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.
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
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| 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
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\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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"lastModified": "2025-09-11T04:13:50.000Z",
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