opensearch-docs-cn/_ml-commons-plugin/pretrained-models.md

layout	title	parent	nav_order
default	Pretrained models	Model-serving framework	120

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.
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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. 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 |