> Can someone explain to me what they're trying to do now?

> "By creating mistral.ai, we intend to train state-of-the-art models with counter-positions to closed-model current offerings. Our vision is to become a leading actor in the field, while developing a very valuable business around integrating these models in the European industry and beyond."

Is this just a rhetorical question from your side? Because the last sentence gives you the answer. They do open models + focusing on integrating those open models into European industry. Seems pretty easy to grasp.

Seems similar to some Linux distros business model, give it away for free but the service is paid.

Have you used any of the Mistral 7b models? They're really good, I think the 7b model just shows what they're capable of. They also dropped an 8x7b mixture of experts model yesterday, that should be very interesting to try.

I have, through Ollama, and it would say their truthfulness is not great. You almost have to treat them with kid gloves and prompt them a certain way to get reasonable results (they're harder to steer than larger models). There's a reddit thread on this where people share tricks on how to handle 7B models.

https://www.reddit.com/r/LocalLLaMA/comments/18e929k/prompt_...

My feeling is 7B models should only be used for domains where reasoning and factual correctness are not needed, like summarization or generating creative variations of ideas. Code generation is ok too if one is willing to babysit it.

But otherwise, the answers can be misleadingly bad. Even reasoning fine-tuned models like Orca2 7B do very badly on simple math questions, despite Chain of Thought prompting.

If you believe a few assumptions then foundation model companies like Mistral look very valuable even if they open their models: 1) few companies are be able to produce top performing foundation models at any given time, 2) this will continue to be true for a long time due to the required expertise, infrastructure, and cost, 3) this somehow translates into a capability commercial customers will need and pay for at scale.

That third step is a bit of a leap. I tend to think that as the open knowledge about how to train models improves the training "recipes" needed to get close to state of the art will get easier to replicate, not harder, with "good enough models" requiring less data and less hardware.

Maybe they could earn money by fine tuning and ensuring it works perfectly for commercial customers.

The open model is the base. But if you want to use it as a customer service bot, you need it to be safe and stop hallucinating.

They just released a much larger model as a torrent. Probably the first open source mixture-of-experts model. Waiting for them to drop the code and explain it.

Which one? Everything on their website is still about the 7B model. 7B is the only model available on their huggingface page too: https://huggingface.co/mistralai

Edit: I'm guessing this is the model referenced: "Mistral "Mixtral" 8x7B 32k model [magnet]" - https://news.ycombinator.com/item?id=38570537

That's it. The community is working on getting it implemented in the popular inference engines. It came out less then 48 hours ago so give it a few days and we should be able to run it and test it at various quantizations depending on how much compute power you have.

There’s an initial version of it already up and running:

https://huggingface.co/DiscoResearch/mixtral-7b-8expert

I am doubtful about these efforts because people are just guessing about how it is supposed to work since Mistral didn't release any code or information yet.

Yes, there are probably some implementation pieces missing. But Disco Research also punches way above their weight on open source LLM stuff, so I would gamble it’s not that far off. They also just released a chat fine-tune of the model just moments ago:

https://huggingface.co/DiscoResearch/DiscoLM-mixtral-8x7b-v2

People had Mixtral 8x7B running on Twitter within 3 hours of the torrent being posted.

It's already been independently benchmarked and finetunes are cooking.