embeddinggemma 300m

Overview

Highlights

• Optimized for low-latency semantic search and RAG pipelines.
• Compact 300M parameter size reduces infrastructure overhead.
• High-performance vectorization for sentence similarity tasks.
• Seamless integration with standard vector database ecosystems.

Usage

# Install Hugging Face transformers
pip install transformers torch

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("google/embeddinggemma-300m")
tokenizer = AutoTokenizer.from_pretrained("google/embeddinggemma-300m")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download google/embeddinggemma-300m

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download google/embeddinggemma-300m config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('google/embeddinggemma-300m')

Git Download

Make sure git-lfs is installed first

git lfs install
git clone https://huggingface.co/google/embeddinggemma-300m

To skip LFS large-file downloads, use:

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/google/embeddinggemma-300m

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('google/embeddinggemma-300m')
tokenizer = AutoTokenizer.from_pretrained('google/embeddinggemma-300m')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

pip install modelscope

CLI Download

Download the full repository

modelscope download --model google/embeddinggemma-300m

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model google/embeddinggemma-300m README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('google/embeddinggemma-300m')

Git Download

Make sure git-lfs is installed first

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

To skip LFS large-file downloads, use:

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/google/embeddinggemma-300m.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'google/embeddinggemma-300m')