Mistral AI
This is the official account for Mistral AI
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Thanks to the community feedback, we've made a small UI improvement: now you can share conversations in @MistralAI Le Chat!
Mistral was founded by Arthur Mensch, TimothĆ©e Lacroix and Guillaume Lample, a trio of former Meta and Google researchers Ā© FT montage/David Atlan
It's finally time! Our Mixtral 8x7B model is up and available now!
Nous-Hermes-2 Mixtral 8x7B comes in two variants, an SFT+DPO and SFT-Only, so you can try and see which works best for you!
It's afaik the first Mixtral based model to beat @Mistral AI Mixtral Instruct model, and in my own personal testing, is potentially the best Open Source LLM available!
Nous-Hermes-2 Mixtral 8x7B comes in two variants, an SFT+DPO and SFT-Only, so you can try and see which works best for you!
It's afaik the first Mixtral based model to beat @Mistral AI Mixtral Instruct model, and in my own personal testing, is potentially the best Open Source LLM available!
Mistral AI brings the strongest open generative models to the developers, along with efficient ways to deploy and customise them for production.
Weāre opening a beta access to our first platform services today. We start simple: la plateforme serves three chat endpoints for generating text following textual instructions and an embedding endpoint. Each endpoint has a different performance/price tradeoff.
Generative endpoints
The two first endpoints, mistral-tiny and mistral-small, currently use our two released open models; the third, mistral-medium, uses a prototype model with higher performances that we are testing in a deployed setting.
We serve instructed versions of our models. We have worked on consolidating the most effective alignment techniques (efficient fine-tuning, direct preference optimisation) to create easy-to-control and pleasant-to-use models. We pre-train models on data extracted from the open Web and perform instruction fine-tuning from annotations.
Mistral-tiny. Our most cost-effective endpoint currently serves Mistral 7B Instruct v0.2, a new minor release of Mistral 7B Instruct. Mistral-tiny only works in English. It obtains 7.6 on MT-Bench. The instructed model can be downloaded here.
Mistral-small. This endpoint currently serves our newest model, Mixtral 8x7B, described in more detail in our blog post. It masters English/French/Italian/German/Spanish and code and obtains 8.3 on MT-Bench.
Mistral-medium. Our highest-quality endpoint currently serves a prototype model, that is currently among the top serviced models available based on standard benchmarks. It masters English/French/Italian/German/Spanish and code and obtains a score of 8.6 on MT-Bench. The following table compare the performance of the base models of Mistral-medium, Mistral-small and the endpoint of a competitor.$mistralai
Weāre opening a beta access to our first platform services today. We start simple: la plateforme serves three chat endpoints for generating text following textual instructions and an embedding endpoint. Each endpoint has a different performance/price tradeoff.
Generative endpoints
The two first endpoints, mistral-tiny and mistral-small, currently use our two released open models; the third, mistral-medium, uses a prototype model with higher performances that we are testing in a deployed setting.
We serve instructed versions of our models. We have worked on consolidating the most effective alignment techniques (efficient fine-tuning, direct preference optimisation) to create easy-to-control and pleasant-to-use models. We pre-train models on data extracted from the open Web and perform instruction fine-tuning from annotations.
Mistral-tiny. Our most cost-effective endpoint currently serves Mistral 7B Instruct v0.2, a new minor release of Mistral 7B Instruct. Mistral-tiny only works in English. It obtains 7.6 on MT-Bench. The instructed model can be downloaded here.
Mistral-small. This endpoint currently serves our newest model, Mixtral 8x7B, described in more detail in our blog post. It masters English/French/Italian/German/Spanish and code and obtains 8.3 on MT-Bench.
Mistral-medium. Our highest-quality endpoint currently serves a prototype model, that is currently among the top serviced models available based on standard benchmarks. It masters English/French/Italian/German/Spanish and code and obtains a score of 8.6 on MT-Bench. The following table compare the performance of the base models of Mistral-medium, Mistral-small and the endpoint of a competitor.$mistralai
As always, please try stay as close to the truth as possible, even for stuff you donāt like.
This platform aspires to maximize signal/noise of the human collective.
This platform aspires to maximize signal/noise of the human collective.