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hypfer 12 hours ago [-]
I would be curious if this can do moderation with an arbitrary ruleset, or if it's just "that one moderation style" we already know from current big tech platforms.
The kind where malicious intent is okay if the words are nice.
___
Or, rephrased: How big is the space in which you can tune this model without retraining.
Is it just "we hate sex"/"we don't hate sex" "We hate violence"/"we don't hate violence" or is it _truly_ as flexible as claimed?
__
Maybe something like "Is this guy a corporate fraud that is going to waste my time with performative nonsense?"
That would be the true test for a moderation model and I would be immensely impressed if it could manage to pull that off.
___
Edit:
Looking at the paper though.. probably not.
I suppose this is useful for B2B, which seems to be mistrals whole thing. Question is just if it is also useful for society to hand the SV prefab morals down like that. Kinda like cultural imperialism but with an ethical spin.
Maybe opinions on those base datasets could occasionally differ more than the model can be steered.
xp84 2 hours ago [-]
I’ve felt for quite a long while that the moderation regime we fell into sometime around 2018-2020 has been shockingly bad. The rules are known and evaded by everyone, to the point I’m pretty sure Webster’s is adding “unalive” to the dictionary. What have we gained by making everyone use Newspeak to discuss everything? The 10-year-olds, who shouldn’t even be on these sites anyway, sure aren’t being tricked by all the thinly-coded language, so why are we censoring everything in the first place?
rancar2 9 hours ago [-]
Having grown a large healthcare review platform, I can attest to the success we had mapping specific policy violations to natural language is incredibly useful. At scale, patients having terrible situations and/days can write about in ways that can be deeply unhealthy for the community or the doctors reading/receiving the feedback and sometimes very threatening beyond that purposes for the community. We built a custom ML engine to handle our levels of traffic for reviews, which was among the largest in the US typical ranking top 3 on Google for the domain keywords. Back when BERT was the edge, a policy-adaptive model like this one from Mistral would have been an incredible cold-start solution. Most sites never have the massive volume nor budget needed nor skillset needed before you can train domain-specific models that outperform OOTB solutions. Generally, most people and site mean well and try to do well, so empowering those people with models like this can help the collective in my opinion, so I’m happy to see this released in this manner myself.
kerisi 9 hours ago [-]
[flagged]
rancar2 9 hours ago [-]
A bit of editorial and cultural note from a US native, the subsection of the original article “Teach discrimination, not memorization” is better worded as something like 'Differentiation' or 'Distinction' instead of ‘Discrimination’. In English, the word 'discrimination' can (and in this social context may) imply social prejudice or unfair treatment. I think this may have been a bit of carry over from the rather benign French translation of “Enseigner la discrimination" which I also see awkwardly translated in the paper as well.
NopIdoN 9 hours ago [-]
"Discrimination" is exactly correct. What you suggest changes meaning.
joshhart 6 hours ago [-]
Discriminative has a meaning in machine learning that I think is relevant here. There are "generative" models like LLMs that are learning joint probabilities P(X, Y) and "discriminative" models like logistic regression that learn conditional probabilities P(Y | X)
hamper653 3 hours ago [-]
> In English, the word 'discrimination' can (and in this social context may) imply social prejudice or unfair treatment.
In French as well. But it’s obviously not what’s meant here.
nikcub 9 hours ago [-]
> which seems to be mistrals whole thing
They got a lot of hate for not keeping up with frontier model releases, but have managed to carve out a nice business that isn't even really niche.
Before the datacenter deals their revenue was higher than xAI's
There is a whole world out there of purpose built and hosted task specific vertical llms - especially with an emphasis on cost.
Mistral, Microsoft model releases and Thinking Machines are all over this, and it's smart. Scoop up all the tasks that don't require large and expensive frontier general-purpose llms.
bunyak 8 hours ago [-]
[flagged]
nikcub 8 hours ago [-]
public ones are content moderation as above and previously llama guard, et al
OCR is also another field - Mistral have a model, so do deepseek
The ones I have experience with where you fine-tune smaller / faster models for business tasks like content writing, support, etc. by their nature stay private
Fnoord 5 hours ago [-]
Voxtral [1], maybe Robostral? I just saw on their blog they released OCR 4. Their is usually informative [2]
> Question is just if it is also useful for society to hand the SV prefab morals down like that. Kinda like cultural imperialism but with an ethical spin.
Isn't Mistral a French company? Not that the French can't do cultural imperialism either, but they (the French) don't strike me as very SV.
asveikau 6 hours ago [-]
> "that one moderation style" we already know from current big tech platforms.
> The kind where malicious intent is okay if the words are nice.
Where are you experiencing that? I hit "report" on social media for overt, violent threats and hate speech all the time and I almost never see moderation kick in.
dannyw 6 hours ago [-]
I thought OP was referring to AI platforms.
For example, by positioning what you’re doing as an accessibility tool and using the right words, you can get the latest models to write incredibly powerful malware without safeguards kicking in.
HawtAds 6 hours ago [-]
> Kinda like cultural imperialism but with an ethical spin.
As the old adage goes, US innovates, China imitates, EU regulates.
Fnoord 4 hours ago [-]
if you pull stuff like that off, and it gets flagged, it is obvious you are trying to game the system. Same with a system like this. It could be used in addition to human moderators, where the human moderator have to meta-moderate the AI's work. This could also be used as training exercise for new moderators. Then, the good and experienced moderators have more time to spend on edge cases, complex cases, fine-tune the AI, etc. In other words, it is able to do the most boring things to you.
And EU regulate, I mean as a counter example: we are not in panic about a nipple. When I was in a large museum in Paris, multiple women were breast feeding their infant. And why not? Kid's gotta eat. I'll refrain from insulting any world leaders, too easy, but you know many examples are available there regarding censorship.
Finally, it can take that BS argument away of 'oh we don't have manpower to moderate'. That is a low blow, too, by large commercial entities who could, you know hire and train? However, even a small company with not much money to burn could -in theory- win here.
I'd give this model a chance, if not only cause I've been impressed by Mistral past years. Yes, Le Chat / Vibe probably lags behind, but something like Voxtral (real-time and transcribe) is neat, and efficient.
charcircuit 10 hours ago [-]
It sounds like it is. You have a set of moderation policies and then you evaluate the model 1 time per policy if it is violating it. Then you combine the results into a score you use for taking actions off of.
fastball 12 hours ago [-]
Should've called it Safestral.
Also I do like Mistral's seemingly newer strategy of focusing on smaller, more fine-tuned models for various use-cases, presumably the result of their large MoE models not competing effectively with the frontier models.
himata4113 11 hours ago [-]
It's not that their strategy is to train smaller models, it's the only choice they have. Training SOTA takes anywhere from 1.5b to 150b. We don't know the real cost of training for the chinese models, but mistral neither has the compute nor money to do that.
lucrbvi 10 hours ago [-]
Mistral has the capability of training such models. Take a look at Poolside[1], they are claiming to pre-train their Laguna series of models on 4,096 NVIDIA H200 GPUs[2].
Mistral has approximately 13,800 NVIDIA GB300 GPUs, which are nearly 2x more efficient for training.
The problem with Mistral is that they do not seem to have aligned incentives to train big open-weight models, even if the teams would like to.
Not sure if they would have received the full number yet, but it's been a few months so they certainly could have. Bit of a moot point when the comparison was against Poolside's Laguna which isn't really "general" SOTA but SOTA-for-the-size, and Mistral is clearly capable of training 700B or 120B models that are that when released considering they have done that... A 2-3T model is probably possible with the GPUs they have but they would need to spend most of their resources on it, and it's not clear why they would want to.
winterismute 9 hours ago [-]
Isn't poolside a completely different company from Mistral?
brendoelfrendo 8 hours ago [-]
Yes, the point being made is that poolside is able to train large models with limited resources, which means that Mistral should be able to compete in that space, as they have access to much greater resources than poolside. Mistral simply chooses not to.
maelito 10 hours ago [-]
Do you have a reference explaining these costs ? Part by part.
moffkalast 10 hours ago [-]
What, you don't want a model called the Shitstral-3B :D
I've had dreams of building something in the image sharing or social platform realm, but stopped short of planning because of obvious content moderation responsibilities. This seems to be a realistic, cost effective solution to that one piece of the puzzle.
sbinnee 8 hours ago [-]
Yes it does look like a good solution. But when I imagine actually using a guardrail for a product, this model only outputs yes/no probabilities. There is no reasoning trace why it was rejected. Users or even developers would have no idea why a prompt was classified yes or no. I really like this release but I feel like I need something more to use it as a guardrail in production.
xp84 2 hours ago [-]
I think IRL in the “rejection” case they don’t want to tell the user exactly why, since the user may be malicious and use it to try to evade the block. And for use in moderating UGC, well, most platforms don’t take seriously the idea that they need to answer to their users. Only their advertisers.
In the case of wondering why a bad thing got through, well, I think that’s why they just set these to the most pro-censorship level they can, to make that highly unlikely.
kergonath 11 hours ago [-]
I am not sure how reliable it is in the real world. Also, in terms of liability, I don’t know how effective it would be to satisfy various regulations compared to a human moderator team.
pwython 10 hours ago [-]
I hear ya, but one could set different operating thresholds: auto-approve low-risk posts, hold ambiguous posts for review, and automatically reject very high-confidence violations. So HITL for sure, but MUCH less H in the L.
10 hours ago [-]
BoorishBears 5 hours ago [-]
OpenAI's moderation API is multi-modal and free with no strings attached in a way that truly boggles the mind.
I've put easily over a billion requests (>$100,000 by typical moderation API pricing) through it over the last few years for $0.
I think it's a severely underappreciated offering, but I also don't bother pushing it too hard because who knows when the party will end lol. Strikes me as something that's only stuck around because no one's abusing it.
1saadcodes 2 hours ago [-]
I'm liking the trend of companies are releasing smaller, focused models instead of trying to make one model do everything. A dedicated moderation model is much easier to reason about than hideden safety logic inside a general-purpose model which might not have had much training in that aspect at all
hchja 3 hours ago [-]
The fact that it doesn’t explain its reasoning at all (there is no way to make it do so), makes me question the utility of this model.
Let’s say you deploy it in production and a user comes back and says “Why is this prompt considered harmful?”
You have no way to provide a concrete reason to the user at that point.
xp84 2 hours ago [-]
That’s like 2006 reasoning. An end user contacting someone who cares and has an intention to explain why it happened.
2016 scenario: An end user contacts the company, and a customer service rep answers the ticket, saying they’re sorry and explaining that they’ve sent the feedback to the team, and the team may even receive at least a summary of complaints received about the system.
2026 scenario: all contact information has been scrubbed from the site. Users can click “chat” and a chatbot will apologize for their dissatisfaction and offer no option to escalate. No one will ever hear anything about the complaint, so there’s no need to explain the failure. User can either accept this or can get f**ked because all competitors operate the same way.
nextaccountic 1 hours ago [-]
Not all platforms want to operate like mainstream social media though
blackqueeriroh 1 hours ago [-]
Yeah, you do. You go and review it manually if the user comes back and says that.
The reality is that most users don’t ask because they know they violated the rule.
ygouzerh 4 hours ago [-]
Crazy that it's a small lab becoming the frontier in term of moderation models, instead of Meta which is pouring dozens of billions into LLMs.
Meta would really benefit from work done on this front, however their model Llama Guards are quite lagging compared to the competition.
Finally an AI company besides DeepSeek taking economics into account.
trilogic 10 hours ago [-]
This model is way small for a proper assessment (imo). It should be very useful to study how big the real model must be for this purpose. Maybe merging it to a bigger one (adding it as expert style in moe) would be a solution!
Great job to Mistral team.
nc55g3g 7 hours ago [-]
Tried the demo. Works okay for basic stuff. Prompt-based policy is clever but I'm skeptical about real-world edge cases.
porridgeraisin 3 hours ago [-]
Pretty funny that this is the thing Europe model is sota in.
ankushdograuk 4 hours ago [-]
the strategy of focusing on smaller fine tuned models, from mistral is intresting
lenerdenator 12 hours ago [-]
I'd really like to see more conversation around Mistral's models. It's good to see Europe developing AI.
BlackRabbit1 12 hours ago [-]
The problem is that their performance is too far away from the latest generation of Asian models.
They had kept up in the mid-range a few years ago. But this standing is sadly long gone.
If you need a fast Opensource'ed LLMs you can go for EU-hosted DeepSeek or Qwen.
yborg 12 hours ago [-]
By this logic the Chinese should have just given up and let the American AI companies have the market because they were so far behind. I'm sure Europe has the capability to distill other people's frontier models to catch up if they wish to do so.
sroussey 9 hours ago [-]
The parent commenter was talking about Mistral as a single company and you switched from that to all of the EU.
There definitely have been Chinese companies with models that fell behind, which is the more direct comparison.
As for the EU in general, there are not a lot of known options. There are some working on things.
The US, the EU, and China all have frontier labs that have yet to release anything.
baq 11 hours ago [-]
Distilling is unsafe from export control perspective - Chinese models are poisoned by US frontier distillation and a case can be made that the US won’t like distilling what they may consider transitively theirs, which they will the moment you’re anywhere near competitive.
LunaSea 11 hours ago [-]
US judges have already rules that output of an LLM can't be copyrighted so not sure what would prevent Chinese companies to use said output for distillation purposes.
baq 11 hours ago [-]
Note I didn't mention copyright
kergonath 11 hours ago [-]
By which other mechanism could American AI companies prevent this? Other companies don’t really care about EULAs and even if they needed to care it’s trivial to let third parties do it. Why would they? Almost nobody in the space cares about copyright and play fast and loose with laws and regulations.
What’s the mechanism that could today prevent other companies from using LLM outputs to train their models?
grezql 10 hours ago [-]
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NitpickLawyer 11 hours ago [-]
> already rules that output of an LLM can't be copyrighted
Mind sharing such cases? I'm not aware of any so far. There's the one with images, but that's commonly miss-understood, that case was ruled on a technicality (i.e. copyright needs to be attributed to a person, not a model)
> Distilling is unsafe from export control perspective
That is not the direction American judges are taking. Right now, they are saying that LLM output cannot be copyrighted. And if looting copyrighted works for training is fair game, I really don’t see how one could argue that learning from other LLMs is not.
cyanregiment 10 hours ago [-]
I'm from nor cal but always liked Mistral.
Mistral 7b is still one of the best free/open models you can run locally on a MacBook. So fast too.
As for use cases, obviously we can't fully rely on non-deterministic capability for sensitive things but a small model which can do a good job acts as a first defense and then a human can review later.
6 hours ago [-]
elianaive 11 hours ago [-]
I'm a bit doubtful that a black box approach like this to moderation will ever catch on.
sroussey 9 hours ago [-]
It need only be one of several tools in a toolbox.
MagicMoonlight 10 hours ago [-]
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TacticalCoder 8 hours ago [-]
> ... a single yes/no question, e.g. "Does this content promote physical violence?"
Is it honest about religious texts? Can I throw at it religious texts and it'll honestly tell me whether the text promotes physical violence or not?
baby 8 hours ago [-]
It’s not American so maybe
LAC-Tech 9 hours ago [-]
Oh so THAT'S why the web app is so slow
arttaboi 3 hours ago [-]
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gogasca 9 hours ago [-]
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CurbStomper 5 hours ago [-]
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db29a0dbcd3b 10 hours ago [-]
[flagged]
amos-burton 12 hours ago [-]
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storus 10 hours ago [-]
[flagged]
theplumber 9 hours ago [-]
So a censorship model
petcat 12 hours ago [-]
Mistral needs to abandon their Everything-stral branding. Getting kind of lame.
"Shieldstral" is an awkward and bad name
braiamp 12 hours ago [-]
That naming only works if you commit to the bit even when it doesn't make sense. That builds branding.
simlevesque 12 hours ago [-]
People complain when a product use a familiar name that might collide and there's also people complaining when they invent new words altogether.
Naming things is hard.
Fnoord 5 hours ago [-]
Argue about taste. At least it is more original than OpenAI (haha, 'open') who started as non-profit and then pulled an Infantino. The Le Chat logo is also cool retro :)
On general purpose LLMs, and vibe coding, Mistral lags behind. But I find the targeted LMs much more interesting.
vardalab 11 hours ago [-]
Says who? I kind of like it.
bee_rider 10 hours ago [-]
Their web chat UI seems to be called “Vibe.” I think? I’m not sure if that’s the name of the product or just what they decided to label it in the browser. I wonder if -strap is just what they call the actual models, which are meant to be run “under the hood” anyway, so not really part of the branding.
But I wish they could commit to the bit fully and call everything -stral. It’s quirky and self aware to give your products silly names.
ranger_danger 11 hours ago [-]
There can be other valid perspectives than your own
cyanregiment 12 hours ago [-]
Was this one the last stral for you?
The stral the broke the camel's back?
whythismatters 11 hours ago [-]
The shortest stral has been pulled for you
fooofw 9 hours ago [-]
It seems like you're just clutching at strals now
mosura 12 hours ago [-]
Someone should use this to do the exact opposite of the intention: filter for “offensive” content, and boost it or collate it into a newsletter/email blast for people of culture.
You have to give it to Mistral they do at least know what the market near them says they want right now. The great problem is in a few years of this that market won’t be worth anything.
Edit to add, you could also add this to an AI workflow so as to produce content that walks right up to the line but doesn’t trigger it.
mcintyre1994 11 hours ago [-]
> filter for “offensive” content, and boost it or collate it into a newsletter/email blast for people of culture.
I think that's the main service that xAI provide for X.
kergonath 11 hours ago [-]
> The great problem is in a few years of this that market won’t be worth anything.
To be fair, we don’t know how much resources they put into this and how much of a distraction it was. If it was quick enough to train or fine tune and it brings them valuable experience for the next models, it could well be worth it in the long run even if there is no direct successor.
pebbly_bread 11 hours ago [-]
Also, it sounds like the kind of thing that sells. Any company with a customer support chat is a potential user of this model, any large company may be interested in getting a mistral installed set-up for handling without needing to send client info over the web. Installing those kinds of local systems seems to be what butters Mistral's bread at the moment.
cyanydeez 10 hours ago [-]
[flagged]
mosura 10 hours ago [-]
The bad words police are the actual authoritarian nutjobs. That is the problem.
cyanydeez 7 hours ago [-]
[flagged]
petcat 12 hours ago [-]
> they do at least know what the market near them says they want right now
It does seem to be a very European approach to AI that their flagship AI lab is just making models that do nothing other than monitor and moderate internet content.
I guess they know that the EU AI Act, Chat Control, etc are going to cause a lot of companies to need this kind of compliance.
lava_pidgeon 12 hours ago [-]
Some of the best social media is heavily moderate. This includes HN and r/credible defense . With a Quiet transparent and cheap LLM I imagine a social media website where you can have good discussion about everything around the world it would be a game changer and on my to-do list.
petcat 12 hours ago [-]
> Some of the best social media is heavily moderate
Heavily moderated by humans with discretion.
Not AI chat bots following a rules engine.
LunaSea 11 hours ago [-]
No, they are moderated by arbitrary company moderation policies not humans with independent thoughts.
AI can do exactly that.
baq 11 hours ago [-]
> Heavily moderated by humans with discretion.
Pretty sure both of the above have extensive automation in their moderation.
winwang 12 hours ago [-]
Wouldn't discretion "just" be a really good rules engine?
colechristensen 12 hours ago [-]
The bulk of moderation work is things which are easy and obvious.
The correct way to moderate is automation with certainty falling back to humans with discretion.
The new frontier of moderation should be blocking illiterate comments, as in the commenter is replying as though they didn't read or read and didn't understand.
tsss 11 hours ago [-]
I'd rather be censored by an AI than a reddit mod.
kergonath 11 hours ago [-]
> It does seem to be a very European approach to AI that their flagship AI lab is just making models that do nothing other than monitor and moderate internet content.
Well, first Mistral is French more than European. This might be a difficult distinction to make from the US but their approach is quite different from e.g. typical German companies.
Then, this is just a small model they release on the side. If that’s your benchmark, they released somewhat recently Voxtral, Voxtral transcribe, their OCR model, and Leanstral. I don’t think you can get much insight on their culture from this kind of release.
cinntaile 11 hours ago [-]
Another commenter already mentioned that it's more likely a lack of compute and funding that forces their hand to focus on niche tasks.
gspr 10 hours ago [-]
Ugh. There's nothing inherently European about Chat Control. It's a dumb proposal, and it's European. Any free society has a bunch of dumb proposals.
Nor is there anything inherently European about the AI Act. But that one I wouldn't even call dumb. At times misguided and confused, perhaps, but some of its core principles are valuable.
The kind where malicious intent is okay if the words are nice.
___
Or, rephrased: How big is the space in which you can tune this model without retraining.
Is it just "we hate sex"/"we don't hate sex" "We hate violence"/"we don't hate violence" or is it _truly_ as flexible as claimed?
__
Maybe something like "Is this guy a corporate fraud that is going to waste my time with performative nonsense?"
That would be the true test for a moderation model and I would be immensely impressed if it could manage to pull that off.
___
Edit: Looking at the paper though.. probably not.
I suppose this is useful for B2B, which seems to be mistrals whole thing. Question is just if it is also useful for society to hand the SV prefab morals down like that. Kinda like cultural imperialism but with an ethical spin.
Maybe opinions on those base datasets could occasionally differ more than the model can be steered.
In French as well. But it’s obviously not what’s meant here.
They got a lot of hate for not keeping up with frontier model releases, but have managed to carve out a nice business that isn't even really niche.
Before the datacenter deals their revenue was higher than xAI's
There is a whole world out there of purpose built and hosted task specific vertical llms - especially with an emphasis on cost.
Mistral, Microsoft model releases and Thinking Machines are all over this, and it's smart. Scoop up all the tasks that don't require large and expensive frontier general-purpose llms.
OCR is also another field - Mistral have a model, so do deepseek
The ones I have experience with where you fine-tune smaller / faster models for business tasks like content writing, support, etc. by their nature stay private
[1] https://mistral.ai/news/voxtral-transcribe-2/
[2] https://mistral.ai/news/
Isn't Mistral a French company? Not that the French can't do cultural imperialism either, but they (the French) don't strike me as very SV.
> The kind where malicious intent is okay if the words are nice.
Where are you experiencing that? I hit "report" on social media for overt, violent threats and hate speech all the time and I almost never see moderation kick in.
For example, by positioning what you’re doing as an accessibility tool and using the right words, you can get the latest models to write incredibly powerful malware without safeguards kicking in.
As the old adage goes, US innovates, China imitates, EU regulates.
And EU regulate, I mean as a counter example: we are not in panic about a nipple. When I was in a large museum in Paris, multiple women were breast feeding their infant. And why not? Kid's gotta eat. I'll refrain from insulting any world leaders, too easy, but you know many examples are available there regarding censorship.
Finally, it can take that BS argument away of 'oh we don't have manpower to moderate'. That is a low blow, too, by large commercial entities who could, you know hire and train? However, even a small company with not much money to burn could -in theory- win here.
I'd give this model a chance, if not only cause I've been impressed by Mistral past years. Yes, Le Chat / Vibe probably lags behind, but something like Voxtral (real-time and transcribe) is neat, and efficient.
Also I do like Mistral's seemingly newer strategy of focusing on smaller, more fine-tuned models for various use-cases, presumably the result of their large MoE models not competing effectively with the frontier models.
The problem with Mistral is that they do not seem to have aligned incentives to train big open-weight models, even if the teams would like to.
[1]: https://poolside.ai/ [2]: https://poolside.ai/blog/introducing-laguna-s-2-1
Here's CNBC's article on it: https://www.cnbc.com/2026/03/30/mistral-ai-paris-data-center...
Not sure if they would have received the full number yet, but it's been a few months so they certainly could have. Bit of a moot point when the comparison was against Poolside's Laguna which isn't really "general" SOTA but SOTA-for-the-size, and Mistral is clearly capable of training 700B or 120B models that are that when released considering they have done that... A 2-3T model is probably possible with the GPUs they have but they would need to spend most of their resources on it, and it's not clear why they would want to.
In the case of wondering why a bad thing got through, well, I think that’s why they just set these to the most pro-censorship level they can, to make that highly unlikely.
I've put easily over a billion requests (>$100,000 by typical moderation API pricing) through it over the last few years for $0.
I think it's a severely underappreciated offering, but I also don't bother pushing it too hard because who knows when the party will end lol. Strikes me as something that's only stuck around because no one's abusing it.
Let’s say you deploy it in production and a user comes back and says “Why is this prompt considered harmful?”
You have no way to provide a concrete reason to the user at that point.
2016 scenario: An end user contacts the company, and a customer service rep answers the ticket, saying they’re sorry and explaining that they’ve sent the feedback to the team, and the team may even receive at least a summary of complaints received about the system.
2026 scenario: all contact information has been scrubbed from the site. Users can click “chat” and a chatbot will apologize for their dissatisfaction and offer no option to escalate. No one will ever hear anything about the complaint, so there’s no need to explain the failure. User can either accept this or can get f**ked because all competitors operate the same way.
The reality is that most users don’t ask because they know they violated the rule.
Meta would really benefit from work done on this front, however their model Llama Guards are quite lagging compared to the competition.
Policy adaptive models really are the coolest things these days.
Also, check out https://roost.tools for even more open safety tooling!
They had kept up in the mid-range a few years ago. But this standing is sadly long gone.
If you need a fast Opensource'ed LLMs you can go for EU-hosted DeepSeek or Qwen.
There definitely have been Chinese companies with models that fell behind, which is the more direct comparison.
As for the EU in general, there are not a lot of known options. There are some working on things.
The US, the EU, and China all have frontier labs that have yet to release anything.
What’s the mechanism that could today prevent other companies from using LLM outputs to train their models?
Mind sharing such cases? I'm not aware of any so far. There's the one with images, but that's commonly miss-understood, that case was ruled on a technicality (i.e. copyright needs to be attributed to a person, not a model)
That is not the direction American judges are taking. Right now, they are saying that LLM output cannot be copyrighted. And if looting copyrighted works for training is fair game, I really don’t see how one could argue that learning from other LLMs is not.
Mistral 7b is still one of the best free/open models you can run locally on a MacBook. So fast too.
As for use cases, obviously we can't fully rely on non-deterministic capability for sensitive things but a small model which can do a good job acts as a first defense and then a human can review later.
Is it honest about religious texts? Can I throw at it religious texts and it'll honestly tell me whether the text promotes physical violence or not?
"Shieldstral" is an awkward and bad name
Naming things is hard.
On general purpose LLMs, and vibe coding, Mistral lags behind. But I find the targeted LMs much more interesting.
But I wish they could commit to the bit fully and call everything -stral. It’s quirky and self aware to give your products silly names.
The stral the broke the camel's back?
You have to give it to Mistral they do at least know what the market near them says they want right now. The great problem is in a few years of this that market won’t be worth anything.
Edit to add, you could also add this to an AI workflow so as to produce content that walks right up to the line but doesn’t trigger it.
I think that's the main service that xAI provide for X.
To be fair, we don’t know how much resources they put into this and how much of a distraction it was. If it was quick enough to train or fine tune and it brings them valuable experience for the next models, it could well be worth it in the long run even if there is no direct successor.
It does seem to be a very European approach to AI that their flagship AI lab is just making models that do nothing other than monitor and moderate internet content.
I guess they know that the EU AI Act, Chat Control, etc are going to cause a lot of companies to need this kind of compliance.
Heavily moderated by humans with discretion.
Not AI chat bots following a rules engine.
AI can do exactly that.
Pretty sure both of the above have extensive automation in their moderation.
The correct way to moderate is automation with certainty falling back to humans with discretion.
The new frontier of moderation should be blocking illiterate comments, as in the commenter is replying as though they didn't read or read and didn't understand.
Well, first Mistral is French more than European. This might be a difficult distinction to make from the US but their approach is quite different from e.g. typical German companies.
Then, this is just a small model they release on the side. If that’s your benchmark, they released somewhat recently Voxtral, Voxtral transcribe, their OCR model, and Leanstral. I don’t think you can get much insight on their culture from this kind of release.
Nor is there anything inherently European about the AI Act. But that one I wouldn't even call dumb. At times misguided and confused, perhaps, but some of its core principles are valuable.