AWS SageMaker

SageMaker can be used for both chat and embedding models. Chat models are supported for endpoints deployed with LMI, and embedding models are supported for endpoints deployed with HuggingFace TEI

To setup SageMaker as a chat model provider, add the following to your config.json file:

config.json

{
  "models": [
    {
      "title": "deepseek-6.7b-instruct",
      "provider": "sagemaker",
      "model": "lmi-model-deepseek-coder-xxxxxxx",
      "region": "us-west-2"
    }
  ],
  "embeddingsProvider": {
    "provider": "sagemaker",
    "model": "mxbai-embed-large-v1-endpoint"
  },
}

The value in model should be the SageMaker endpoint name you deployed.

Authentication will be through temporary or long-term credentials in ~/.aws/credentials under a profile called "sagemaker".

[sagemaker]
aws_access_key_id = abcdefg
aws_secret_access_key = hijklmno
aws_session_token = pqrstuvwxyz # Optional: means short term creds.