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

layout	title	parent	nav_order
default	Pretrained models	Using custom models within OpenSearch	120

Pretrained models were taken out of experimental status and released to General Availability in OpenSearch 2.9.
{: .warning}

Pretrained models

The ML 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 about how to upload and use ML models, see Using custom models within OpenSearch.

Supported pretrained models

The ML 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 performance comparison 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 you can use to download them. Note that you must prefix the model name with huggingface/, as shown in the Model name column. As of OpenSearch 2.6, all artifacts are set to version 1.0.1.

| Model name | Vector dimensions | Auto-truncation | TorchScript artifact | ONNX artifact | |---|---|---|---| | huggingface/sentence-transformers/all-distilroberta-v1 | 768-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/all-MiniLM-L6-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/all-MiniLM-L12-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/all-mpnet-base-v2 | 768-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/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 | | huggingface/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 | | huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url |