Global AI chat room · 18 online now Join now
P
MODEL Listed

paraphrase multilingual MiniLM L12 v2 onnx Q

The paraphrase-multilingual-MiniLM-L12-v2 (ONNX quantized) is a lightweight, high-efficiency sentence transformer designed for cross-lingual semantic similarity tasks. Unlike large generative models, this model focuses on mapping text from over 100 languages into a shared vector space, making it ideal for clustering, semantic search, and duplicate detection across different languages. The ONNX quantization significantly reduces the memory footprint and latency, allowing for high-throughput deployment on CPUs without requiring heavy GPU resources. For developers, this means an easy integration path for RAG pipelines or multilingual chatbots where low-latency embedding generation is critical.

Qdranttext2text-generation
View model source
01 / MODEL CARD

Model card

The paraphrase-multilingual-MiniLM-L12-v2 (ONNX quantized) is a lightweight, high-efficiency sentence transformer designed for cross-lingual semantic similarity tasks. Unlike large generative models, this model focuses on mapping text from over 100 languages into a shared vector space, making it ideal for clustering, semantic search, and duplicate detection across different languages. The ONNX quantization significantly reduces the memory footprint and latency, allowing for high-throughput deployment on CPUs without requiring heavy GPU resources. For developers, this means an easy integration path for RAG pipelines or multilingual chatbots where low-latency embedding generation is critical.

Model typetext2text-generation
ProviderQdrant
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/Qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q
View model source
Version informationUse the source repository for the latest version
—
03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: Qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model Qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model Qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('Qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/Qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

Open source page
Email