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MODEL Listed

embeddinggemma-300m

Embedding-Gemma-300M is a lightweight, high-efficiency embedding model designed for semantic search and sentence similarity tasks. Unlike massive LLMs, this model focuses on mapping text to a dense vector space, making it ideal for developers building RAG (Retrieval-Augmented Generation) pipelines where low latency and minimal memory overhead are critical. At 300M parameters, it offers a pragmatic balance between representational power and deployment costs, allowing for fast indexing and retrieval on commodity hardware. It integrates seamlessly into existing vector databases and is particularly effective for clustering, deduplication, and similarity-based filtering without the need for expensive GPU clusters.

googlesentence similarity
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01 / MODEL CARD

Model card

Embedding-Gemma-300M is a lightweight, high-efficiency embedding model designed for semantic search and sentence similarity tasks. Unlike massive LLMs, this model focuses on mapping text to a dense vector space, making it ideal for developers building RAG (Retrieval-Augmented Generation) pipelines where low latency and minimal memory overhead are critical. At 300M parameters, it offers a pragmatic balance between representational power and deployment costs, allowing for fast indexing and retrieval on commodity hardware. It integrates seamlessly into existing vector databases and is particularly effective for clustering, deduplication, and similarity-based filtering without the need for expensive GPU clusters.

Model typesentence similarity
Providergoogle
Licensegemma
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/google/embeddinggemma-300m
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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: google/embeddinggemma-300m
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 google/embeddinggemma-300m
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 google/embeddinggemma-300m 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('google/embeddinggemma-300m')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/google/embeddinggemma-300m.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/google/embeddinggemma-300m.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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