mradermacher/kenichi-thinking-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "odytrice/kenichi-thinking",
    "datasets": [
      "odytrice/kenichi-sft"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "text-generation-inference",
      "peft",
      "qwen3_5",
      "code",
      "vision-language",
      "fsharp",
      "svelte",
      "typescript",
      "dotnet",
      "docker",
      "kubernetes"
    ],
    "frontmatter": {
      "base_model": "odytrice/kenichi-thinking",
      "datasets": [
        "odytrice/kenichi-sft"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "text-generation-inference",
        "peft",
        "qwen3_5",
        "code",
        "vision-language",
        "fsharp",
        "svelte",
        "typescript",
        "dotnet",
        "docker",
        "kubernetes"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         weighted/imatrix quants of https://huggingface.co/odytrice/kenichi-thinking  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/kenichi-thinking-GGUF **This is a vision model - mmproj files (if any) will be in the static repository.**",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: odytrice/kenichi-thinking\ndatasets:\n- odytrice/kenichi-sft\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- text-generation-inference\n- peft\n- qwen3_5\n- code\n- vision-language\n- fsharp\n- svelte\n- typescript\n- dotnet\n- docker\n- kubernetes\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\n<!-- ### quants:  Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj: 1 -->\nweighted/imatrix quants of https://huggingface.co/odytrice/kenichi-thinking\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#kenichi-thinking-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/kenichi-thinking-GGUF\n\n**This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/kenichi-thinking-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/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ1_M.gguf) | i1-IQ1_M | 7.7 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ2_S.gguf) | i1-IQ2_S | 9.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ2_M.gguf) | i1-IQ2_M | 10.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q2_K_S.gguf) | i1-Q2_K_S | 10.3 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q2_K.gguf) | i1-Q2_K | 10.8 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 11.3 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ3_XS.gguf) | i1-IQ3_XS | 12.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.2 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ3_S.gguf) | i1-IQ3_S | 12.5 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ3_M.gguf) | i1-IQ3_M | 12.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.4 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q3_K_L.gguf) | i1-Q3_K_L | 14.4 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-IQ4_XS.gguf) | i1-IQ4_XS | 15.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q4_0.gguf) | i1-Q4_0 | 15.6 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q4_K_S.gguf) | i1-Q4_K_S | 15.7 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q4_K_M.gguf) | i1-Q4_K_M | 16.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q4_1.gguf) | i1-Q4_1 | 17.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q5_K_S.gguf) | i1-Q5_K_S | 18.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q5_K_M.gguf) | i1-Q5_K_M | 19.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/kenichi-thinking-i1-GGUF/resolve/main/kenichi-thinking.i1-Q6_K.gguf) | i1-Q6_K | 22.2 | 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",
    "text-generation-inference",
    "peft",
    "qwen3_5",
    "code",
    "vision-language",
    "fsharp",
    "svelte",
    "typescript",
    "dotnet",
    "docker",
    "kubernetes",
    "en",
    "dataset:odytrice/kenichi-sft",
    "base_model:odytrice/kenichi-thinking",
    "base_model:quantized:odytrice/kenichi-thinking",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 0,
  "downloads": 2486,
  "gated": false,
  "private": false,
  "last_modified": "2026-03-26T15:02:02.000Z",
  "created_at": "2026-03-26T13:09:46.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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  "author": "mradermacher",
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