While Microsoft's equity stake is currently about 1%, pending Mistral's next funding round, regulators will assess if the tech giant is gaining unfair advantage across multiple generative AI players.
The integration of Mistral models into the Azure AI model catalog accessible through Azure AI Studio and Azure Machine Learning provides Microsoft customers with a diverse selection of the best state-of-the-art and open-source models for crafting and deploying custom AI applications.
Mistral Large achieves strong performance on tests of reasoning, knowledge, coding, math and cross-lingual understanding and is second only to OpenAI's GPT-4
In this article, we cover the best ways to get started with Mixtral-8x7B. Let's dive in!
Mistral’s platform aims to empower developers with cutting-edge generative AI. The API follows chat interface standards popularized by other services to minimize migration barriers.
Leveraging the mixture of experts design allows scaling model capacity and performance substantially while controlling memory and compute costs, since any single token does not end up using all weights.
Andreessen Horowitz is leading the round with participation from other high-profile backers like NVIDIA, Salesforce, General Catalyst, Lightspeed, and Bpifrance.
Mistral-7B, is now natively available in the Vertex AI Notebooks. This integration provides seamless access to test, fine-tune, and deploy Mistral-7B on Google's managed AI service.
The purpose of this article is to help readers easily get up and running with Mistral 7B locally, or in your preferred cloud.