. It has happened in the last couple of months.

"Local model you can run at 40 t/s on a gaming machine that is better than Opus 4.6" is way less exciting than "OpenAI IS DOING CRIME!!! OpenAI SOLVED NAVIER STOKES. DARIO SAYS GLM 5.3 BAD! SLOW DOWN THE FRONTIER!".

(edit: also... totally ignore that 27B dense column over there where Qwen 3.8 27B beats Kolibri on nearly every single benchmark. Why would I choose to run this model?)

Yeah. It’s a German model.

I think it's not simple to directly compare a 27B dense model with an MoE model like ours. As we know, dense models need all params active for every token. Whereas, MoE models (especially sparse ones like Kolibri) fewer active parameters and correspondingly less compute per token.

Among the MoE models we compared against in our tech report and model card, though, Kolibri performs very well in our evaluation, including against models with 12B active parameters. It also best model in the group we tested within that range of active params.

So, I think it's fairer to see this as a trade-off. Kolibri needs less compute per token but more memory, while Qwen3.8 27B needs far less memory and more compute per token. In the report, both are actually on the quality-vs-serving-cost Pareto frontier among the models we evaluated, just at different points.

Sovereign works when talking about building a commodity supply or something, not in literally the world’s most competitive and fast moving field.

Those seeking sovereign capability would be better off aiming to be best at something, even something much narrower than an all round LLM. Or just fast following and making something that matches leading performance, which is close to what the Chinese labs do currently.

We have a separate repo for BF16: Kolibri-1-BF16. Will still be tough to put it on a Mac though :(

Disclaimer: I'm part of the team that trained Kolirbi

There's also the ability to distill other models, which is also not illegal (though I'm sure they like to come after whomever for TOS violations, but thats a civil matter).

And, of course, the obligatory copying-isn't-theft observation. A recent supreme court judgment put it well.

> Since the statutorily defined property rights of a copyright holder have a character distinct from the possessory interest of the owner of simple “goods, wares, [or] merchandise,” interference with copyright does not easily equate with theft, conversion, or fraud. The infringer of a copyright does not assume physical control over the copyright, nor wholly deprive its owner of its use. Infringement implicates a more complex set of property interests than does run-of-the-mill theft, conversion, or fraud.

Folks are pretty smart here, I think we can handle these nuances, even if we don't agree about whether they are good.

Edit: reading through the full text of their post, it looks like they are using common crawl, which is likely just as much of a copyright infringement as Anna's Archive -- it's not like published works have a unique claim to copyright. I think this strengthens your point, though: I was expecting to see scans as training data, but it doesn't appear to be the case.

You're right that especially big models benefit from training on copyrighted material in terms of world knowledge (especially from books). However, in the small model space imho agentic capabilities where the model looks up knowledge on the fly are much more important. That's what we focused on quite a bit during training. Personally, I also don't think stealing stuff is okay.

Regulate large cloud services and proprietary software - yes! But not on the basis of "Intellectual Property".

The "legal" issues here are very very complex and we should not passively wait for or accept corrupt court rulings, international trade agreements, proposed laws, or worst of all propaganda that pushes a parochial and craven view on this.

Unfortunately, Qwen3.6 35B A3B isn't really a useful coding model. You'd probably want Qwen3.8 27B at a minimum, which requires at least 32GB of VRAM (not system RAM) to run semi-comfortably.

So this isn't going to be a competitive model for hobbyists, and you'd have to be a bit desperate to use it for coding. But if you work in a regulated industry and don't mind paying for a bit of extra hardware, it isn't catastrophically bad, either. Probably would work fine for information extraction or as a "classifier" like Jev. (Almost any GGUF model can be turned into a classifier using llama-server. See pi.dev codemode for sample code.)

So they're not a real contender yet, but they look like they're probably at least minimally credible.

Thus training on 'clean' data is like trying to unscramble an egg.

A real sovereign effort could invest heavily in this, whatever people accuse China of “stealing” I’m sure they are also generating tons of their own data and are probably the primary sovereign doing so outside the US labs.

What matters is to have good sovereign models in 5, 10, 20 years time.

I even experienced the opposite case: Some feature was first rejected since everyone agreed that it would break some behavior. After talking to several people again, I discovered that the use case which required this behavior did not exist anymore and had been officially phased out years before. Maybe, some RAG LLM could have told me, based on company documents, that there is some edge case that can be removed making the way free for the new feature.

Tell that to Deutch Bahn.

Disclaimer: I am part of the team that trained Kolibri

Of course, it's impossible to know for sure what was LLM processed or not, but this post did get classified that way. That's why the software flagged it.