ggml-org/embeddinggemma-300m-gguf Q8_0 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.
ggml-org/embeddinggemma-300m-gguf overview
embeddinggemma-300M GGUF Recommended way to run this model: Then the endpoint can be accessed at http://localhost:8080/embedding, for example using curl: Alternatively, the llama-embedding command line tool can be used: #### embdnormalize When a model uses pooling, or the pooling method is specified using --pooling, the normalization can be controlled by the embdnormalize 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: And for llama-embedding, by passing --embd-normalize , for example:
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| embeddinggemma-300M-Q8_0.gguf | GGUF | — | 313.36 MB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
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"base_model": [
"google/embeddinggemma-300M"
],
"frontmatter": {
"base_model": [
"google/embeddinggemma-300M"
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"summary": "# embeddinggemma-300M GGUF Recommended way to run this model: ``sh llama-server -hf ggml-org/embeddinggemma-300M-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-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-GGUF --embd-normalize -1 -p \"Hello embeddings\" ``",
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"readme_markdown": "---\nbase_model:\n- google/embeddinggemma-300M\n---\n# embeddinggemma-300M GGUF\n\nRecommended way to run this model:\n\n```sh\nllama-server -hf ggml-org/embeddinggemma-300M-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-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-GGUF --embd-normalize -1 -p \"Hello embeddings\"\n```\n",
"related_quantizations": []
},
"tags": [
"gguf",
"base_model:google/embeddinggemma-300m",
"base_model:quantized:google/embeddinggemma-300m",
"endpoints_compatible",
"region:us"
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"last_modified": "2025-09-04T16:19:45.000Z",
"created_at": "2025-09-04T16:14:00.000Z",
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Source payload excerpt (from Hugging Face API)
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"createdAt": "2025-09-04T16:14:00.000Z",
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