mradermacher/bioinspiredllm-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "lamm-mit/BioinspiredLLM",
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "quantized_by": "mradermacher",
    "tags": [
      "biology",
      "materials science",
      "code",
      "scientific AI",
      "biological materials",
      "bioinspiration",
      "machine learning",
      "generative"
    ],
    "frontmatter": {
      "base_model": "lamm-mit/BioinspiredLLM",
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "quantized_by": "mradermacher",
      "tags": [
        "biology",
        "materials science",
        "code",
        "scientific AI",
        "biological materials",
        "bioinspiration",
        "machine learning",
        "generative"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      static quants of https://huggingface.co/lamm-mit/BioinspiredLLM  weighted/imatrix quants are available at https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: lamm-mit/BioinspiredLLM\nlanguage:\n- en\nlibrary_name: transformers\nquantized_by: mradermacher\ntags:\n- biology\n- materials science\n- code\n- scientific AI\n- biological materials\n- bioinspiration\n- machine learning\n- generative\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\nstatic quants of https://huggingface.co/lamm-mit/BioinspiredLLM\n\n<!-- provided-files -->\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/BioinspiredLLM-i1-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/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q2_K.gguf) | Q2_K | 5.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_S.gguf) | Q3_K_S | 5.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_L.gguf) | Q3_K_L | 7.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.IQ4_XS.gguf) | IQ4_XS | 7.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.5 | fast on arm, low quality |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q5_K_S.gguf) | Q5_K_S | 9.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q5_K_M.gguf) | Q5_K_M | 9.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q6_K.gguf) | Q6_K | 10.8 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |\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",
    "biology",
    "materials science",
    "code",
    "scientific AI",
    "biological materials",
    "bioinspiration",
    "machine learning",
    "generative",
    "en",
    "base_model:lamm-mit/BioinspiredLLM",
    "base_model:quantized:lamm-mit/BioinspiredLLM",
    "endpoints_compatible",
    "region:us"
  ],
  "likes": 1,
  "downloads": 118,
  "gated": false,
  "private": false,
  "last_modified": "2024-11-25T09:00:52.000Z",
  "created_at": "2024-11-22T23:58:59.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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  "id": "mradermacher/BioinspiredLLM-GGUF",
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  "createdAt": "2024-11-22T23:58:59.000Z",
  "lastModified": "2024-11-25T09:00:52.000Z",
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