Model reference · open weights
embeddinggemma is an open-weight embedding model from unsloth, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
EmbeddingGemma model card Model Page: EmbeddingGemma Resources and Technical Documentation: Responsible Generative AI Toolkit EmbeddingGemma on Kaggle EmbeddingGemma on Vertex Model Garden Terms of Use: Terms Authors: Google DeepMind Model Information Description EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages. The small size and on-device focus makes it possible to deploy in environments with limited resources such as mobile phones, laptops, or desktops, democratizing access to state of the art AI models and helping foster innovation for everyone. For more technical details, refer to our paper: EmbeddingGemma: Powerful and Lightweight Text Representations. Inputs and outputs - Input: - Text string, such as a question, a prompt, or a document to be embedded - Maximum input context length of 2048 tokens - Output: - Numerical vector representations of input text data - Output embedding dimension size of 768, with smaller options available (512, 256, or 128) via Matryoshka Representation Learning (MRL). MRL allows users to truncate the output embedding of size 768 to their desired size and then re-normalize for efficient and accurate representation. Citation Usage These model weights are designed to be used with Sentence Transformers, using the Gemma 3 implementation from Hugging Face Transformers as the backbone. First install the Sentence Transformers library: Then you can load this model and run inference. NOTE: EmbeddingGemma activations do not support float16. Please use float32 or bfloat16 as appropriate for your hardware. Model Data Training Dataset This model was trained on a dataset of text data that includes a wide variety of sources totaling approximately 320 billion tokens. Here are the key components: - Web Documents: A diverse collection of web text en
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | unsloth |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 303M |
| Context | 2k tokens |
| Variants | 2 |
| Runs with | sentence-transformers |
| Released | 2025-09-04 |
| Popularity | 29k downloads / month |
| Likes | 87 |
| Licence | Open, with conditions |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys unsloth-embeddinggemma for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (unsloth-embeddinggemma below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"unsloth-embeddinggemma","input":"text to embed"}'
Licence
Open weights under gemma, which carries conditions (e.g. attribution or an acceptable-use clause). Worth a read before production use — we can walk you through it. Read the licence ↗
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