The model-serving framework is an experimental feature. For updates on the progress of the model-serving framework, or if you want to leave feedback that could help improve the feature, join the discussion in the Model-serving framework forum.

Pretrained models

The model-serving framework supports a variety of open-source pretrained models that can assist with a range of machine learning (ML) search and analytics use cases.

Uploading pretrained models

To use a pretrained model in your OpenSearch cluster:

1. Select the model you want to upload. For a list of pretrained models, see supported pretrained models.
2. Upload the model using the upload API. Because a pretrained model originates from the ML Commons model repository, you only need to provide the name, version, and model_format in the upload API request.

POST /_plugins/_ml/models/_upload
{
  "name": "huggingface/sentence-transformers/all-MiniLM-L12-v2",
  "version": "1.0.1",
  "model_format": "TORCH_SCRIPT"
}

For more information on how to upload and use ML models, see Model-serving framework.

Supported pretrained models

The model-serving framework supports the following models, categorized by type. All models are traced from Hugging Face. Although models with the same type will have similar use cases, each model has a different model size and performs differently depending on your cluster. For a comparison of the performances of some pretrained models, see the sbert documentation.

Sentence transformers

Sentence transformer models map sentences and paragraphs across a dimensional dense vector space. The number of vectors depends on the model. Use these models for use cases such as clustering and semantic search.

The following table provides a list of sentence transformer models and artifact links to download them: