mradermacher/b1ade-1b-bf16-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "w601sxs/b1ade-1b-bf16",
    "datasets": [
      "kaist-ai/CoT-Collection"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "quantized_by": "mradermacher",
    "frontmatter": {
      "base_model": "w601sxs/b1ade-1b-bf16",
      "datasets": [
        "kaist-ai/CoT-Collection"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "quantized_by": "mradermacher"
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      static quants of https://huggingface.co/w601sxs/b1ade-1b-bf16  weighted/imatrix quants are available at https://huggingface.co/mradermacher/b1ade-1b-bf16-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: w601sxs/b1ade-1b-bf16\ndatasets:\n- kaist-ai/CoT-Collection\nlanguage:\n- en\nlibrary_name: transformers\nquantized_by: mradermacher\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/w601sxs/b1ade-1b-bf16\n\n<!-- provided-files -->\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/b1ade-1b-bf16-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/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q2_K.gguf) | Q2_K | 0.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q3_K_S.gguf) | Q3_K_S | 0.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.IQ4_XS.gguf) | IQ4_XS | 0.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q3_K_L.gguf) | Q3_K_L | 0.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q5_K_S.gguf) | Q5_K_S | 0.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q5_K_M.gguf) | Q5_K_M | 0.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q6_K.gguf) | Q6_K | 0.9 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.Q8_0.gguf) | Q8_0 | 1.2 | fast, best quality |\n| [GGUF](https://huggingface.co/mradermacher/b1ade-1b-bf16-GGUF/resolve/main/b1ade-1b-bf16.f16.gguf) | f16 | 2.1 | 16 bpw, overkill |\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",
    "en",
    "dataset:kaist-ai/CoT-Collection",
    "base_model:w601sxs/b1ade-1b-bf16",
    "base_model:quantized:w601sxs/b1ade-1b-bf16",
    "endpoints_compatible",
    "region:us"
  ],
  "likes": 0,
  "downloads": 174,
  "gated": false,
  "private": false,
  "last_modified": "2025-02-07T07:32:46.000Z",
  "created_at": "2025-02-07T05:37:45.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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