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MODEL Listed

multilingual-e5-small

Multilingual E5 Small is a lightweight, high-efficiency embedding model designed for cross-lingual sentence similarity and semantic search. Unlike larger LLMs, this model focuses specifically on mapping text from multiple languages into a shared vector space, making it an ideal choice for developers building RAG (Retrieval-Augmented Generation) pipelines or clustering systems where low latency and minimal memory overhead are critical. It balances performance with a small footprint, allowing for deployment on edge devices or CPU-only environments without sacrificing significant retrieval accuracy. Integration is straightforward via standard sentence-transformer libraries, providing a scalable alternative to proprietary embedding APIs for international applications.

intfloatsentence similarity
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01 / MODEL CARD

Model card

Multilingual E5 Small is a lightweight, high-efficiency embedding model designed for cross-lingual sentence similarity and semantic search. Unlike larger LLMs, this model focuses specifically on mapping text from multiple languages into a shared vector space, making it an ideal choice for developers building RAG (Retrieval-Augmented Generation) pipelines or clustering systems where low latency and minimal memory overhead are critical. It balances performance with a small footprint, allowing for deployment on edge devices or CPU-only environments without sacrificing significant retrieval accuracy. Integration is straightforward via standard sentence-transformer libraries, providing a scalable alternative to proprietary embedding APIs for international applications.

Model typesentence similarity
Providerintfloat
Licensemit
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/intfloat/multilingual-e5-small
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Version informationUse the source repository for the latest version
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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: intfloat/multilingual-e5-small
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 intfloat/multilingual-e5-small
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 intfloat/multilingual-e5-small 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('intfloat/multilingual-e5-small')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/intfloat/multilingual-e5-small.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/intfloat/multilingual-e5-small.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.

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