mradermacher/fluentlyqwen2.5-32b-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "fluently/FluentlyQwen2.5-32B",
    "datasets": [
      "fluently-sets/ultraset",
      "fluently-sets/ultrathink",
      "fluently-sets/reasoning-1-1k",
      "fluently-sets/MATH-500-Overall"
    ],
    "language": [
      "en",
      "fr",
      "es",
      "ru",
      "zh",
      "ja",
      "fa",
      "code"
    ],
    "library_name": "transformers",
    "license": "mit",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "fluently-lm",
      "fluently",
      "prinum",
      "instruct",
      "trained",
      "math",
      "roleplay",
      "reasoning",
      "axolotl",
      "unsloth",
      "argilla",
      "qwen2"
    ],
    "frontmatter": {
      "base_model": "fluently/FluentlyQwen2.5-32B",
      "datasets": [
        "fluently-sets/ultraset",
        "fluently-sets/ultrathink",
        "fluently-sets/reasoning-1-1k",
        "fluently-sets/MATH-500-Overall"
      ],
      "language": [
        "en",
        "fr",
        "es",
        "ru",
        "zh",
        "ja",
        "fa",
        "code"
      ],
      "library_name": "transformers",
      "license": "mit",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "fluently-lm",
        "fluently",
        "prinum",
        "instruct",
        "trained",
        "math",
        "roleplay",
        "reasoning",
        "axolotl",
        "unsloth",
        "argilla",
        "qwen2"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         weighted/imatrix quants of https://huggingface.co/fluently/FluentlyQwen2.5-32B  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: fluently/FluentlyQwen2.5-32B\ndatasets:\n- fluently-sets/ultraset\n- fluently-sets/ultrathink\n- fluently-sets/reasoning-1-1k\n- fluently-sets/MATH-500-Overall\nlanguage:\n- en\n- fr\n- es\n- ru\n- zh\n- ja\n- fa\n- code\nlibrary_name: transformers\nlicense: mit\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- fluently-lm\n- fluently\n- prinum\n- instruct\n- trained\n- math\n- roleplay\n- reasoning\n- axolotl\n- unsloth\n- argilla\n- qwen2\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:  -->\nweighted/imatrix quants of https://huggingface.co/fluently/FluentlyQwen2.5-32B\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#FluentlyQwen2.5-32B-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-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/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/FluentlyQwen2.5-32B-i1-GGUF/resolve/main/FluentlyQwen2.5-32B.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | 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",
    "fluently-lm",
    "fluently",
    "prinum",
    "instruct",
    "trained",
    "math",
    "roleplay",
    "reasoning",
    "axolotl",
    "unsloth",
    "argilla",
    "qwen2",
    "en",
    "fr",
    "es",
    "ru",
    "zh",
    "ja",
    "fa",
    "code",
    "dataset:fluently-sets/ultraset",
    "dataset:fluently-sets/ultrathink",
    "dataset:fluently-sets/reasoning-1-1k",
    "dataset:fluently-sets/MATH-500-Overall",
    "base_model:fluently/FluentlyQwen2.5-32B",
    "base_model:quantized:fluently/FluentlyQwen2.5-32B",
    "license:mit",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 1,
  "downloads": 326,
  "gated": false,
  "private": false,
  "last_modified": "2025-12-31T22:41:42.000Z",
  "created_at": "2025-09-08T19:33:19.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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