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Open-weight model · Sentence similarity
by Sentence Transformers sentence-transformers/distiluse-base-multilingual-cased-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
What it takes to serve distiluse-base-multilingual-cased-v1 (135M 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 | 0.3 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x 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, read Sep 18, 2026.
Ask what an embedding service costs to host and this one answers with 0.3 GB: at 16-bit the weights and the working memory both sit at that figure, so the cheapest host in our table, a single MI300X with 192 GB at $1.85 an hour, is oversized for it alone. You run it as a sidecar next to whatever generation model occupies the card, turning sentences and paragraphs into 512-dimensional vectors for clustering or semantic search. Sentence Transformers built it on DistilBERT with a 119,547-entry vocabulary.
Apache 2.0 covers commercial use, modification and redistribution, with the duty to keep license and copyright notices and state significant changes, and it carries a patent grant. Check the 512-token context before committing: longer passages must be chunked ahead of embedding. The files ship in safetensors, ONNX, OpenVINO, PyTorch and TensorFlow, and the method is written up in arXiv:1908.10084.
By Sentence Transformers, published under apache-2.0, revision 826fee3d516e.
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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:
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/distiluse-base-multilingual-cased-v1')
embeddings = model.encode(sentences)
print(embeddings)
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
30 files, 5.3 GB in total. The weights are 16 files totalling 5.3 GB in bin, h5, onnx, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| 2_Dense/model.safetensors | Weights | 1.6 MB | f8893703d8a6 |
| 2_Dense/pytorch_model.bin | Weights | 1.6 MB | 55f239314f9d |
| model.safetensors | Weights | 538.9 MB | a8c22f695296 |
| onnx/model.onnx | Weights | 539.0 MB | 44fbf16e7072 |
| onnx/model_O1.onnx | Weights | 539.0 MB | ff2e2ee741ce |
| onnx/model_O2.onnx | Weights | 539.0 MB | c19c162affe4 |
| onnx/model_O3.onnx | Weights | 539.0 MB | 8ceae59b5460 |
| onnx/model_O4.onnx | Weights | 269.5 MB | 582e3f1ab726 |
| onnx/model_qint8_arm64.onnx | Weights | 135.3 MB | e0f8cf6efba9 |
| onnx/model_qint8_avx512.onnx | Weights | 135.3 MB | e0f8cf6efba9 |
| onnx/model_qint8_avx512_vnni.onnx | Weights | 135.3 MB | e0f8cf6efba9 |
| onnx/model_quint8_avx2.onnx | Weights | 135.4 MB | 8bd3991c7c37 |
| openvino/openvino_model.bin | Weights | 538.9 MB | 106b129b2501 |
| openvino/openvino_model_qint8_quantized.bin | Weights | 135.7 MB | 881ddde6b7ff |
| pytorch_model.bin | Weights | 539.0 MB | 03df542359d5 |
| tf_model.h5 | Weights | 539.1 MB | 51e468d470af |
| 1_Pooling/config.json | Configuration | 190 B | — |
| 2_Dense/config.json | Configuration | 114 B | — |
| config.json | Configuration | 556 B | — |
| config_sentence_transformers.json | Configuration | 122 B | — |
| modules.json | Configuration | 341 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 112 B | — |
| README.md | Documentation | 2.2 KB | — |
| openvino/openvino_model.xml | Other | 217.6 KB | — |
| openvino/openvino_model_qint8_quantized.xml | Other | 372.6 KB | — |
| .gitattributes | Repository | 744 B | — |
| tokenizer.json | Tokenizer | 2.0 MB | — |
| tokenizer_config.json | Tokenizer | 452 B | — |
| vocab.txt | Tokenizer | 995.5 KB | — |
Released by Sentence Transformers through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 5.3 GB |
| 16-bit | 0.3 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.
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (135M parameters) plus a working margin. A long context needs more.
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.
Yes. distiluse-base-multilingual-cased-v1 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.
512 tokens, from the maximum position embeddings in its published configuration.
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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: This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
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