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mradermacher/marco-mini-base-gguf overview

About static quants of https://huggingface.co/AIDC-AI/Marco-Mini-Base For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Marco-Mini-Base-i1-GGUF

transformersggufmoemixture-of-expertsmultilingualupcyclingenzhardeesfrkojapttriditnlplruvithheukmsbncsurkk
mradermacher/marco-mini-base-gguf visual
Downloads
1,356
Likes
0
Pipeline
Library
transformers
Visibility
Public
Access
Open

Repository Files & Downloads

11 files detected
Direct downloads for all repository files
FileTypeQuantizationSizeLink
Marco-Mini-Base.IQ4_XS.gguf GGUF IQ4_XS 8.77 GB Download
Marco-Mini-Base.Q2_K.gguf GGUF Q2_K 5.96 GB Download
Marco-Mini-Base.Q3_K_L.gguf GGUF Q3_K_L 8.43 GB Download
Marco-Mini-Base.Q3_K_M.gguf GGUF Q3_K_M 7.78 GB Download
Marco-Mini-Base.Q3_K_S.gguf GGUF Q3_K_S 7.05 GB Download
Marco-Mini-Base.Q4_K_M.gguf GGUF Q4_K_M 9.87 GB Download
Marco-Mini-Base.Q4_K_S.gguf GGUF Q4_K_S 9.26 GB Download
Marco-Mini-Base.Q5_K_M.gguf GGUF Q5_K_M 11.54 GB Download
Marco-Mini-Base.Q5_K_S.gguf GGUF Q5_K_S 11.19 GB Download
Marco-Mini-Base.Q6_K.gguf GGUF Q6_K 13.33 GB Download
Marco-Mini-Base.Q8_0.gguf GGUF 17.25 GB Download

Model Details Live

Model Slug
mradermacher/marco-mini-base-gguf
Author
mradermacher
Pipeline Task
Library
transformers
Created
2026-04-03
Last Modified
2026-04-04
Gated
No
Private
No
HF SHA
b677b1ee018475be12ab64d6fc50c66480e7d918
License
apache-2.0
Language
en, zh, ar, de, es, fr, ko, ja, pt, tr, id, it, nl, pl, ru, vi, th, he, uk, ms, bn, cs, ur, kk, el, ro, hu, ne, az
Base Model
AIDC-AI/Marco-Mini-Base

Metadata Inspector

Normalized metadata (stored in metadata_json)
{
  "metadata": {},
  "card_data": {
    "base_model": "AIDC-AI/Marco-Mini-Base",
    "datasets": [
      "nvidia/Nemotron-CC-v2",
      "nvidia/Nemotron-Pretraining-SFT-v1",
      "nvidia/Nemotron-Pretraining-Specialized-v1",
      "nvidia/Nemotron-CC-v2.1",
      "allenai/dolmino-mix-1124",
      "nvidia/Nemotron-CC-Math-v1",
      "nvidia/OpenMathInstruct-2",
      "HuggingFaceTB/finemath",
      "LLM360/MegaMath",
      "open-thoughts/OpenThoughts3-1.2M",
      "opencsg/Fineweb-Edu-Chinese-V2.1",
      "HuggingFaceFW/fineweb-2",
      "allenai/dolma3_dolmino_mix-100B-1125"
    ],
    "language": [
      "en",
      "zh",
      "ar",
      "de",
      "es",
      "fr",
      "ko",
      "ja",
      "pt",
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      "ru",
      "vi",
      "th",
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    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "moe",
      "mixture-of-experts",
      "multilingual",
      "upcycling"
    ],
    "frontmatter": {
      "base_model": "AIDC-AI/Marco-Mini-Base",
      "datasets": [
        "nvidia/Nemotron-CC-v2",
        "nvidia/Nemotron-Pretraining-SFT-v1",
        "nvidia/Nemotron-Pretraining-Specialized-v1",
        "nvidia/Nemotron-CC-v2.1",
        "allenai/dolmino-mix-1124",
        "nvidia/Nemotron-CC-Math-v1",
        "nvidia/OpenMathInstruct-2",
        "HuggingFaceTB/finemath",
        "LLM360/MegaMath",
        "open-thoughts/OpenThoughts3-1.2M",
        "opencsg/Fineweb-Edu-Chinese-V2.1",
        "HuggingFaceFW/fineweb-2",
        "allenai/dolma3_dolmino_mix-100B-1125"
      ],
      "language": [
        "en",
        "zh",
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      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "moe",
        "mixture-of-experts",
        "multilingual",
        "upcycling"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         static quants of https://huggingface.co/AIDC-AI/Marco-Mini-Base  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Marco-Mini-Base-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: AIDC-AI/Marco-Mini-Base\ndatasets:\n- nvidia/Nemotron-CC-v2\n- nvidia/Nemotron-Pretraining-SFT-v1\n- nvidia/Nemotron-Pretraining-Specialized-v1\n- nvidia/Nemotron-CC-v2.1\n- allenai/dolmino-mix-1124\n- nvidia/Nemotron-CC-Math-v1\n- nvidia/OpenMathInstruct-2\n- HuggingFaceTB/finemath\n- LLM360/MegaMath\n- open-thoughts/OpenThoughts3-1.2M\n- opencsg/Fineweb-Edu-Chinese-V2.1\n- HuggingFaceFW/fineweb-2\n- allenai/dolma3_dolmino_mix-100B-1125\nlanguage:\n- en\n- zh\n- ar\n- de\n- es\n- fr\n- ko\n- ja\n- pt\n- tr\n- id\n- it\n- nl\n- pl\n- ru\n- vi\n- th\n- he\n- uk\n- ms\n- bn\n- cs\n- ur\n- kk\n- el\n- ro\n- hu\n- ne\n- az\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- moe\n- mixture-of-experts\n- multilingual\n- upcycling\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\n<!-- ### quants:  x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nstatic quants of https://huggingface.co/AIDC-AI/Marco-Mini-Base\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#Marco-Mini-Base-GGUF).***\n\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/Marco-Mini-Base-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/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q2_K.gguf) | Q2_K | 6.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q3_K_S.gguf) | Q3_K_S | 7.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q3_K_M.gguf) | Q3_K_M | 8.5 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q3_K_L.gguf) | Q3_K_L | 9.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.IQ4_XS.gguf) | IQ4_XS | 9.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q4_K_S.gguf) | Q4_K_S | 10.0 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q4_K_M.gguf) | Q4_K_M | 10.7 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q5_K_S.gguf) | Q5_K_S | 12.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q5_K_M.gguf) | Q5_K_M | 12.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q6_K.gguf) | Q6_K | 14.4 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Base-GGUF/resolve/main/Marco-Mini-Base.Q8_0.gguf) | Q8_0 | 18.6 | fast, best quality |\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.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "moe",
    "mixture-of-experts",
    "multilingual",
    "upcycling",
    "en",
    "zh",
    "ar",
    "de",
    "es",
    "fr",
    "ko",
    "ja",
    "pt",
    "tr",
    "id",
    "it",
    "nl",
    "pl",
    "ru",
    "vi",
    "th",
    "he",
    "uk",
    "ms",
    "bn",
    "cs",
    "ur",
    "kk",
    "el",
    "ro",
    "hu",
    "ne",
    "az",
    "dataset:nvidia/Nemotron-CC-v2",
    "dataset:nvidia/Nemotron-Pretraining-SFT-v1",
    "dataset:nvidia/Nemotron-Pretraining-Specialized-v1",
    "dataset:nvidia/Nemotron-CC-v2.1",
    "dataset:allenai/dolmino-mix-1124",
    "dataset:nvidia/Nemotron-CC-Math-v1",
    "dataset:nvidia/OpenMathInstruct-2",
    "dataset:HuggingFaceTB/finemath",
    "dataset:LLM360/MegaMath",
    "dataset:open-thoughts/OpenThoughts3-1.2M",
    "dataset:opencsg/Fineweb-Edu-Chinese-V2.1",
    "dataset:HuggingFaceFW/fineweb-2",
    "dataset:allenai/dolma3_dolmino_mix-100B-1125",
    "base_model:AIDC-AI/Marco-Mini-Base",
    "base_model:quantized:AIDC-AI/Marco-Mini-Base",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 1356,
  "gated": false,
  "private": false,
  "last_modified": "2026-04-04T15:11:52.000Z",
  "created_at": "2026-04-03T18:05:30.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}
Source payload excerpt (from Hugging Face API)
{
  "_id": "69d0016abbf83b1a4b7ed79f",
  "id": "mradermacher/Marco-Mini-Base-GGUF",
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  "sha": "b677b1ee018475be12ab64d6fc50c66480e7d918",
  "createdAt": "2026-04-03T18:05:30.000Z",
  "lastModified": "2026-04-04T15:11:52.000Z",
  "author": "mradermacher",
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  "siblings_count": 13
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