Instructions to use rty813/embeddinggemma-2-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rty813/embeddinggemma-2-bf16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rty813/embeddinggemma-2-bf16")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModel

processor = AutoProcessor.from_pretrained("rty813/embeddinggemma-2-bf16")
model = AutoModel.from_pretrained("rty813/embeddinggemma-2-bf16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rty813/embeddinggemma-2-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rty813/embeddinggemma-2-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rty813/embeddinggemma-2-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rty813/embeddinggemma-2-bf16

SGLang

How to use rty813/embeddinggemma-2-bf16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rty813/embeddinggemma-2-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rty813/embeddinggemma-2-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rty813/embeddinggemma-2-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rty813/embeddinggemma-2-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rty813/embeddinggemma-2-bf16 with Docker Model Runner:
```
docker model run hf.co/rty813/embeddinggemma-2-bf16
```

embeddinggemma-2-bf16 (bf16)

Full-precision (bf16) PyTorch port of google/embeddinggemma-2, for vLLM / TGI / Xinference / transformers users who don't want the GGUF quant.

Format: pytorch_model.bin (bf16). safetensors conversion in progress.
Architecture: identical to the original — drop-in for AutoModelForCausalLM.from_pretrained("rty813/embeddinggemma-2-bf16").
Also loads in vLLM (vllm serve rty813/embeddinggemma-2-bf16) and Xinference (search "embeddinggemma-2").

Original model card:

license: apache-2.0 pipeline_tag: feature-extraction tags: - embedding - feature-extraction - sentence-transformers - multimodal-embedding - multimodal - vision - audio - video - image-feature-extraction - audio-feature-extraction - video-feature-extraction - sentence-similarity language: - multilingual library_name: transformers

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders.

Designed to run on consumer hardware such as mobile devi

Downloads last month: 72

Model tree for rty813/embeddinggemma-2-bf16

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google/embeddinggemma-2

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