mradermacher/multilingual-text-semantic-search-siamese-bert-v1-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1",
    "datasets": [
      "flax-sentence-embeddings/stackexchange_xml",
      "ms_marco",
      "gooaq",
      "yahoo_answers_topics",
      "search_qa",
      "eli5",
      "natural_questions",
      "trivia_qa",
      "embedding-data/QQP",
      "embedding-data/PAQ_pairs",
      "embedding-data/Amazon-QA",
      "embedding-data/WikiAnswers"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "sentence-transformers",
      "feature-extraction",
      "sentence-similarity"
    ],
    "frontmatter": {
      "base_model": "SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1",
      "datasets": [
        "flax-sentence-embeddings/stackexchange_xml",
        "ms_marco",
        "gooaq",
        "yahoo_answers_topics",
        "search_qa",
        "eli5",
        "natural_questions",
        "trivia_qa",
        "embedding-data/QQP",
        "embedding-data/PAQ_pairs",
        "embedding-data/Amazon-QA",
        "embedding-data/WikiAnswers"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "sentence-transformers",
        "feature-extraction",
        "sentence-similarity"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      weighted/imatrix quants of https://huggingface.co/SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1\ndatasets:\n- flax-sentence-embeddings/stackexchange_xml\n- ms_marco\n- gooaq\n- yahoo_answers_topics\n- search_qa\n- eli5\n- natural_questions\n- trivia_qa\n- embedding-data/QQP\n- embedding-data/PAQ_pairs\n- embedding-data/Amazon-QA\n- embedding-data/WikiAnswers\nlanguage:\n- en\nlibrary_name: transformers\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\nweighted/imatrix quants of https://huggingface.co/SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1\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#Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-GGUF\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/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ1_M.gguf) | i1-IQ1_M | 0.1 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_S.gguf) | i1-IQ2_S | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_M.gguf) | i1-IQ2_M | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.1 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.1 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_S.gguf) | i1-IQ3_S | 0.1 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q2_K.gguf) | i1-Q2_K | 0.1 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.1 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_M.gguf) | i1-IQ3_M | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.1 | prefer IQ4_XS |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_0.gguf) | i1-Q4_0 | 0.1 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.1 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_1.gguf) | i1-Q4_1 | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.1 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.1 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.1 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q6_K.gguf) | i1-Q6_K | 0.1 | practically like static Q6_K |\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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "sentence-transformers",
    "feature-extraction",
    "sentence-similarity",
    "en",
    "dataset:flax-sentence-embeddings/stackexchange_xml",
    "dataset:ms_marco",
    "dataset:gooaq",
    "dataset:yahoo_answers_topics",
    "dataset:search_qa",
    "dataset:eli5",
    "dataset:natural_questions",
    "dataset:trivia_qa",
    "dataset:embedding-data/QQP",
    "dataset:embedding-data/PAQ_pairs",
    "dataset:embedding-data/Amazon-QA",
    "dataset:embedding-data/WikiAnswers",
    "base_model:SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1",
    "base_model:quantized:SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1",
    "endpoints_compatible",
    "region:us",
    "imatrix"
  ],
  "likes": 0,
  "downloads": 170,
  "gated": false,
  "private": false,
  "last_modified": "2025-07-11T04:30:37.000Z",
  "created_at": "2025-04-25T10:10:21.000Z",
  "pipeline_tag": "feature-extraction",
  "library_name": "transformers"
}

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