ggml-org/embeddinggemma-300m-qat-q4_0-gguf overview

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    "license": "gemma",
    "tags": [
      "sentence-transformers",
      "sentence-similarity",
      "feature-extraction"
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    "base_model": [
      "google/embeddinggemma-300m-qat-q4_0-unquantized"
    ],
    "frontmatter": {
      "license": "gemma",
      "tags": [
        "sentence-transformers",
        "sentence-similarity",
        "feature-extraction"
      ],
      "base_model": [
        "google/embeddinggemma-300m-qat-q4_0-unquantized"
      ]
    },
    "hero_image_url": "",
    "summary": "# embeddinggemma-300M-qat-q4_0 GGUF Recommended way to run this model: ``sh llama-server -hf ggml-org/embeddinggemma-300M-qat-q4_0-GGUF --embeddings ` Then the endpoint can be accessed at http://localhost:8080/embedding, for example using curl: `console curl --request POST \\ --url http://localhost:8080/embedding \\ --header \"Content-Type: application/json\" \\ --data '{\"input\": \"Hello embeddings\"}' \\ --silent ` Alternatively, the llama-embedding command line tool can be used: `sh llama-embedding -hf ggml-org/embeddinggemma-300M-qat-q4_0-GGUF --verbose-prompt -p \"Hello embeddings\" ` #### embd_normalize When a model uses pooling, or the pooling method is specified using --pooling, the normalization can be controlled by the embd_normalize parameter. The default value is 2 which means that the embeddings are normalized using the Euclidean norm (L2). Other options are: * -1 No normalization *  0 Max absolute *  1 Taxicab *  2 Euclidean/L2 * \\>2 P-Norm This can be passed in the request body to llama-server, for example: `sh --data '{\"input\": \"Hello embeddings\", \"embd_normalize\": -1}' \\ ` And for llama-embedding, by passing --embd-normalize , for example: `sh llama-embedding -hf ggml-org/embeddinggemma-300M-qat-q4_0-GGUF  --embd-normalize -1 -p \"Hello embeddings\" ``",
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    "readme_markdown": "---\nlicense: gemma\ntags:\n  - sentence-transformers\n  - sentence-similarity\n  - feature-extraction\nbase_model:\n- google/embeddinggemma-300m-qat-q4_0-unquantized\n---\n# embeddinggemma-300M-qat-q4_0 GGUF\n\nRecommended way to run this model:\n\n```sh\nllama-server -hf ggml-org/embeddinggemma-300M-qat-q4_0-GGUF --embeddings\n```\n\nThen the endpoint can be accessed at http://localhost:8080/embedding, for\nexample using `curl`:\n```console\ncurl --request POST \\\n    --url http://localhost:8080/embedding \\\n    --header \"Content-Type: application/json\" \\\n    --data '{\"input\": \"Hello embeddings\"}' \\\n    --silent\n```\n\nAlternatively, the `llama-embedding` command line tool can be used:\n```sh\nllama-embedding -hf ggml-org/embeddinggemma-300M-qat-q4_0-GGUF --verbose-prompt -p \"Hello embeddings\"\n```\n\n#### embd_normalize\nWhen a model uses pooling, or the pooling method is specified using `--pooling`,\nthe normalization can be controlled by the `embd_normalize` parameter.\n\nThe default value is `2` which means that the embeddings are normalized using\nthe Euclidean norm (L2). Other options are:\n* -1 No normalization\n*  0 Max absolute\n*  1 Taxicab\n*  2 Euclidean/L2\n* \\>2 P-Norm\n\nThis can be passed in the request body to `llama-server`, for example:\n```sh\n    --data '{\"input\": \"Hello embeddings\", \"embd_normalize\": -1}' \\\n```\n\nAnd for `llama-embedding`, by passing `--embd-normalize <value>`, for example:\n```sh\nllama-embedding -hf ggml-org/embeddinggemma-300M-qat-q4_0-GGUF  --embd-normalize -1 -p \"Hello embeddings\"\n```",
    "related_quantizations": []
  },
  "tags": [
    "sentence-transformers",
    "gguf",
    "sentence-similarity",
    "feature-extraction",
    "base_model:google/embeddinggemma-300m-qat-q4_0-unquantized",
    "base_model:quantized:google/embeddinggemma-300m-qat-q4_0-unquantized",
    "license:gemma",
    "endpoints_compatible",
    "region:us"
  ],
  "likes": 5,
  "downloads": 781,
  "gated": false,
  "private": false,
  "last_modified": "2025-09-15T07:51:38.000Z",
  "created_at": "2025-09-04T16:14:48.000Z",
  "pipeline_tag": "feature-extraction",
  "library_name": "sentence-transformers"
}

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  "sha": "8dd0ca2a66a8f14470acb0e2a71f801afbc5fb73",
  "createdAt": "2025-09-04T16:14:48.000Z",
  "lastModified": "2025-09-15T07:51:38.000Z",
  "author": "ggml-org",
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  "pipeline_tag": "feature-extraction",
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