mradermacher/qwen3-1.7b-shiningvaliant3-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "ValiantLabs/Qwen3-1.7B-ShiningValiant3",
    "datasets": [
      "sequelbox/Celestia3-DeepSeek-R1-0528",
      "sequelbox/Mitakihara-DeepSeek-R1-0528",
      "sequelbox/Raiden-DeepSeek-R1"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "shining-valiant",
      "shining-valiant-3",
      "valiant",
      "valiant-labs",
      "qwen",
      "qwen-3",
      "qwen-3-1.7b",
      "1.7b",
      "reasoning",
      "code",
      "code-reasoning",
      "science",
      "science-reasoning",
      "physics",
      "biology",
      "chemistry",
      "earth-science",
      "astronomy",
      "machine-learning",
      "artificial-intelligence",
      "compsci",
      "computer-science",
      "information-theory",
      "ML-Ops",
      "math",
      "cuda",
      "deep-learning",
      "transformers",
      "agentic",
      "LLM",
      "neuromorphic",
      "self-improvement",
      "complex-systems",
      "cognition",
      "linguistics",
      "philosophy",
      "logic",
      "epistemology",
      "simulation",
      "game-theory",
      "knowledge-management",
      "creativity",
      "problem-solving",
      "architect",
      "engineer",
      "developer",
      "creative",
      "analytical",
      "expert",
      "rationality",
      "conversational",
      "chat",
      "instruct"
    ],
    "frontmatter": {
      "base_model": "ValiantLabs/Qwen3-1.7B-ShiningValiant3",
      "datasets": [
        "sequelbox/Celestia3-DeepSeek-R1-0528",
        "sequelbox/Mitakihara-DeepSeek-R1-0528",
        "sequelbox/Raiden-DeepSeek-R1"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "shining-valiant",
        "shining-valiant-3",
        "valiant",
        "valiant-labs",
        "qwen",
        "qwen-3",
        "qwen-3-1.7b",
        "1.7b",
        "reasoning",
        "code",
        "code-reasoning",
        "science",
        "science-reasoning",
        "physics",
        "biology",
        "chemistry",
        "earth-science",
        "astronomy",
        "machine-learning",
        "artificial-intelligence",
        "compsci",
        "computer-science",
        "information-theory",
        "ML-Ops",
        "math",
        "cuda",
        "deep-learning",
        "transformers",
        "agentic",
        "LLM",
        "neuromorphic",
        "self-improvement",
        "complex-systems",
        "cognition",
        "linguistics",
        "philosophy",
        "logic",
        "epistemology",
        "simulation",
        "game-theory",
        "knowledge-management",
        "creativity",
        "problem-solving",
        "architect",
        "engineer",
        "developer",
        "creative",
        "analytical",
        "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/Qwen3-1.7B-ShiningValiant3  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: ValiantLabs/Qwen3-1.7B-ShiningValiant3\ndatasets:\n- sequelbox/Celestia3-DeepSeek-R1-0528\n- sequelbox/Mitakihara-DeepSeek-R1-0528\n- sequelbox/Raiden-DeepSeek-R1\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- shining-valiant\n- shining-valiant-3\n- valiant\n- valiant-labs\n- qwen\n- qwen-3\n- qwen-3-1.7b\n- 1.7b\n- reasoning\n- code\n- code-reasoning\n- science\n- science-reasoning\n- physics\n- biology\n- chemistry\n- earth-science\n- astronomy\n- machine-learning\n- artificial-intelligence\n- compsci\n- computer-science\n- information-theory\n- ML-Ops\n- math\n- cuda\n- deep-learning\n- transformers\n- agentic\n- LLM\n- neuromorphic\n- self-improvement\n- complex-systems\n- cognition\n- linguistics\n- philosophy\n- logic\n- epistemology\n- simulation\n- game-theory\n- knowledge-management\n- creativity\n- problem-solving\n- architect\n- engineer\n- developer\n- creative\n- analytical\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 -->\nweighted/imatrix quants of https://huggingface.co/ValiantLabs/Qwen3-1.7B-ShiningValiant3\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#Qwen3-1.7B-ShiningValiant3-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-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/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ1_S.gguf) | i1-IQ1_S | 0.6 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_S.gguf) | i1-IQ2_S | 0.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_M.gguf) | i1-IQ2_M | 0.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.8 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.9 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q2_K.gguf) | i1-Q2_K | 0.9 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_S.gguf) | i1-IQ3_S | 1.0 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.0 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_M.gguf) | i1-IQ3_M | 1.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.0 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.1 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.2 | prefer IQ4_XS |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_0.gguf) | i1-Q4_0 | 1.2 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.2 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.2 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_1.gguf) | i1-Q4_1 | 1.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q6_K.gguf) | i1-Q6_K | 1.5 | 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",
    "shining-valiant",
    "shining-valiant-3",
    "valiant",
    "valiant-labs",
    "qwen",
    "qwen-3",
    "qwen-3-1.7b",
    "1.7b",
    "reasoning",
    "code",
    "code-reasoning",
    "science",
    "science-reasoning",
    "physics",
    "biology",
    "chemistry",
    "earth-science",
    "astronomy",
    "machine-learning",
    "artificial-intelligence",
    "compsci",
    "computer-science",
    "information-theory",
    "ML-Ops",
    "math",
    "cuda",
    "deep-learning",
    "agentic",
    "LLM",
    "neuromorphic",
    "self-improvement",
    "complex-systems",
    "cognition",
    "linguistics",
    "philosophy",
    "logic",
    "epistemology",
    "simulation",
    "game-theory",
    "knowledge-management",
    "creativity",
    "problem-solving",
    "architect",
    "engineer",
    "developer",
    "creative",
    "analytical",
    "expert",
    "rationality",
    "conversational",
    "chat",
    "instruct",
    "en",
    "dataset:sequelbox/Celestia3-DeepSeek-R1-0528",
    "dataset:sequelbox/Mitakihara-DeepSeek-R1-0528",
    "dataset:sequelbox/Raiden-DeepSeek-R1",
    "base_model:ValiantLabs/Qwen3-1.7B-ShiningValiant3",
    "base_model:quantized:ValiantLabs/Qwen3-1.7B-ShiningValiant3",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix"
  ],
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  "last_modified": "2025-07-11T00:00:36.000Z",
  "created_at": "2025-07-09T02:01:29.000Z",
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  "library_name": "transformers"
}

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