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embeddinggemma-300m-GGUF
by unsloth · source Hugging Face · updated 2025-09-04
gemma265 MB~1 GB RAMsource aliveunlabeled
Responsible Generative AI Toolkit EmbeddingGemma on Kaggle EmbeddingGemma on Vertex Model Garden
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Source and license
- Source: https://huggingface.co/unsloth/embeddinggemma-300m-GGUF
- License: gemma (restricted license: terms at the source apply)
- Requirements: about 1 GB of RAM, 265 MB on disk (estimate: suggested file size × 1.15 + 0.5 GB; a real measurement comes with lsh models). Runs with llama.cpp.
- Tags:
sentence-transformersggufsentence-similarityfeature-extractiontext-embeddings-inferenceendpoints_compatible
Numbers
- 14,122 downloads on Hugging Face
- 88 likes
- license gemma
- 0.3 GB for embeddinggemma-300m-Q4_0.gguf
- 327,838 npm downloads a week for node-llama-cpp
- latest node-llama-cpp@3.22.1
Numbers as of 2026-10-02 20:53 UTC, from the source API.
Summary
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Files
| file | quant | size |
|---|---|---|
embeddinggemma-300M-BF16.gguf | BF16 | 584 MB |
embeddinggemma-300M-F32.gguf | F32 | 1.1 GB |
embeddinggemma-300M-Q8_0.gguf | Q8_0 | 313 MB |
embeddinggemma-300m-Q4_0.gguf | Q4_0 | 265 MB |
From the source README
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.
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
Card id model:hf:unsloth/embeddinggemma-300m-GGUF · collected 2026-10-02 20:53 UTC · JSON