mradermacher/qwen3.5-27b-dag-reasoning-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "sequelbox/Qwen3.5-27B-DAG-Reasoning",
    "datasets": [
      "sequelbox/DAG-Reasoning-DeepSeek-R1-0528"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "dag-reasoning",
      "valiant",
      "valiant-labs",
      "qwen",
      "qwen-3.5",
      "qwen-3.5-27b",
      "27b",
      "reasoning",
      "directed-acyclic-graph",
      "graph",
      "logic",
      "analysis",
      "programming",
      "knowledge",
      "root-cause-analysis",
      "economics",
      "business",
      "business-management",
      "finance",
      "law",
      "supply-chain",
      "logistics",
      "software-engineering",
      "cybersecurity",
      "architecture",
      "energy",
      "politics",
      "problem-solving",
      "creative",
      "analytical",
      "expert",
      "rationality",
      "conversational",
      "chat",
      "instruct"
    ],
    "frontmatter": {
      "base_model": "sequelbox/Qwen3.5-27B-DAG-Reasoning",
      "datasets": [
        "sequelbox/DAG-Reasoning-DeepSeek-R1-0528"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "dag-reasoning",
        "valiant",
        "valiant-labs",
        "qwen",
        "qwen-3.5",
        "qwen-3.5-27b",
        "27b",
        "reasoning",
        "directed-acyclic-graph",
        "graph",
        "logic",
        "analysis",
        "programming",
        "knowledge",
        "root-cause-analysis",
        "economics",
        "business",
        "business-management",
        "finance",
        "law",
        "supply-chain",
        "logistics",
        "software-engineering",
        "cybersecurity",
        "architecture",
        "energy",
        "politics",
        "problem-solving",
        "creative",
        "analytical",
        "expert",
        "rationality",
        "conversational",
        "chat",
        "instruct"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         weighted/imatrix quants of https://huggingface.co/sequelbox/Qwen3.5-27B-DAG-Reasoning  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: sequelbox/Qwen3.5-27B-DAG-Reasoning\ndatasets:\n- sequelbox/DAG-Reasoning-DeepSeek-R1-0528\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- dag-reasoning\n- valiant\n- valiant-labs\n- qwen\n- qwen-3.5\n- qwen-3.5-27b\n- 27b\n- reasoning\n- directed-acyclic-graph\n- graph\n- logic\n- analysis\n- programming\n- knowledge\n- root-cause-analysis\n- economics\n- business\n- business-management\n- finance\n- law\n- supply-chain\n- logistics\n- software-engineering\n- cybersecurity\n- architecture\n- energy\n- politics\n- problem-solving\n- creative\n- analytical\n- expert\n- rationality\n- conversational\n- chat\n- instruct\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/sequelbox/Qwen3.5-27B-DAG-Reasoning\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#Qwen3.5-27B-DAG-Reasoning-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-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/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ1_M.gguf) | i1-IQ1_M | 7.7 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ2_S.gguf) | i1-IQ2_S | 9.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ2_M.gguf) | i1-IQ2_M | 10.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q2_K_S.gguf) | i1-Q2_K_S | 10.3 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q2_K.gguf) | i1-Q2_K | 10.8 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 11.3 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ3_XS.gguf) | i1-IQ3_XS | 12.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.2 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ3_S.gguf) | i1-IQ3_S | 12.5 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ3_M.gguf) | i1-IQ3_M | 12.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.4 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q3_K_L.gguf) | i1-Q3_K_L | 14.4 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-IQ4_XS.gguf) | i1-IQ4_XS | 15.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q4_0.gguf) | i1-Q4_0 | 15.6 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q4_K_S.gguf) | i1-Q4_K_S | 15.7 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q4_K_M.gguf) | i1-Q4_K_M | 16.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q4_1.gguf) | i1-Q4_1 | 17.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q5_K_S.gguf) | i1-Q5_K_S | 18.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.i1-Q5_K_M.gguf) | i1-Q5_K_M | 19.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF/resolve/main/Qwen3.5-27B-DAG-Reasoning.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",
    "dag-reasoning",
    "valiant",
    "valiant-labs",
    "qwen",
    "qwen-3.5",
    "qwen-3.5-27b",
    "27b",
    "reasoning",
    "directed-acyclic-graph",
    "graph",
    "logic",
    "analysis",
    "programming",
    "knowledge",
    "root-cause-analysis",
    "economics",
    "business",
    "business-management",
    "finance",
    "law",
    "supply-chain",
    "logistics",
    "software-engineering",
    "cybersecurity",
    "architecture",
    "energy",
    "politics",
    "problem-solving",
    "creative",
    "analytical",
    "expert",
    "rationality",
    "conversational",
    "chat",
    "instruct",
    "en",
    "dataset:sequelbox/DAG-Reasoning-DeepSeek-R1-0528",
    "base_model:sequelbox/Qwen3.5-27B-DAG-Reasoning",
    "base_model:quantized:sequelbox/Qwen3.5-27B-DAG-Reasoning",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix"
  ],
  "likes": 1,
  "downloads": 251,
  "gated": false,
  "private": false,
  "last_modified": "2026-03-13T15:59:24.000Z",
  "created_at": "2026-03-13T11:42:26.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

{
  "_id": "69b3f8223140975e0411568d",
  "id": "mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF",
  "modelId": "mradermacher/Qwen3.5-27B-DAG-Reasoning-i1-GGUF",
  "sha": "9ba1208adc69c53440f74add447da6bdf23bd031",
  "createdAt": "2026-03-13T11:42:26.000Z",
  "lastModified": "2026-03-13T15:59:24.000Z",
  "author": "mradermacher",
  "downloads": 251,
  "likes": 1,
  "gated": false,
  "private": false,
  "pipeline_tag": "",
  "library_name": "transformers",
  "siblings_count": 26
}