mradermacher/qwq-lcot-7b-instruct-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "prithivMLmods/QwQ-LCoT-7B-Instruct",
    "datasets": [
      "amphora/QwQ-LongCoT-130K"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "creativeml-openrail-m",
    "quantized_by": "mradermacher",
    "tags": [
      "Long-CoT",
      "Qwen2.5",
      "7B",
      "safetensors",
      "text-generation-inference",
      "QwQ",
      "SFT",
      "Math",
      "Qwen with Questions"
    ],
    "frontmatter": {
      "base_model": "prithivMLmods/QwQ-LCoT-7B-Instruct",
      "datasets": [
        "amphora/QwQ-LongCoT-130K"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "creativeml-openrail-m",
      "quantized_by": "mradermacher",
      "tags": [
        "Long-CoT",
        "Qwen2.5",
        "7B",
        "safetensors",
        "text-generation-inference",
        "QwQ",
        "SFT",
        "Math",
        "Qwen with Questions"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      weighted/imatrix quants of https://huggingface.co/prithivMLmods/QwQ-LCoT-7B-Instruct  static quants are available at https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: prithivMLmods/QwQ-LCoT-7B-Instruct\ndatasets:\n- amphora/QwQ-LongCoT-130K\nlanguage:\n- en\nlibrary_name: transformers\nlicense: creativeml-openrail-m\nquantized_by: mradermacher\ntags:\n- Long-CoT\n- Qwen2.5\n- 7B\n- safetensors\n- text-generation-inference\n- QwQ\n- SFT\n- Math\n- Qwen with Questions\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\nweighted/imatrix quants of https://huggingface.co/prithivMLmods/QwQ-LCoT-7B-Instruct\n\n<!-- provided-files -->\nstatic quants are available at https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-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/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/QwQ-LCoT-7B-Instruct-i1-GGUF/resolve/main/QwQ-LCoT-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | 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",
    "Long-CoT",
    "Qwen2.5",
    "7B",
    "safetensors",
    "text-generation-inference",
    "QwQ",
    "SFT",
    "Math",
    "Qwen with Questions",
    "en",
    "dataset:amphora/QwQ-LongCoT-130K",
    "base_model:prithivMLmods/QwQ-LCoT-7B-Instruct",
    "base_model:quantized:prithivMLmods/QwQ-LCoT-7B-Instruct",
    "license:creativeml-openrail-m",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 1,
  "downloads": 174,
  "gated": false,
  "private": false,
  "last_modified": "2024-12-25T14:02:28.000Z",
  "created_at": "2024-12-15T17:09:24.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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