# all-MiniLM-L12-v2 by Sentence Transformers: Open Model Source: https://savrn.com/models/all-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 all-MiniLM-L12-v2 (33M 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 | 0.1 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 | | 8-bit | 0.0 GB | 0.0 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 | 0.0 GB | 0.0 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 23, 2026. [all-MiniLM-L12-v2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/all-minilm-l12-v2/gpus) ## SAVRN's Notes on all-MiniLM-L12-v2 Most of the 1.3 GB download for all-MiniLM-L12-v2 is format duplication: 29 files spanning safetensors, onnx, openvino, pytorch and rust builds of a model whose 16-bit working set is 0.1 GB, with 8-bit and 4-bit rounding to zero, so pick your runtime's format. Underneath, 33M parameters turn up to 512 tokens into a 384-number vector for semantic search and clustering. One MI300X at $1.85 an hour is the cheapest listed setup; the real question is whether something this small rides along on hardware you already run. Apache 2.0 permits commercial use, modification and redistribution with an express patent grant, and asks you to keep the license and NOTICE file and state significant changes. Check the training sets named in the file, from ms_marco and search_qa to stackexchange_xml, code_search_net and s2orc, against the text you will feed it. Released March 2, 2022, it was last updated March 31, 2026. SAVRN Research, 2026-09-18 ## Model Card By Sentence Transformers, published under apache-2.0, revision a50ef00143b4. 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/all-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 (760 words)](https://savrn.com/models/all-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 30,522 Model type bert ## Identity and Version Repository sentence-transformers/all-MiniLM-L12-v2 Publisher Sentence Transformers Task Sentence similarity Modality Text Library sentence-transformers Parameters 33M parameters Languages en Revision a50ef00143b4d5391434df20ae11632588ac25be First published 2022-03-02 Last updated 2026-03-31 ## Files and Weights 29 files, 1.3 GB in total. The weights are 14 files totalling 1.3 GB in bin, onnx, ot, safetensors. Weights14 files · 1.3 GB Configuration8 files · 53.9 KB Tokenizer3 files · 698.1 KB Documentation1 file · 10.5 KB Other2 files · 1.1 MB Repository1 file · 1.2 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 133.5 MB | d2d541e5f101 | | onnx/model.onnx | Weights | 133.1 MB | 84c56795d395 | | onnx/model_O1.onnx | Weights | 133.0 MB | 681eac1ec7d0 | | onnx/model_O2.onnx | Weights | 133.0 MB | b1bee3861e7e | | onnx/model_O3.onnx | Weights | 133.0 MB | 691688efa849 | | onnx/model_O4.onnx | Weights | 66.6 MB | e3a8fea85d1f | | onnx/model_qint8_arm64.onnx | Weights | 34.1 MB | d37479e69ddc | | onnx/model_qint8_avx512.onnx | Weights | 34.1 MB | d37479e69ddc | | onnx/model_qint8_avx512_vnni.onnx | Weights | 34.1 MB | d37479e69ddc | | onnx/model_quint8_avx2.onnx | Weights | 34.2 MB | 3c5e33c47849 | | openvino/openvino_model.bin | Weights | 132.9 MB | e9cb0a0124ce | | openvino/openvino_model_qint8_quantized.bin | Weights | 33.8 MB | 1ad4ebfa1ba2 | | pytorch_model.bin | Weights | 133.5 MB | 54609dea3ff8 | | rust_model.ot | Weights | 133.5 MB | e6187a301772 | | 1_Pooling/config.json | Configuration | 190 B | — | | config.json | Configuration | 615 B | — | | config_sentence_transformers.json | Configuration | 116 B | — | | data_config.json | Configuration | 39.3 KB | — | | modules.json | Configuration | 349 B | — | | sentence_bert_config.json | Configuration | 53 B | — | | special_tokens_map.json | Configuration | 112 B | — | | train_script.py | Configuration | 13.2 KB | — | | README.md | Documentation | 10.5 KB | — | | openvino/openvino_model.xml | Other | 398.3 KB | — | | openvino/openvino_model_qint8_quantized.xml | Other | 708.1 KB | — | | .gitattributes | Repository | 1.2 KB | — | | tokenizer.json | Tokenizer | 466.2 KB | — | | tokenizer_config.json | Tokenizer | 352 B | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 1.3 GB [Download from Sentence Transformers](https://huggingface.co/sentence-transformers/all-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 - Derived from microsoft/MiniLM-L12-H384-uncased - Described by [arXiv:1704.05179](https://savrn.com/papers/searchqa-a-new-q-a-dataset-augmented-with-context-from-a-search-engine) - Described by [arXiv:1810.09305](https://savrn.com/papers/wikihow-a-large-scale-text-summarization-dataset) - Described by [arXiv:1904.06472](https://savrn.com/papers/a-repository-of-conversational-datasets) - Described by [arXiv:2102.07033](https://savrn.com/papers/paq-65-million-probably-asked-questions-and-what-you-can-do-with-them) - Described by [arXiv:2104.08727](https://savrn.com/papers/gooaq-open-question-answering-with-diverse-answer-types) - Quantized from microsoft/MiniLM-L12-H384-uncased - Trained on (disclosed) code_search_net - Trained on (disclosed) eli5 - Trained on (disclosed) embedding-data/PAQ_pairs - Trained on (disclosed) embedding-data/QQP - Trained on (disclosed) embedding-data/SPECTER - Trained on (disclosed) embedding-data/WikiAnswers - Trained on (disclosed) embedding-data/altlex - Trained on (disclosed) embedding-data/flickr30k-captions - Trained on (disclosed) embedding-data/sentence-compression - Trained on (disclosed) embedding-data/simple-wiki - Trained on (disclosed) flax-sentence-embeddings/stackexchange_xml - Trained on (disclosed) gooaq - Trained on (disclosed) ms_marco - Trained on (disclosed) multi_nli - Trained on (disclosed) natural_questions - Trained on (disclosed) s2orc - Trained on (disclosed) search_qa - Trained on (disclosed) snli - Trained on (disclosed) trivia_qa - Trained on (disclosed) wikihow - Trained on (disclosed) yahoo_answers_topics ## Evaluations Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical. | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | mteb/arguana | Task ArguAnaMetric ArguAnaSetup Obtained using MTEB v1.38.3Comparison conditions not established | 47.128 | [Obtained using MTEB v1.38.3](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-05 | | mteb/arguana | Task ArguAna_default_testMetric ArguAna_default_testSetup Obtained using MTEB v1.38.3Comparison conditions not established | 47.128 | [Obtained using MTEB v1.38.3](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-05 | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.3 GB | | 16-bit | 0.1 GB | | 8-bit | 0.0 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare all-MiniLM-L12-v2 - [all-MiniLM-L12-v2 vs e5-small-v2](https://savrn.com/models/compare/all-minilm-l12-v2-vs-e5-small-v2) - [all-MiniLM-L12-v2 vs gte-small](https://savrn.com/models/compare/all-minilm-l12-v2-vs-gte-small) - [all-MiniLM-L12-v2 vs snowflake-arctic-embed-s](https://savrn.com/models/compare/all-minilm-l12-v2-vs-snowflake-arctic-embed-s) ## Questions About all-MiniLM-L12-v2 ### How much GPU memory does all-MiniLM-L12-v2 need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (33M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run all-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 all-MiniLM-L12-v2 commercially? Yes. all-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 all-MiniLM-L12-v2's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Sentence similarity ### [gte-small](https://savrn.com/models/gte-small) [Dingkun Long](https://savrn.com/model-publishers/thenlper) General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc. We compared the performance of the GTE models with other popular text embedding models on the MTEB… Open weights mit 33M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/gte-small) Model · Sentence similarity ### [e5-small-v2](https://savrn.com/models/e5-small-v2) [Liang Wang](https://savrn.com/model-publishers/intfloat) Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 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. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark. Below is an example for usage with sentencetransformers. Package requirements pip install sentencetransformers~=2.2.2 1. Do I need to add the prefix "query: " and "passage: " to input texts? Yes, this is how the model is trained, otherwise you will see a performance degradation.… Open weights mit 33M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/e5-small-v2) Model · Sentence similarity ### [AtmicQuoterv3](https://savrn.com/models/atmicquoterv3) [Atmic Intelligence Project- Sri Ramanasramam](https://savrn.com/model-publishers/sriramanaatmic) Fine-tuned from BAAI/bge-small-en-v1.5 (MultipleNegativesRankingLoss, 3 epochs, batch size 16, lr 2e-5) on the combined AtmicQuoterv1 + AtmicQuoterv3 training data, for exact-citation retrieval over Sri Ramana Maharshi's teachings. Queries are encoded with the BGE query instruction (Represent this sentence for searching relevant passages: ); passages are not. Validation set: 1,006 queries (48 from the root validation.jsonl + 958 from AtmicQuoterv1's validation split). Corpus: 8,742 unique passages (AtmicQuoterv1's corpus and the full citation/RAGcitation.json corpus, after the text normalization described below). "alone" = dense retrieval only, ranking the full corpus. "hybrid BM25" =… Open weights 33M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/atmicquoterv3) Model · Sentence similarity ### [bge-small-vscode-dup](https://savrn.com/models/bge-small-vscode-dup) [Musab Suhail](https://savrn.com/model-publishers/musab6969) This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. 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. Evaluated with EmbeddingSimilarityEvaluator Approximate statistics based on the first 100 samples: "scale": 20.0, "similarityfct": "cossim", "gatheracrossdevices": false, "directions": [ "querytodoc" "partitionmode": "joint", "hardnessmode": null, "hardnessstrength": 0.0 bibtex title = "Sentence-BERT: Sentence Embeddings using… Open weights 33M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/bge-small-vscode-dup) Model · Sentence similarity ### [snowflake-arctic-embed-s](https://savrn.com/models/snowflake-arctic-embed-s) [Snowflake](https://savrn.com/model-publishers/snowflake) 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv. 07/18/2024: Release of snowflake-arctic-embed-m-v1.5, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the launch post on the Snowflake engineering… Open weights apache-2.0 33M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/snowflake-arctic-embed-s) Model · Sentence similarity ### [rubert-tiny2](https://savrn.com/models/rubert-tiny2) [David Dale](https://savrn.com/model-publishers/cointegrated) This is an updated version of cointegrated/rubert-tiny: a small Russian BERT-based encoder with high-quality sentence embeddings. This post in Russian gives more details. The differences from the previous version include: - sentence embeddings approximate LaBSE closer than before; - meaningful segment embeddings (tuned on the NLI task) - the model is focused only on Russian. The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task. Sentence embeddings can be produced as follows: Alternatively, you can use the model with sentencetransformers: For those who want to run the inference with VLLM, there is a… Open weights mit 29M parameters 2,048 tokens sentence-transformers [View model](https://savrn.com/models/rubert-tiny2) ## Sentence Transformers [All models and datasets](https://savrn.com/model-publishers/sentence-transformers) ## Versions - [a50ef00143b4](https://savrn.com/models/all-minilm-l12-v2/versions/a50ef00143b4) · current 2026-10-23 ## 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/all-MiniLM-L12-v2) - [How the hub is built](https://savrn.com/model-hub/methodology)