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Magistral — the first reasoning model by Mistral AI

mistral.ai

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Re: Magistral — the first reasoning model by Mistral AI

#281

Post by kergonath »

melicerte wrote: post #35
pu_pe wrote: post #11

Benchmarks suggest this model loses to Deepseek-R1 in every one-shot comparison. Considering they were likely not even pitting it against the newer R1 version (no mention of that in the article) and at more than double the cost, this looks like the best AI company in the EU is struggling to keep up with the state-of-the-art.

If you look at Mistral investors[0], you will quickly understand that Mistral is far from being European. My understanding is it is mainly owned by US companies with a few other companies from EU and other places in the world. [0] https://tracxn.com/d/companies/mistral-ai/__SLZq7rzxLYqqA97j... (edited for typo)

It’s a French company, subject to French laws and European regulations. That’s what matters, from a user point of view.

Re: Magistral — the first reasoning model by Mistral AI

#282

Post by msgodel »

ambicapter wrote: post #252
msgodel wrote: post #183

Earlier quoted context omitted.

There's nothing the regulation could meaningfully hope to accomplish other than slow down people willing to play by the rules.

Wow, the "criminals don't follow laws therefore laws are worthless" argument, here? In my HN?

Usually it's possible to actually detect crime (in fact it's usually hard to ignore.) That's not the case with AI.

Re: Magistral — the first reasoning model by Mistral AI

#283

Post by asadotzler »

ACCount36 wrote: post #230
louiskottmann wrote: post #215

Earlier quoted context omitted.

Indeed, and with the technology plateau-ing, being 6-12 months late with less debt is just long term thinking. Also, Europe being in the race is a big deal for consumers.

>with the technology plateau-ing People were claiming that since year 2022. Where's the plateau?

The pre-training plateau is real. Nearly all the improvements since then have been around fine tuning and reinforcement learning, which can only get you so far. Without continued scaling in the base models, the hope of AGI is dead. You cannot reach AGI without making the pre-training model itself a whole lot better, with more or better data, both of which are in short supply.

Re: Magistral — the first reasoning model by Mistral AI

#284

Post by whodidntante »

atemerev wrote: post #47
cpldcpu wrote: post #36

Earlier quoted context omitted.

Sorry, this is just getting old... Its a trite talking point and not the reason why there are so few consumer-AI companies in Europe.

And what would be the reason? I am genuinely interested. Also, are there viable not "consumer" AI companies here? Only Mistral seems to train foundation models, and good for them, however, as of now they are absolutely not SOTA.

I can only talk about my personal (US based) experience. This includes many US based startups,some VC startups, senior leadership in a large tech company, and a senior executive position in another large tech company. I have also worked with, and built, tech organizations in multiple EU countries, and have been involved in the technical due diligence and acquisition discussions with several EU companies. I admit that my experience is about 6 years old, as I am no longer in the tech industry, and I do not know what has changed during this time.

Money: There is more money for US startups. Investors (US and EU) want to invest in US based startups, not EU startups. US investors are willing to risk more money and take greater risk. EU startups that gain traction will attract US companies in that they provide a good way to extend their market to the EU, not as much for their innovations. Tech entrepreneurs (US or EU) want to work in the US if they can, because that is where the excitement and risk taking is and where the money can be made.

Teams: Building and managing EU tech teams is very different than US tech teams. EU teams need a lot more emotional hand holding, and EU engineers are far more salary oriented than equity oriented. It is far more difficult to motivate them to go above and beyond - the "we need to get this fix or feature in tonight so we can deploy n the morning" simply will not get done if it is already 5pm. Firing EU workers is much more difficult. There are a lot more regulations for EU teams, in order to "protect" them, and that results in the teams being more "lifestyle" teams rather than "innovation teams". EU teams get paid a lot less than their US counterparts.

Failure: Good failure is not a problem in the US, it can actually be a badge of honor. EU is very risk averse, and people avoid failure.

There are of course exceptions all around, but the weight of these observations and experiences are in favor of US teams.

This is in no way saying it is better to live in the US, there are a lot of things about the EU that are more attractive than the US, and I would probably have a better lifestyle living in Europe now that I am no longer working. But innovation and money is not one of them.

Re: Magistral — the first reasoning model by Mistral AI

#285

Post by danielhanchen »

gyrovagueGeist wrote: post #162
danielhanchen wrote: post #29

Earlier quoted context omitted.

Their paper https://mistral.ai/static/research/magistral.pdf is also cool! They edited GRPO via: 1. Removed KL Divergence 2. Normalize by total length (Dr. GRPO style) 3. Minibatch normalization for advantages 4. Relaxing trust region

Does anyone know why they added minibatch advantage normalization (or when it can be useful)? The paper they cite "What matters in on-policy RL" claims it does not lead to much difference on their suite of test problems, and (mean-of-minibatch)-normalization doesn't seem theoretically motivated for convergence to the optimal policy?

Tbh I'm unsure as well I took a skim of the paper so if I find anything I'll post it here!

Re: Magistral — the first reasoning model by Mistral AI

#286

Post by danielhanchen »

monkmartinez wrote: post #121
danielhanchen wrote: post #3

I made some GGUFs for those interested in running them at https://huggingface.co/unsloth/Magistral-Small-2506-GGUF ollama run hf.co/unsloth/Magistral-Small-2506-GGUF:UD-Q4_K_XL or ./llama.cpp/llama-cli -hf unsloth/Magistral-Small-2506-GGUF:UD-Q4_K_XL --jinja --temp 0.7 --top-k -1 --top-p 0.95 -ngl 99 Please use --jinja for llama.cpp and use temperature = 0.7, top-p 0.95! Also best to increase Ollama's context length…

At the risk of dating myself; Unsloth is the Bomb-dot-com!!! I use your models all the time and they just work. Thank you!!! What does llama.cpp normally use if not "jinja" for their templates?

Oh thanks! Yes I was gonna bring it up to them! Imo if there is a chat template, by default it should be --jinja

Re: Magistral — the first reasoning model by Mistral AI

#287

Post by thot_experiment »

mandelken wrote: post #250
thot_experiment wrote: post #209

Earlier quoted context omitted.

I use it as a personal assistant (so tool use integrated into calendar/todo/notes etc) often times using the multimodal aspect (taking a photo of a todo list, asking it to remind me to buy something from a picture). I also use it as a code completion tool in vscode, as well as a replacement for most basic google searches ("how does this syntax work", "what's the torch method for X") I use it for almost every interact…

Cool. What framework or program do you use to orchestrate this?

Me, Mistral and Claude writing modules on top of a homebrew assistant framework in node with a web frontend. I started out mostly handwriting the first couple modules and the framework for it. (todo and a time tracker) and now the AI is getting pretty good at replicating the patterns I like using, esp with some prompt engineering as long as I don't ask for entire architectures but just prod it along. It's just so easy to make the exact thing you want now. All the heavy lifting is done by ollama and the node/browser APIs.

The only dependency on the node side is 'mime' which is just a dict of mime types, data lives inside node's new `node:sqlite` everything on the front side that isn't just vanilla is alpine. It runs on my main desktop and has filesystem access (which doesn't yet do anything useful really) but the advantage here is that since I've written (well at least read) all of the code I can put a very high level of trust into my interactions.

Re: Magistral — the first reasoning model by Mistral AI

#288

Post by LordDragonfang »

bobsomers wrote: post #276
LordDragonfang wrote: post #269

Earlier quoted context omitted.

"Thinking" is a term of art referring to the hidden/internal output of "reasoning" models where they output "chain of thought" before giving an answer[1]. This technique and name stem from the early observation that LLMs do better when explicitly told to "think step by step"[2]. Hope that helps clarify things for you for future constructive discussion. [1] https://arxiv.org/html/2410.10630v1 [2] https://arxiv.org/pdf…

We are aware of the term of art. The point that was trying to be made, which I agree with, is that anthropomorphizing a statistical model isn’t actually helpful. It only serves to confuse laypersons into assuming these models are capable of a lot more than they really are. That’s perfect if you’re a salesperson trying to dump your bad AI startup onto the public with an IPO, but unhelpful for pretty much any other rea…

If that was their point, it would have been more constructive to actually make it.

To your point, it's only anthropomorphization if you make the anthrocentric assumption that "thinking" refers to something that only humans can do.[1]

And I don't think it confuses laypeople, when literally telling it to "think" achieves the very similar results as in humans - it produces output that someone provided it out-of-context would easily identify as "thinking out loud", and improves the accuracy of results like how... thinking does.

The best mental model of RLHF'd LLMs that I've seen is that they are statistical models "simulating"[1] how a human-like character would respond to a given natural-language input. To calculate the statistically "most likely" answer that an intelligent creature would give to a non-trivial question, with any sort of accuracy, you need emergent effects which look an awful like like a (low fidelity) simulation of intelligence. This includes simulating "thought". (And the distinction between "simulating thinking" and "thinking" is a distinction without a difference given enough accuracy)

I'm curious as to what "capabilities" you think the layperson is misled about, because if anything they tend to exceed layperson understanding IME. And I'm curious what mental model you have of LLMs that provides more "true understanding" of how a statistical model can generate answers that appear nowhere in its training.

[1] It also begs the question of whether there exists a clear and narrow definition of what "thinking" is that everyone can agree on. I suspect if you ask five philosophers you'll get six different answers, as the saying goes.

[2] https://www.astralcodexten.com/p/janus-simulators

Re: Magistral — the first reasoning model by Mistral AI

#290

Post by viraptor »

alister wrote: post #227

As a quick test of logical reasoning and basic Wikipedia-level knowledge, I asked Mistral AI the following question: A Brazilian citizen is flying from Sao Paulo to Paris, with a connection in Lisbon. Does he need to clear immigration in Lisbon or in Paris or in both cities or in neither city? Mistral AI said that "immigration control will only be cleared in Paris," which I think is wrong. After I pointed it to the W…

It looks like you're not sure of the answer yourself. That's really not a good way to test the LLMs. You'll just prefer the one that convinces you the most rather than the correct one.
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