nli-mpnet-base-v2 by Sentence Transformers: Open Model

Runs On

What it takes to serve nli-mpnet-base-v2 (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 (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.1 GB	0.1 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.

SAVRN's Notes on nli-mpnet-base-v2

Clustering and semantic search are the jobs here: each sentence or paragraph comes back as a 768-dimensional vector, and the 109 million parameters behind it need 0.3 GB of memory at 16-bit, 0.2 GB of that being weights. The cheapest setup, one MI300X with 192 GB at $1.85 per hour on-demand, would sit nearly idle on it. Do not buy a GPU for it; put it on a card already running something else, or use the ONNX or OpenVINO exports the publisher includes.

Apache 2.0 with its patent grant lets a deployment modify, redistribute and commercialize it, provided the notices stay attached. Check the 514-token context first, since longer documents must be chunked and that policy shapes what retrieval returns. Of the 4.26 GB repository, fetch only the format your stack loads. No evaluations or SAVRN Index host prices are on file, so plan your own comparison.

SAVRN Research, 2026-09-18

Model Card

By Sentence Transformers, published under apache-2.0, revision c2f4dd9a1dc4.

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.

Usage (Sentence-Transformers)

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/nli-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

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.

Read the full model card (408 words)

Configuration

Architecture: MPNetModel
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/nli-mpnet-base-v2
Publisher: Sentence Transformers
Task: Sentence similarity
Modality: Text
Library: sentence-transformers
Parameters: 109M parameters
Languages: tf
Revision: c2f4dd9a1dc4337c28cfbd650433f761bb304c50
First published: 2022-03-02
Last updated: 2025-08-19

Files and Weights

27 files, 4.3 GB in total. The weights are 14 files totalling 4.3 GB in bin, h5, onnx, safetensors.

Weights14 files · 4.3 GB

Configuration6 files · 1.4 KB

Tokenizer3 files · 698.9 KB

Documentation1 file · 4.6 KB

Other2 files · 1.2 MB

Repository1 file · 744 B

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	438.0 MB	cad5e88c1806
onnx/model.onnx	Weights	435.8 MB	f4eaaef3227f
onnx/model_O1.onnx	Weights	435.7 MB	7859da8e0d75
onnx/model_O2.onnx	Weights	435.7 MB	b26070b85e93
onnx/model_O3.onnx	Weights	435.7 MB	23feca3e58ff
onnx/model_O4.onnx	Weights	217.9 MB	01c9aabc5cab
onnx/model_qint8_arm64.onnx	Weights	110.1 MB	890c0088d7ce
onnx/model_qint8_avx512.onnx	Weights	110.1 MB	890c0088d7ce
onnx/model_qint8_avx512_vnni.onnx	Weights	110.1 MB	890c0088d7ce
onnx/model_quint8_avx2.onnx	Weights	110.2 MB	f97501d0be95
openvino/openvino_model.bin	Weights	435.6 MB	70c3dce97899
openvino/openvino_model_qint8_quantized.bin	Weights	110.0 MB	7aadd947b05c
pytorch_model.bin	Weights	438.0 MB	c250de5d7533
tf_model.h5	Weights	438.2 MB	573d04bc90e9
1_Pooling/config.json	Configuration	190 B	—
config.json	Configuration	587 B	—
config_sentence_transformers.json	Configuration	122 B	—
modules.json	Configuration	229 B	—
sentence_bert_config.json	Configuration	52 B	—
special_tokens_map.json	Configuration	239 B	—
README.md	Documentation	4.6 KB	—
openvino/openvino_model.xml	Other	432.8 KB	—
openvino/openvino_model_qint8_quantized.xml	Other	742.1 KB	—
.gitattributes	Repository	744 B	—
tokenizer.json	Tokenizer	466.2 KB	—
tokenizer_config.json	Tokenizer	1.2 KB	—
vocab.txt	Tokenizer	231.5 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 4.3 GB

Download from Sentence Transformers

Released by Sentence Transformers through its official repository on Hugging Face. Read the license.

Built From

Described by arXiv:1908.10084

Memory Requirements

Precision	Weights in memory
As published	4.3 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.

Questions About nli-mpnet-base-v2

How much GPU memory does nli-mpnet-base-v2 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 nli-mpnet-base-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 nli-mpnet-base-v2 commercially?

Yes. nli-mpnet-base-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 nli-mpnet-base-v2's context length?

514 tokens, from the maximum position embeddings in its published configuration.

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