mradermacher/deepseek-v2-13b-math7k-expert-enhance-subset-expert-moe-32-experts-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts",
    "datasets": "HectorHe/math7k",
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "model_name": "Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "generated_from_trainer",
      "open-r1",
      "trl",
      "expert_enhance_distillation"
    ],
    "frontmatter": {
      "base_model": "HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts",
      "datasets": "HectorHe/math7k",
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "model_name": "Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "generated_from_trainer",
        "open-r1",
        "trl",
        "expert_enhance_distillation"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         static quants of https://huggingface.co/HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts\ndatasets: HectorHe/math7k\nlanguage:\n- en\nlibrary_name: transformers\nmodel_name: Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- generated_from_trainer\n- open-r1\n- trl\n- expert_enhance_distillation\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/HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts\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#Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF).***\n\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-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/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q2_K.gguf) | Q2_K | 6.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q3_K_S.gguf) | Q3_K_S | 7.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q3_K_M.gguf) | Q3_K_M | 8.2 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q3_K_L.gguf) | Q3_K_L | 8.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.IQ4_XS.gguf) | IQ4_XS | 8.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q4_K_S.gguf) | Q4_K_S | 9.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q4_K_M.gguf) | Q4_K_M | 10.5 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q5_K_S.gguf) | Q5_K_S | 11.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q5_K_M.gguf) | Q5_K_M | 12.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q6_K.gguf) | Q6_K | 14.2 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-GGUF/resolve/main/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts.Q8_0.gguf) | Q8_0 | 16.8 | 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",
    "generated_from_trainer",
    "open-r1",
    "trl",
    "expert_enhance_distillation",
    "en",
    "dataset:HectorHe/math7k",
    "base_model:HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts",
    "base_model:quantized:HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 1,
  "downloads": 89,
  "gated": false,
  "private": false,
  "last_modified": "2025-08-19T23:36:37.000Z",
  "created_at": "2025-08-19T15:59:46.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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