--- title: "embeddinggemma-2 GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/79f5ba28-1a82-41f8-85e6-959938229484" updated: "2026-11-13" --- # embeddinggemma-2 GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a GGUF copy of Google's embeddinggemma-2, a 271M feature extraction model, with the llama command or the Llama app. For developers who want a small text embedding model locally. embeddinggemma-2 GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ggml-org ## About ### What it is This is a GGUF copy of google/embeddinggemma-2, a 271M feature extraction model, made by ggml-org. By its name it is a text embedding model. A tool converted it automatically, so it runs in the llama tools. ### Who it suits It suits developers who want a small embedding model on their own machine, for search or similarity features. The card gives few other details. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run an embedding model locally with the llama command | A local search feature that needs text vectors | | Research | Retrieval researchers | Try a small GGUF embedding model | Compare it with other local embedders | | IT | Teams that keep data in-house | Host embeddings on their own hardware | An internal document search | If you need a chat model that writes text, you can skip it. ### Where to use it - The Llama app - The llama command in a terminal on Windows, macOS or Linux - Python on the same machines, for downloading the files by hand Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Local embeddings | Developers | A model served on your machine | | Retrieval experiments | Researchers | A small model to test search ideas with | ### How to set it up - The Llama app or the llama command - Python, only if you download the files by hand 1. Install the Llama app or the llama command. 2. Run the serve command below. It fetches the model by its Hugging Face name. 3. Optionally download the files by hand with the Hugging Face tools. ```bash llama serve -hf ggml-org/embeddinggemma-2-GGUF ``` ```sh pip install -U huggingface_hub hf download ggml-org/embeddinggemma-2-GGUF ``` ### How to use it Run the serve command in a terminal, or open the model in the Llama app. The card shows nothing beyond that, so check the llama tools for how to send text to a running server. ```bash llama serve -hf ggml-org/embeddinggemma-2-GGUF ``` ### Example You run the serve command in a terminal. The tool fetches the model from Hugging Face and starts it on your machine. The card shows no sample request or output, so what comes back depends on how you call the server. ### For newcomers and experts If you are new, an embedding is a list of numbers that stands for the meaning of a text. The first small thing to try is the serve command above. If you know the field, this is the original embeddinggemma-2 weights converted to GGUF, at 271M parameters, which is about 136 MB at 4-bit by Hugging Face's estimate. The card gives no dimension count or benchmark, so look at the source model for those. Run llama serve with the model name → Files come from Hugging Face → The model starts on your machine → Send text and read the vectors ### Good to know - Conversion: an automatic tool from ggml-org made this copy from the source model. - Source: the base model is google/embeddinggemma-2. - Detail: the card has no scores, sizes of the vectors or usage notes. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: you download the model from Hugging Face, and the card names no other access. ## README ````markdown # embeddinggemma-2 GGUF Runs a GGUF copy of Google's embeddinggemma-2, a 271M feature extraction model, with the llama command or the Llama app. For developers who want a small text embedding model locally. Made by ggml-org on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a GGUF copy of google/embeddinggemma-2, a 271M feature extraction model, made by ggml-org. By its name it is a text embedding model. A tool converted it automatically, so it runs in the llama tools. Who it suits: It suits developers who want a small embedding model on their own machine, for search or similarity features. The card gives few other details. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Software | Developers | Run an embedding model locally with the llama command | A local search feature that needs text vectors Research | Retrieval researchers | Try a small GGUF embedding model | Compare it with other local embedders IT | Teams that keep data in-house | Host embeddings on their own hardware | An internal document search ``` If you need a chat model that writes text, you can skip it. Where to use it: - The Llama app - The llama command in a terminal on Windows, macOS or Linux - Python on the same machines, for downloading the files by hand ```table Title: Jobs it does Use case | Who | What they get back Local embeddings | Developers | A model served on your machine Retrieval experiments | Researchers | A small model to test search ideas with ``` How to set it up: - The Llama app or the llama command - Python, only if you download the files by hand 1. Install the Llama app or the llama command. 2. Run the serve command below. It fetches the model by its Hugging Face name. 3. Optionally download the files by hand with the Hugging Face tools. ```bash llama serve -hf ggml-org/embeddinggemma-2-GGUF ``` ```sh pip install -U huggingface_hub hf download ggml-org/embeddinggemma-2-GGUF ``` How to use it: Run the serve command in a terminal, or open the model in the Llama app. The card shows nothing beyond that, so check the llama tools for how to send text to a running server. ```bash llama serve -hf ggml-org/embeddinggemma-2-GGUF ``` Example: You run the serve command in a terminal. The tool fetches the model from Hugging Face and starts it on your machine. The card shows no sample request or output, so what comes back depends on how you call the server. For newcomers and experts: If you are new, an embedding is a list of numbers that stands for the meaning of a text. The first small thing to try is the serve command above. If you know the field, this is the original embeddinggemma-2 weights converted to GGUF, at 271M parameters, which is about 136 MB at 4-bit by Hugging Face's estimate. The card gives no dimension count or benchmark, so look at the source model for those. ```flow Run llama serve with the model name Files come from Hugging Face The model starts on your machine Send text and read the vectors ``` Good to know: - Conversion: an automatic tool from ggml-org made this copy from the source model. - Source: the base model is google/embeddinggemma-2. - Detail: the card has no scores, sizes of the vectors or usage notes. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: you download the model from Hugging Face, and the card names no other access. ## On Hugging Face, 13 November 2026 - Task: feature extraction - Parameters: 271M - Weights: about 136 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 125,632 - Likes: 60 - License: apache-2.0 - Based on: google/embeddinggemma-2 - Fine-tunes: 1 - Tags: gguf, quantized, embedding, feature-extraction ## Links - Model: https://huggingface.co/ggml-org/embeddinggemma-2-GGUF - Files: https://huggingface.co/ggml-org/embeddinggemma-2-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ggml-org/embeddinggemma-2-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ggml-org/embeddinggemma-2-GGUF on Hugging Face](https://huggingface.co/ggml-org/embeddinggemma-2-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | embeddinggemma-2 GGUF (this page) | 125,632 | 60 | 271M | apache-2.0 | 2026-11-01 | | [embeddinggemma-2-GGUF](https://mindket.com/p/1116e8df-1d0d-45af-a2f7-55766b25a501) | 96,479 | 371 | 271M | apache-2.0 | 2026-10-07 | | [embeddinggemma-2](https://mindket.com/p/400f8dd4-de3d-480c-b595-2db8566715dc) | 503,609 | 3,347 | 744M | apache-2.0 | 2026-10-28 | | [MiniCPM-OCR Jev GGUF](https://mindket.com/p/925e0e80-eaca-47f8-8edc-b26e2ba06f7c) | 31,316 | 22 | 1.9B | apache-2.0 | 2026-11-02 | | [Laya-GGUF](https://mindket.com/p/ce86ea17-f8a2-43ac-b4ed-5814f58345c3) | 16,039 | 41 | 421M | apache-2.0 | 2026-11-12 | | [Qwen3.8-35B-A3B-Distill-GGUF](https://mindket.com/p/d707b0c8-89b8-4c4f-8d49-2ff67de613c7) | 1,337,097 | 277 | 35.5B | apache-2.0 | 2026-09-16 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-01 - First published: 2026-10-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ggml-org/embeddinggemma-2-GGUF on Hugging Face](https://huggingface.co/ggml-org/embeddinggemma-2-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/79f5ba28-1a82-41f8-85e6-959938229484) - [Its page on Mindket](https://mindket.com/p/79f5ba28-1a82-41f8-85e6-959938229484)