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mradermacher/qwen3-32b-guardpoint-i1-gguf overview

About weighted/imatrix quants of https://huggingface.co/ValiantLabs/Qwen3-32B-Guardpoint For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-GGUF

transformersggufguardpointvaliantvaliant-labsqwenqwen-3qwen-3-32b32breasoningsciencescience-reasoningmedicineinternal-medicineclinical-diagnosismedical-understandingmedical-reasoningmedical-diagnosismedical-managementproblem-solvinganatomyangiologybariatriccardiovasculardentaldermatologyendocrinologyENThematologyimmunology
mradermacher/qwen3-32b-guardpoint-i1-gguf visual
Downloads
369
Likes
2
Pipeline
Library
transformers
Visibility
Public
Access
Open

Repository Files & Downloads

24 files detected
Direct downloads for all repository files
FileTypeQuantizationSizeLink
Qwen3-32B-Guardpoint.i1-IQ1_M.gguf GGUF IQ1_M 7.41 GB Download
Qwen3-32B-Guardpoint.i1-IQ1_S.gguf GGUF IQ1_S 6.82 GB Download
Qwen3-32B-Guardpoint.i1-IQ2_M.gguf GGUF IQ2_M 10.58 GB Download
Qwen3-32B-Guardpoint.i1-IQ2_S.gguf GGUF IQ2_S 9.79 GB Download
Qwen3-32B-Guardpoint.i1-IQ2_XS.gguf GGUF IQ2_XS 9.27 GB Download
Qwen3-32B-Guardpoint.i1-IQ2_XXS.gguf GGUF IQ2_XXS 8.40 GB Download
Qwen3-32B-Guardpoint.i1-IQ3_M.gguf GGUF IQ3_M 13.90 GB Download
Qwen3-32B-Guardpoint.i1-IQ3_S.gguf GGUF IQ3_S 13.44 GB Download
Qwen3-32B-Guardpoint.i1-IQ3_XS.gguf GGUF IQ3_XS 12.76 GB Download
Qwen3-32B-Guardpoint.i1-IQ3_XXS.gguf GGUF IQ3_XXS 11.94 GB Download
Qwen3-32B-Guardpoint.i1-IQ4_XS.gguf GGUF IQ4_XS 16.48 GB Download
Qwen3-32B-Guardpoint.i1-Q2_K.gguf GGUF Q2_K 11.50 GB Download
Qwen3-32B-Guardpoint.i1-Q2_K_S.gguf GGUF Q2_K_S 10.68 GB Download
Qwen3-32B-Guardpoint.i1-Q3_K_L.gguf GGUF Q3_K_L 16.14 GB Download
Qwen3-32B-Guardpoint.i1-Q3_K_M.gguf GGUF Q3_K_M 14.87 GB Download
Qwen3-32B-Guardpoint.i1-Q3_K_S.gguf GGUF Q3_K_S 13.40 GB Download
Qwen3-32B-Guardpoint.i1-Q4_0.gguf GGUF 17.42 GB Download
Qwen3-32B-Guardpoint.i1-Q4_1.gguf GGUF 19.22 GB Download
Qwen3-32B-Guardpoint.i1-Q4_K_M.gguf GGUF Q4_K_M 18.40 GB Download
Qwen3-32B-Guardpoint.i1-Q4_K_S.gguf GGUF Q4_K_S 17.48 GB Download
Qwen3-32B-Guardpoint.i1-Q5_K_M.gguf GGUF Q5_K_M 21.62 GB Download
Qwen3-32B-Guardpoint.i1-Q5_K_S.gguf GGUF Q5_K_S 21.08 GB Download
Qwen3-32B-Guardpoint.i1-Q6_K.gguf GGUF Q6_K 25.04 GB Download
Qwen3-32B-Guardpoint.imatrix.gguf GGUF 14.57 MB Download

Model Details Live

Model Slug
mradermacher/qwen3-32b-guardpoint-i1-gguf
Author
mradermacher
Pipeline Task
Library
transformers
Created
2026-01-14
Last Modified
2026-01-14
Gated
No
Private
No
HF SHA
513c5f3cdff5f6b19ab69cac9166a7c75a57979b
License
apache-2.0
Language
en
Base Model
ValiantLabs/Qwen3-32B-Guardpoint

Metadata Inspector

Normalized metadata (stored in metadata_json)
{
  "metadata": {},
  "card_data": {
    "base_model": "ValiantLabs/Qwen3-32B-Guardpoint",
    "datasets": [
      "sequelbox/Superpotion-DeepSeek-V3.2-Speciale"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "guardpoint",
      "valiant",
      "valiant-labs",
      "qwen",
      "qwen-3",
      "qwen-3-32b",
      "32b",
      "reasoning",
      "science",
      "science-reasoning",
      "medicine",
      "internal-medicine",
      "clinical-diagnosis",
      "medical-understanding",
      "medical-reasoning",
      "medical-diagnosis",
      "medical-management",
      "problem-solving",
      "anatomy",
      "angiology",
      "bariatric",
      "cardiovascular",
      "dental",
      "dermatology",
      "endocrinology",
      "ENT",
      "hematology",
      "immunology",
      "infectious-disease",
      "musculoskeletal",
      "neurology",
      "obstetrics",
      "ophtamology",
      "oncology",
      "orthopedics",
      "pathology",
      "psychiatry",
      "pulmonology",
      "radiology",
      "surgery",
      "triage",
      "urology",
      "analytical",
      "data",
      "data-interpretation",
      "expert",
      "rationality",
      "conversational",
      "chat",
      "instruct"
    ],
    "frontmatter": {
      "base_model": "ValiantLabs/Qwen3-32B-Guardpoint",
      "datasets": [
        "sequelbox/Superpotion-DeepSeek-V3.2-Speciale"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "guardpoint",
        "valiant",
        "valiant-labs",
        "qwen",
        "qwen-3",
        "qwen-3-32b",
        "32b",
        "reasoning",
        "science",
        "science-reasoning",
        "medicine",
        "internal-medicine",
        "clinical-diagnosis",
        "medical-understanding",
        "medical-reasoning",
        "medical-diagnosis",
        "medical-management",
        "problem-solving",
        "anatomy",
        "angiology",
        "bariatric",
        "cardiovascular",
        "dental",
        "dermatology",
        "endocrinology",
        "ENT",
        "hematology",
        "immunology",
        "infectious-disease",
        "musculoskeletal",
        "neurology",
        "obstetrics",
        "ophtamology",
        "oncology",
        "orthopedics",
        "pathology",
        "psychiatry",
        "pulmonology",
        "radiology",
        "surgery",
        "triage",
        "urology",
        "analytical",
        "data",
        "data-interpretation",
        "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/ValiantLabs/Qwen3-32B-Guardpoint  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: ValiantLabs/Qwen3-32B-Guardpoint\ndatasets:\n- sequelbox/Superpotion-DeepSeek-V3.2-Speciale\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- guardpoint\n- valiant\n- valiant-labs\n- qwen\n- qwen-3\n- qwen-3-32b\n- 32b\n- reasoning\n- science\n- science-reasoning\n- medicine\n- internal-medicine\n- clinical-diagnosis\n- medical-understanding\n- medical-reasoning\n- medical-diagnosis\n- medical-management\n- problem-solving\n- anatomy\n- angiology\n- bariatric\n- cardiovascular\n- dental\n- dermatology\n- endocrinology\n- ENT\n- hematology\n- immunology\n- infectious-disease\n- musculoskeletal\n- neurology\n- obstetrics\n- ophtamology\n- oncology\n- orthopedics\n- pathology\n- psychiatry\n- pulmonology\n- radiology\n- surgery\n- triage\n- urology\n- analytical\n- data\n- data-interpretation\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/ValiantLabs/Qwen3-32B-Guardpoint\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#Qwen3-32B-Guardpoint-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-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/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Guardpoint-i1-GGUF/resolve/main/Qwen3-32B-Guardpoint.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",
    "guardpoint",
    "valiant",
    "valiant-labs",
    "qwen",
    "qwen-3",
    "qwen-3-32b",
    "32b",
    "reasoning",
    "science",
    "science-reasoning",
    "medicine",
    "internal-medicine",
    "clinical-diagnosis",
    "medical-understanding",
    "medical-reasoning",
    "medical-diagnosis",
    "medical-management",
    "problem-solving",
    "anatomy",
    "angiology",
    "bariatric",
    "cardiovascular",
    "dental",
    "dermatology",
    "endocrinology",
    "ENT",
    "hematology",
    "immunology",
    "infectious-disease",
    "musculoskeletal",
    "neurology",
    "obstetrics",
    "ophtamology",
    "oncology",
    "orthopedics",
    "pathology",
    "psychiatry",
    "pulmonology",
    "radiology",
    "surgery",
    "triage",
    "urology",
    "analytical",
    "data",
    "data-interpretation",
    "expert",
    "rationality",
    "conversational",
    "chat",
    "instruct",
    "en",
    "dataset:sequelbox/Superpotion-DeepSeek-V3.2-Speciale",
    "base_model:ValiantLabs/Qwen3-32B-Guardpoint",
    "base_model:quantized:ValiantLabs/Qwen3-32B-Guardpoint",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix"
  ],
  "likes": 2,
  "downloads": 369,
  "gated": false,
  "private": false,
  "last_modified": "2026-01-14T20:41:35.000Z",
  "created_at": "2026-01-14T16:05:41.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}
Source payload excerpt (from Hugging Face API)
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