# multi-qa-mpnet-base-dot-v1 by Sentence Transformers Source: https://savrn.com/models/multi-qa-mpnet-base-dot-v1 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve multi-qa-mpnet-base-dot-v1 (109M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.2 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [multi-qa-mpnet-base-dot-v1 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/multi-qa-mpnet-base-dot-v1/gpus) ## SAVRN's Notes on multi-qa-mpnet-base-dot-v1 The license field on this page is empty, the first thing to settle for multi-qa-mpnet-base-dot-v1. It maps sentences and paragraphs to 768-dimensional vectors for semantic search, trained by Sentence Transformers on 215M question and answer pairs including ms_marco and natural_questions. At 109M parameters it needs 0.3 GB at 16-bit and 0.1 GB at 4-bit, so the single MI300X our Index lists at $1.85 an hour is far more than it needs; it shares a card with the generator it feeds, and the 514-token input limit means you chunk documents first. With no license listed, we would not put it into a commercial pipeline until the publisher's terms are confirmed; open access to the files is not permission to use them. Check the formats: the 28 files total 3.8 GB because safetensors, onnx, openvino and pytorch versions are included, and you need only one. SAVRN Research, 2026-09-18 ## Model Card This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. Text Embeddings Inference (TEI) is a blazing fast… Excerpt from the card by Sentence Transformers. ## Configuration Architecture MPNetForMaskedLM Context length (tokens) 514 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 30,527 Model type mpnet ## Identity and Version Repository sentence-transformers/multi-qa-mpnet-base-dot-v1 Publisher Sentence Transformers Task Sentence similarity Modality Text Library sentence-transformers Parameters 109M parameters Languages en Revision 17997f24dca0df1a4fed68894fb0e1e133e60482 First published 2022-03-02 Last updated 2025-08-19 ## Files and Weights 28 files, 3.8 GB in total. The weights are 13 files totalling 3.8 GB in bin, onnx, safetensors. Weights13 files · 3.8 GB Configuration8 files · 40.8 KB Tokenizer3 files · 697.9 KB Documentation1 file · 9.9 KB Other2 files · 1.2 MB Repository1 file · 791 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 438.0 MB | d0338d7385e2 | | onnx/model.onnx | Weights | 435.8 MB | dd2b2dcce9b4 | | onnx/model_O1.onnx | Weights | 435.7 MB | 44ade08ff5c7 | | onnx/model_O2.onnx | Weights | 435.7 MB | 78a8d3efbe1c | | onnx/model_O3.onnx | Weights | 435.7 MB | fb826ef52612 | | onnx/model_O4.onnx | Weights | 217.9 MB | 0f6a2a10c8d7 | | onnx/model_qint8_arm64.onnx | Weights | 110.1 MB | b30b640c8766 | | onnx/model_qint8_avx512.onnx | Weights | 110.1 MB | b30b640c8766 | | onnx/model_qint8_avx512_vnni.onnx | Weights | 110.1 MB | b30b640c8766 | | onnx/model_quint8_avx2.onnx | Weights | 110.2 MB | 96b00f1e7e6b | | openvino/openvino_model.bin | Weights | 435.6 MB | 2a0cd47f057c | | openvino/openvino_model_qint8_quantized.bin | Weights | 110.0 MB | 365a9b02c703 | | pytorch_model.bin | Weights | 438.0 MB | 9e1e76b7a067 | | 1_Pooling/config.json | Configuration | 190 B | — | | config.json | Configuration | 571 B | — | | config_sentence_transformers.json | Configuration | 212 B | — | | data_config.json | Configuration | 25.5 KB | — | | modules.json | Configuration | 229 B | — | | sentence_bert_config.json | Configuration | 53 B | — | | special_tokens_map.json | Configuration | 239 B | — | | train_script.py | Configuration | 13.9 KB | — | | README.md | Documentation | 9.9 KB | — | | openvino/openvino_model.xml | Other | 432.3 KB | — | | openvino/openvino_model_qint8_quantized.xml | Other | 741.4 KB | — | | .gitattributes | Repository | 791 B | — | | tokenizer.json | Tokenizer | 466.0 KB | — | | tokenizer_config.json | Tokenizer | 363 B | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License Not stated by the source Access Open weights, no gate Download size 3.8 GB [Download from Sentence Transformers](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1) Released by Sentence Transformers through its official repository on Hugging Face. ## Built From - Trained on (disclosed) eli5 - Trained on (disclosed) embedding-data/Amazon-QA - Trained on (disclosed) embedding-data/PAQ_pairs - Trained on (disclosed) embedding-data/QQP - Trained on (disclosed) embedding-data/WikiAnswers - Trained on (disclosed) flax-sentence-embeddings/stackexchange_xml - Trained on (disclosed) gooaq - Trained on (disclosed) ms_marco - Trained on (disclosed) natural_questions - Trained on (disclosed) search_qa - Trained on (disclosed) trivia_qa - Trained on (disclosed) yahoo_answers_topics ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 3.8 GB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare multi-qa-mpnet-base-dot-v1 - [all-mpnet-base-v2 vs multi-qa-mpnet-base-dot-v1](https://savrn.com/models/compare/all-mpnet-base-v2-vs-multi-qa-mpnet-base-dot-v1) ## Questions About multi-qa-mpnet-base-dot-v1 ### How much GPU memory does multi-qa-mpnet-base-dot-v1 need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run multi-qa-mpnet-base-dot-v1 on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### What is multi-qa-mpnet-base-dot-v1's context length? 514 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Sentence similarity ### [all-mpnet-base-v2](https://savrn.com/models/all-mpnet-base-v2) [Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers) This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Text Embeddings Inference (TEI) is a blazing fast inference solution for text embedding models. Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API: Or check… Open weights apache-2.0 109M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/all-mpnet-base-v2) Model · Sentence similarity ### [paraphrase-mpnet-base-v2](https://savrn.com/models/paraphrase-mpnet-base-v2) [Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers) This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Text Embeddings Inference (TEI) is a blazing fast inference solution for text embedding models. Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API: Or check… Open weights apache-2.0 109M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/paraphrase-mpnet-base-v2) Model · Sentence similarity ### [nli-mpnet-base-v2](https://savrn.com/models/nli-mpnet-base-v2) [Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers) This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. 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It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we… Open weights 109M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/lt-wikidata-comp-en) Model · Sentence similarity ### [lt-un-data-fine-fine-en](https://savrn.com/models/lt-un-data-fine-fine-en) [Dell Research Harvard](https://savrn.com/model-publishers/dell-research-harvard) This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we… Open weights 109M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/lt-un-data-fine-fine-en) Model · Sentence similarity ### [names-linking-model](https://savrn.com/models/names-linking-model) [Dell Research Harvard](https://savrn.com/model-publishers/dell-research-harvard) This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we… Open weights 109M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/names-linking-model) ## Sentence Transformers [All models and datasets](https://savrn.com/model-publishers/sentence-transformers) ## Versions - [17997f24dca0](https://savrn.com/models/multi-qa-mpnet-base-dot-v1/versions/17997f24dca0) · current 2026-10-24 ## Explore More - [All sentence similarity models](https://savrn.com/models/tasks/sentence-similarity) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Sentence Transformers website](https://sbert.net/) - [Hugging Face record](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1) - [How the hub is built](https://savrn.com/model-hub/methodology)