Hugging Face models in Amazon Bedrock

We are excited to announce that popular open models from Hugging Face are now available on Amazon Bedrock in the new Bedrock Marketplace! AWS customers can now deploy 83 open models with Bedrock Marketplace to build their Generative AI applications.

Under the hood, Bedrock Marketplace model endpoints are managed by Amazon Sagemaker Jumpstart. With Bedrock Marketplace, you can now combine the ease of use of SageMaker JumpStart with the fully managed infrastructure of Amazon Bedrock, including compatibility with high-level APIs such as Agents, Knowledge Bases, Guardrails and Model Evaluations.

When registering your Sagemaker Jumpstart endpoints in Amazon Bedrock, you only pay for the Sagemaker compute resources and regular Amazon Bedrock APIs prices are applicable.

In this blog we will show you how to deploy Gemma 2 27B Instruct and use the model with Amazon Bedrock APIs. Learn how to:

There are two ways to deploy an open model to be used with Amazon Bedrock:

To get started, in the Amazon Bedrock console, make sure you are in one of the 14 regions where the Bedrock Marketplace is available. Then, you choose “Model catalog” in the “Foundation models” section of the navigation pane. Here, you can search for both serverless models and models available in Amazon Bedrock Marketplace. You filter results by “Hugging Face” provider and you can browse through the 83 open models available.

We just launched the deployment of GoogleGemma 2 27B Instruct model on a ml.g5.48xlarge instance, hosted in your Amazon Sagemaker tenancy, compatible with Amazon Bedrock APIs!

You are also required to accept the End User License Agreement of the model provider.

Choosing the model opens the model detail page where you can see more information from the model provider such as highlights about the model, and usage including sample API calls.

It brings you to the deployment page where you can select the endpoint name, the instance configuration and advanced settings related to networking configuration and service role used to perform the deployment in Sagemaker. Let’s use the default advanced settings and the recommended instance type.

The endpoint deployment can take several minutes. It will appear in the “Marketplace deployments” page, which you can find in the “Foundation models” section of the navigation pane.