Google recently announced the integration of Paris based AI startup Mistral AI’s open-source model, Mistral-7B, with Vertex AI Notebooks. This integration empowers Google Cloud customers to delve into a comprehensive end-to-end workflow, enabling them to experiment, fine-tune, and deploy Mistral-7B and its instructional variant on Vertex AI Notebooks.

Leveraging this integration, Mistral AI users can optimize their models using vLLM, a highly efficient Large Language Model serving framework. By utilising Vertex AI Notebooks, users can deploy a vLLM image, maintained by Model Garden, on a Vertex AI endpoint for inference, ensuring streamlined model deployment.

Vertex AI Notebooks facilitate collaborative efforts among data scientists. They can seamlessly connect to Google Cloud data services, analyze datasets, experiment with diverse modeling techniques, deploy trained models into production, and manage MLOps throughout the model lifecycle. 

A pivotal feature of this collaboration is the Vertex AI Model Registry, a central repository that empowers users to manage the lifecycle of Mistral AI models and their fine-tuned counterparts. From this registry, users gain a comprehensive overview of their models, enhancing organization and tracking capabilities. 

Importantly, users can effortlessly deploy specific model versions directly from the registry, simplifying the deployment process. Additionally, users can employ aliases to deploy models to designated endpoints, further streamlining the deployment and management procedures.

Despite its compact size, Mistral-7B boasts deep reasoning capabilities and compressed knowledge. It utilises innovative technologies like Grouped-Query Attention (GQA) and Sliding Window Attention (SWA) to balance speed and accuracy, particularly in handling longer sequences, reducing training time, costs, and energy consumption, thus promoting sustainability and efficiency in AI applications.