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---
layout: default
title: Pretrained models
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parent: Using custom models within OpenSearch
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nav_order: 120
---
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Pretrained models are generally available in OpenSearch 2.9 and later.
Sparse encoding models are generally available in OpenSearch 2.11 and later.
{: .note}
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# Pretrained models
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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.
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## 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 ](#supported-pretrained-models ).
2. Upload the model using the [upload API ]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-serving-framework#upload-model-to-opensearch ). 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"
}
```
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Note that for sparse encoding models, you still need to upload the full request body, as shown in the following example:
```
POST /_plugins/_ml/models/_upload
{
"name": "amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1",
"version": "1.0.0",
"description": "This is a neural sparse encoding model: It transfers text into sparse vector, and then extract nonzero index and value to entry and weights. It serves only in ingestion and customer should use tokenizer model in query.",
"model_format": "TORCH_SCRIPT",
"function_name": "SPARSE_ENCODING",
"model_content_hash_value": "9a41adb6c13cf49a7e3eff91aef62ed5035487a6eca99c996156d25be2800a9a",
"url": "https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.0/torch_script/opensearch-neural-sparse-encoding-doc-v1-1.0.0-torch_script.zip"
}
```
{% include copy-curl.html %}
You can find the `url` and `model_content_hash_value` in the model config link for each model. For more information, see the [Supported pretrained models section ](#supported-pretrained-models ). Set the `function_name` to `SPARSE_ENCODING` or `SPARSE_TOKENIZE` .
Note that the `function_name` parameter in the request corresponds to the `model_task_type` parameter in the model config. When using a pretrained model, make sure to change the name of the parameter from `model_task_type` to `function_name` in the model upload request.
{: .important}
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For more information about how to upload and use ML models, see [Using custom models within OpenSearch ]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-serving-framework/ ).
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## Supported pretrained models
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OpenSearch supports the following models, categorized by type. Text embedding models are sourced from [Hugging Face ](https://huggingface.co/ ). Sparse encoding models are trained by OpenSearch. Although models with the same type will have similar use cases, each model has a different model size and will perform differently depending on your cluster setup. For a performance comparison of some pretrained models, see the [SBERT documentation ](https://www.sbert.net/docs/pretrained_models.html#model-overview ).
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### Sentence transformers
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Sentence transformer models map sentences and paragraphs across a dimensional dense vector space. The number of vectors depends on the type of model. You can use these models for use cases such as clustering or semantic search.
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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.
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| Model name | Vector dimensions | Auto-truncation | TorchScript artifact | ONNX artifact |
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|---|---|---|---|
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| `huggingface/sentence-transformers/all-distilroberta-v1` | 768-dimensional dense vector space. | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/torch_script/sentence-transformers_all-distilroberta-v1-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/onnx/sentence-transformers_all-distilroberta-v1-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/onnx/config.json ) |
| `huggingface/sentence-transformers/all-MiniLM-L6-v2` | 384-dimensional dense vector space. | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/torch_script/sentence-transformers_all-MiniLM-L6-v2-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/onnx/sentence-transformers_all-MiniLM-L6-v2-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/onnx/config.json ) |
| `huggingface/sentence-transformers/all-MiniLM-L12-v2` | 384-dimensional dense vector space. | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/torch_script/sentence-transformers_all-MiniLM-L12-v2-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/sentence-transformers_all-MiniLM-L12-v2-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/config.json ) |
| `huggingface/sentence-transformers/all-mpnet-base-v2` | 768-dimensional dense vector space. | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/torch_script/sentence-transformers_all-mpnet-base-v2-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/onnx/sentence-transformers_all-mpnet-base-v2-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/onnx/config.json ) |
| `huggingface/sentence-transformers/msmarco-distilbert-base-tas-b` | 768-dimensional dense vector space. Optimized for semantic search. | No | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/torch_script/sentence-transformers_msmarco-distilbert-base-tas-b-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/onnx/sentence-transformers_msmarco-distilbert-base-tas-b-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/onnx/config.json ) |
| `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 ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/torch_script/sentence-transformers_multi-qa-MiniLM-L6-cos-v1-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/onnx/sentence-transformers_multi-qa-MiniLM-L6-cos-v1-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/onnx/config.json ) |
| `huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1` | 384-dimensional dense vector space. | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/torch_script/sentence-transformers_multi-qa-mpnet-base-dot-v1-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/onnx/sentence-transformers_multi-qa-mpnet-base-dot-v1-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/onnx/config.json ) |
| `huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2` | 384-dimensional dense vector space. | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/torch_script/sentence-transformers_paraphrase-MiniLM-L3-v2-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/onnx/sentence-transformers_paraphrase-MiniLM-L3-v2-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/onnx/config.json ) |
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| `huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` | 384-dimensional dense vector space. | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/torch_script/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2-1.0.1-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/torch_script/config.json ) | - [model_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/onnx/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2-1.0.1-onnx.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/onnx/config.json ) |
### Sparse encoding models
Sparse encoding models transfer text into a sparse vector and convert the vector to a list of `<token: weight>` pairs representing the text entry and its corresponding weight in the sparse vector. You can use these models for use cases such as clustering or sparse neural search.
The following table provides a list of sparse encoding models and artifact links you can use to download them.
| Model name | Auto-truncation | TorchScript artifact |
|---|---|
| `amazon/neural-sparse/opensearch-neural-sparse-encoding-v1` | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.0/torch_script/opensearch-neural-sparse-encoding-v1-1.0.0-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.0/torch_script/config.json ) |
| `amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1` | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.0/torch_script/opensearch-neural-sparse-encoding-doc-v1-1.0.0-torch_script.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.0/torch_script/config.json ) |
| `amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` | Yes | - [model_url ](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/opensearch-neural-sparse-tokenizer-v1-1.0.0.zip )< br > - [config_url ](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/config.json ) |
