New
v1.8.1
Today, Google releases EmbeddingGemma, a state-of-the-art multilingual embedding model perfect for on-device use cases. Designed for speed and efficiency, the model features a compact size of 308M parameters and a 2K context window, unlocking new possibilities for mobile RAG pipelines, agents, and more. EmbeddingGemma is trained to support over 100 languages and is the highest-ranking text-only multilingual embedding model under 500M on the Massive Text Embedding Benchmark (MTEB) at the time of writing.
- CPU:
docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-1.8.1 \
--model-id google/embeddinggemma-300m --dtype float32
- CPU with ONNX Runtime:
docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-1.8.1 \
--model-id onnx-community/embeddinggemma-300m-ONNX --dtype float32 --pooling mean