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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:
- Select the model you want to upload. For a list of pretrained models, see supported pretrained models.
- 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
, andmodel_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. As of OpenSearch 2.6, all artifacts are set to version 1.0.1.
Model name | Vector dimensions | Auto-truncation | Torchscript artifact | ONNX artifact |
---|---|---|---|---|
sentence-transformers/all-distilroberta-v1 | 768-dimensional dense vector space. | Yes | - model_url - config_url | - model_url - config_url |
sentence-transformers/all-MiniLM-L6-v2 | 384-dimensional dense vector space. | Yes | - model_url - config_url | - model_url - config_url |
sentence-transformers/all-MiniLM-L12-v2 | 384-dimensional dense vector space. | Yes | - model_url - config_url | - model_url - config_url |
sentence-transformers/all-mpnet-base-v2 | 768-dimensional dense vector space. | Yes | - model_url - config_url | - model_url - config_url |
sentence-transformers/msmarco-distilbert-base-tas-b | 768-dimensional dense vector space. Optimized for semantic search. | No | - model_url - config_url | - model_url - config_url |
sentence-transformers/multi-qa-MiniLM-L6-cos-v1 | 384 dimensional dense vector space. Designed for semantic search and trained on 215 million question/answer pairs. | Yes | - model_url - config_url | - model_url - config_url |
sentence-transformers/multi-qa-mpnet-base-dot-v1 | 384 dimensional dense vector space. | Yes | - model_url - config_url | - model_url - config_url |
sentence-transformers/paraphrase-MiniLM-L3-v2 | 384-dimensional dense vector space. | Yes | - model_url - config_url | - model_url - config_url |
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | 384-dimensional dense vector space. | Yes | - model_url - config_url | - model_url - config_url |