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mradermacher/embeddinggemma-300m-gguf Q3_K_L GGUF - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.

Model Intelligence Sheet

mradermacher/embeddinggemma-300m-gguf overview

About static quants of https://huggingface.co/google/embeddinggemma-300m For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

transformersggufsentence-transformerssentence-similarityfeature-extractiontext-embeddings-inferenceenbase_model:google/embeddinggemma-300mbase_model:quantized:google/embeddinggemma-300mlicense:gemmaendpoints_compatibleregion:us
mradermacher/embeddinggemma-300m-gguf visual
Downloads
245
Likes
1
Pipeline
feature-extraction
Library
transformers
Visibility
Public
Access
Open

Repository Files & Downloads

12 files detected
Direct downloads for all repository files
FileTypeQuantizationSizeLink
embeddinggemma-300m.IQ4_XS.gguf GGUF IQ4_XS 216.56 MB Download
embeddinggemma-300m.Q2_K.gguf GGUF Q2_K 202.38 MB Download
embeddinggemma-300m.Q3_K_L.gguf GGUF Q3_K_L 216.76 MB Download
embeddinggemma-300m.Q3_K_M.gguf GGUF Q3_K_M 213.34 MB Download
embeddinggemma-300m.Q3_K_S.gguf GGUF Q3_K_S 208.32 MB Download
embeddinggemma-300m.Q4_K_M.gguf GGUF Q4_K_M 225.39 MB Download
embeddinggemma-300m.Q4_K_S.gguf GGUF Q4_K_S 221.26 MB Download
embeddinggemma-300m.Q5_K_M.gguf GGUF Q5_K_M 235.30 MB Download
embeddinggemma-300m.Q5_K_S.gguf GGUF Q5_K_S 231.84 MB Download
embeddinggemma-300m.Q6_K.gguf GGUF Q6_K 248.33 MB Download
embeddinggemma-300m.Q8_0.gguf GGUF 313.36 MB Download
embeddinggemma-300m.f16.gguf GGUF F16 584.06 MB Download

Model Details Live

Model Slug
mradermacher/embeddinggemma-300m-gguf
Author
mradermacher
Pipeline Task
feature-extraction
Library
transformers
Created
2025-09-11
Last Modified
2025-09-11
Gated
No
Private
No
HF SHA
1779b0002a92a912f0dd46174353f3ea8957c4cc
License
gemma
Language
en
Base Model
google/embeddinggemma-300m

Metadata Inspector

Normalized metadata (stored in metadata_json)
{
  "metadata": {},
  "card_data": {
    "base_model": "google/embeddinggemma-300m",
    "extra_gated_button_content": "Acknowledge license",
    "extra_gated_heading": "Access EmbeddingGemma on Hugging Face",
    "extra_gated_prompt": "To access EmbeddingGemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately.",
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "gemma",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "sentence-transformers",
      "sentence-similarity",
      "feature-extraction",
      "text-embeddings-inference"
    ],
    "frontmatter": {
      "base_model": "google/embeddinggemma-300m",
      "extra_gated_button_content": "Acknowledge license",
      "extra_gated_heading": "Access EmbeddingGemma on Hugging Face",
      "extra_gated_prompt": "To access EmbeddingGemma on Hugging Face, you’re required to review",
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "gemma",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "sentence-transformers",
        "sentence-similarity",
        "feature-extraction",
        "text-embeddings-inference"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         static quants of https://huggingface.co/google/embeddinggemma-300m  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: google/embeddinggemma-300m\nextra_gated_button_content: Acknowledge license\nextra_gated_heading: Access EmbeddingGemma on Hugging Face\nextra_gated_prompt: To access EmbeddingGemma on Hugging Face, you’re required to review\n  and agree to Google’s usage license. To do this, please ensure you’re logged in\n  to Hugging Face and click below. Requests are processed immediately.\nlanguage:\n- en\nlibrary_name: transformers\nlicense: gemma\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- sentence-transformers\n- sentence-similarity\n- feature-extraction\n- text-embeddings-inference\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\n<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nstatic quants of https://huggingface.co/google/embeddinggemma-300m\n\n<!-- provided-files -->\n\n***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#embeddinggemma-300m-GGUF).***\n\nweighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.\n## Usage\n\nIf you are unsure how to use GGUF files, refer to one of [TheBloke's\nREADMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for\nmore details, including on how to concatenate multi-part files.\n\n## Provided Quants\n\n(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)\n\n| Link | Type | Size/GB | Notes |\n|:-----|:-----|--------:|:------|\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q2_K.gguf) | Q2_K | 0.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q3_K_S.gguf) | Q3_K_S | 0.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.IQ4_XS.gguf) | IQ4_XS | 0.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q3_K_L.gguf) | Q3_K_L | 0.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q5_K_S.gguf) | Q5_K_S | 0.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q5_K_M.gguf) | Q5_K_M | 0.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q6_K.gguf) | Q6_K | 0.4 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |\n| [GGUF](https://huggingface.co/mradermacher/embeddinggemma-300m-GGUF/resolve/main/embeddinggemma-300m.f16.gguf) | f16 | 0.7 | 16 bpw, overkill |\n\nHere is a handy graph by ikawrakow comparing some lower-quality quant\ntypes (lower is better):\n\n![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)\n\nAnd here are Artefact2's thoughts on the matter:\nhttps://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9\n\n## FAQ / Model Request\n\nSee https://huggingface.co/mradermacher/model_requests for some answers to\nquestions you might have and/or if you want some other model quantized.\n\n## Thanks\n\nI thank my company, [nethype GmbH](https://www.nethype.de/), for letting\nme use its servers and providing upgrades to my workstation to enable\nthis work in my free time.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "sentence-transformers",
    "sentence-similarity",
    "feature-extraction",
    "text-embeddings-inference",
    "en",
    "base_model:google/embeddinggemma-300m",
    "base_model:quantized:google/embeddinggemma-300m",
    "license:gemma",
    "endpoints_compatible",
    "region:us"
  ],
  "likes": 1,
  "downloads": 245,
  "gated": false,
  "private": false,
  "last_modified": "2025-09-11T04:13:50.000Z",
  "created_at": "2025-09-11T04:10:13.000Z",
  "pipeline_tag": "feature-extraction",
  "library_name": "transformers"
}
Source payload excerpt (from Hugging Face API)
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  "sha": "1779b0002a92a912f0dd46174353f3ea8957c4cc",
  "createdAt": "2025-09-11T04:10:13.000Z",
  "lastModified": "2025-09-11T04:13:50.000Z",
  "author": "mradermacher",
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