# paraphrase-multilingual-MiniLM-L12-v2 by Sentence Transformers Source: https://savrn.com/models/paraphrase-multilingual-minilm-l12-v2 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve paraphrase-multilingual-MiniLM-L12-v2 (118M 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. [paraphrase-multilingual-MiniLM-L12-v2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/paraphrase-multilingual-minilm-l12-v2/gpus) ## SAVRN's Notes on paraphrase-multilingual-MiniLM-L12-v2 Every sentence or paragraph you feed this comes back as 384 numbers, a dense vector you can cluster or search against. The vocabulary runs to 250,037 entries and the name says multilingual, so one index can hold more than one language. Memory is not a decision here: 0.3 GB at 16-bit on the cheapest card we track, a 192 GB MI300X at $1.85 an hour, so it shares a card rather than owning one. Check the 512-token ceiling first: anything longer gets chunked before it is embedded. The last update landed on 2026-01-28, so pin the revision your pipeline was built against. Without the sentence-transformers library you run the transformer and apply the pooling step yourself. Apache 2.0 permits commercial use, modification and redistribution; keep the notices, note significant changes, and the patent grant from contributors comes with it. SAVRN Research, 2026-09-18 ## Model Card By Sentence Transformers, published under apache-2.0, revision e8f8c211226b. This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ### Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ``` from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') embeddings = model.encode(sentences) print(embeddings) ``` ### Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. [Read the full model card (317 words)](https://savrn.com/models/paraphrase-multilingual-minilm-l12-v2/card) ## Configuration Architecture BertModel Context length (tokens) 512 Layers 12 Hidden size 384 Feed-forward size 1,536 Attention heads 12 Vocabulary size 250,037 Model type bert ## Identity and Version Repository sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 Publisher Sentence Transformers Task Sentence similarity Modality Text Library sentence-transformers Parameters 118M parameters Languages ar, bg, ca, cs, da, de, el, en Revision e8f8c211226b894fcb81acc59f3b34ba3efd5f42 First published 2022-03-02 Last updated 2026-01-28 ## Files and Weights 28 files, 4.6 GB in total. The weights are 14 files totalling 4.6 GB in bin, h5, onnx, safetensors. Weights14 files · 4.6 GB Configuration7 files · 14.8 MB Tokenizer2 files · 9.1 MB Documentation1 file · 3.9 KB Other3 files · 6.2 MB Repository1 file · 1.0 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 470.6 MB | eaa086f0ffee | | onnx/model.onnx | Weights | 470.3 MB | 10f7a0884202 | | onnx/model_O1.onnx | Weights | 470.2 MB | 9ae4b831e992 | | onnx/model_O2.onnx | Weights | 470.1 MB | 338ef03c2838 | | onnx/model_O3.onnx | Weights | 470.1 MB | 2426785672da | | onnx/model_O4.onnx | Weights | 235.2 MB | 307bba13f9f5 | | onnx/model_qint8_arm64.onnx | Weights | 118.4 MB | 783fea82d71a | | onnx/model_qint8_avx512.onnx | Weights | 118.4 MB | 783fea82d71a | | onnx/model_qint8_avx512_vnni.onnx | Weights | 118.4 MB | 783fea82d71a | | onnx/model_quint8_avx2.onnx | Weights | 118.5 MB | 98a01d88b7de | | openvino/openvino_model.bin | Weights | 470.0 MB | 04e8cc4ceedb | | openvino/openvino_model_qint8_quantized.bin | Weights | 119.0 MB | 24acd56a5f5a | | pytorch_model.bin | Weights | 470.7 MB | 16cc9e54df6e | | tf_model.h5 | Weights | 470.9 MB | 22150b6ba00e | | 1_Pooling/config.json | Configuration | 190 B | — | | config.json | Configuration | 645 B | — | | config_sentence_transformers.json | Configuration | 122 B | — | | modules.json | Configuration | 229 B | — | | sentence_bert_config.json | Configuration | 53 B | — | | special_tokens_map.json | Configuration | 239 B | — | | unigram.json | Configuration | 14.8 MB | 71b44701d7ef | | README.md | Documentation | 3.9 KB | — | | openvino/openvino_model.xml | Other | 398.5 KB | — | | openvino/openvino_model_qint8_quantized.xml | Other | 708.8 KB | — | | sentencepiece.bpe.model | Other | 5.1 MB | cfc8146abe2a | | .gitattributes | Repository | 1.0 KB | — | | tokenizer.json | Tokenizer | 9.1 MB | 2c3387be7655 | | tokenizer_config.json | Tokenizer | 526 B | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 4.6 GB [Download from Sentence Transformers](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) Released by Sentence Transformers through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:1908.10084](https://savrn.com/papers/sentence-bert-sentence-embeddings-using-siamese-bert-networks) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 4.6 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 paraphrase-multilingual-MiniLM-L12-v2 - [all-mpnet-base-v2 vs paraphrase-multilingual-MiniLM-L12-v2](https://savrn.com/models/compare/all-mpnet-base-v2-vs-paraphrase-multilingual-minilm-l12-v2) - [ko-sroberta-multitask vs paraphrase-multilingual-MiniLM-L12-v2](https://savrn.com/models/compare/ko-sroberta-multitask-vs-paraphrase-multilingual-minilm-l12-v2) - [multilingual-e5-small vs paraphrase-multilingual-MiniLM-L12-v2](https://savrn.com/models/compare/multilingual-e5-small-vs-paraphrase-multilingual-minilm-l12-v2) ## Questions About paraphrase-multilingual-MiniLM-L12-v2 ### How much GPU memory does paraphrase-multilingual-MiniLM-L12-v2 need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (118M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run paraphrase-multilingual-MiniLM-L12-v2 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. ### Can I use paraphrase-multilingual-MiniLM-L12-v2 commercially? Yes. paraphrase-multilingual-MiniLM-L12-v2 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is paraphrase-multilingual-MiniLM-L12-v2's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Sentence similarity ### [multilingual-e5-small](https://savrn.com/models/multilingual-e5-small) [Liang Wang](https://savrn.com/model-publishers/intfloat) Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 384. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. This model is initialized from microsoft/Multilingual-MiniLM-L12-H384 and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at https://arxiv.org/pdf/2402.05672. Check out unilm/e5 to reproduce evaluation results on the… Open weights mit 118M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/multilingual-e5-small) Model · Sentence similarity ### [contact_dedupe_STx_model](https://savrn.com/models/contact-dedupe-stx-model) [Gourav](https://savrn.com/model-publishers/gouravchat) 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 384 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 118M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/contact-dedupe-stx-model) Model · Sentence similarity ### [e5-small-dialect2MSA](https://savrn.com/models/e5-small-dialect2msa) [Oussama ben slama](https://savrn.com/model-publishers/oussamabenslama) This is a sentence-transformers model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 100 samples: - Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models. - Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss. - Binary and Scalar Embedding… Open weights 118M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/e5-small-dialect2msa) Model · Sentence similarity ### [KielEmbed-Code](https://savrn.com/models/kielembed-code) [Tech](https://savrn.com/model-publishers/kiel2) This is a sentence-transformers model finetuned from microsoft/codebert-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 100 samples: - numtrainepochs: 1 - learningrate: 2e-05 - warmupsteps: 0.1 - gradientaccumulationsteps: 4 - fp16: True - perdevicetrainbatchsize: 8 - numtrainepochs: 1 - maxsteps: -1 - learningrate: 2e-05 - lrschedulertype: linear - lrschedulerkwargs: None - warmupsteps: 0.1 - optim… Open weights 125M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/kielembed-code) Model · Sentence similarity ### [ko-sroberta-multitask](https://savrn.com/models/ko-sroberta-multitask) [Junghyun Gan](https://savrn.com/model-publishers/jhgan) 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. KorSTS, KorNLI 학습 데이터셋으로 멀티 태스크 학습을 진행한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다. The model was trained with the parameters: sentencetransformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of… Open weights 111M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/ko-sroberta-multitask) Model · Sentence similarity ### [lt-un-data-fine-fine-es](https://savrn.com/models/lt-un-data-fine-fine-es) [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 110M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/lt-un-data-fine-fine-es) ## Sentence Transformers [All models and datasets](https://savrn.com/model-publishers/sentence-transformers) ## Versions - [e8f8c211226b](https://savrn.com/models/paraphrase-multilingual-minilm-l12-v2/versions/e8f8c211226b) · current 2026-10-24 ## Explore More - [All sentence similarity models](https://savrn.com/models/tasks/sentence-similarity) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [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/paraphrase-multilingual-MiniLM-L12-v2) - [How the hub is built](https://savrn.com/model-hub/methodology)