mradermacher/marco-nano-base-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "AIDC-AI/Marco-Nano-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",
      "tr",
      "id",
      "it",
      "nl",
      "pl",
      "ru",
      "vi",
      "th",
      "he",
      "uk",
      "ms",
      "bn",
      "cs",
      "ur",
      "kk",
      "el",
      "ro",
      "hu",
      "ne",
      "az"
    ],
    "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-Nano-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",
        "tr",
        "id",
        "it",
        "nl",
        "pl",
        "ru",
        "vi",
        "th",
        "he",
        "uk",
        "ms",
        "bn",
        "cs",
        "ur",
        "kk",
        "el",
        "ro",
        "hu",
        "ne",
        "az"
      ],
      "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         weighted/imatrix quants of https://huggingface.co/AIDC-AI/Marco-Nano-Base  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: AIDC-AI/Marco-Nano-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: 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/AIDC-AI/Marco-Nano-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-Nano-Base-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/Marco-Nano-Base-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-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.imatrix.gguf) | imatrix | 0.2 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 2.8 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ1_M.gguf) | i1-IQ1_M | 2.9 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ2_S.gguf) | i1-IQ2_S | 3.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ2_M.gguf) | i1-IQ2_M | 3.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q2_K.gguf) | i1-Q2_K | 3.5 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.5 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.8 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ3_S.gguf) | i1-IQ3_S | 4.0 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.0 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ3_M.gguf) | i1-IQ3_M | 4.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.4 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.5 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q4_K_S.gguf) | i1-Q4_K_S | 5.1 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q4_1.gguf) | i1-Q4_1 | 5.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-i1-GGUF/resolve/main/Marco-Nano-Base.i1-Q6_K.gguf) | i1-Q6_K | 7.4 | 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",
    "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-Nano-Base",
    "base_model:quantized:AIDC-AI/Marco-Nano-Base",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 1,
  "downloads": 5140,
  "gated": false,
  "private": false,
  "last_modified": "2026-04-03T17:19:20.000Z",
  "created_at": "2026-04-03T16:05:35.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

{
  "_id": "69cfe54f548c567a3b8e2b19",
  "id": "mradermacher/Marco-Nano-Base-i1-GGUF",
  "modelId": "mradermacher/Marco-Nano-Base-i1-GGUF",
  "sha": "012ee2b014dfa22b5554d0be34306d06e98b65f0",
  "createdAt": "2026-04-03T16:05:35.000Z",
  "lastModified": "2026-04-03T17:19:20.000Z",
  "author": "mradermacher",
  "downloads": 5140,
  "likes": 1,
  "gated": false,
  "private": false,
  "pipeline_tag": "",
  "library_name": "transformers",
  "siblings_count": 27
}