prithivmlmods/gliese-4b-oss-0410-gguf overview

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      "prithivMLmods/Gliese-4B-OSS-0410"
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    "pipeline_tag": "text-generation",
    "library_name": "transformers",
    "tags": [
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      "math"
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    "frontmatter": {
      "license": "apache-2.0",
      "language": [
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      "base_model": [
        "prithivMLmods/Gliese-4B-OSS-0410"
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      "pipeline_tag": "text-generation",
      "library_name": "transformers",
      "tags": [
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    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "> Gliese-4B-OSS-0410 is a reasoning-focused model fine-tuned on Qwen-4B for enhanced reasoning and polished token probability distributions, delivering balanced multilingual generation across mathematics and general-purpose reasoning tasks. The model is fine-tuned on curated GPT-OSS synthetic dataset entries, improving its ability to handle structured reasoning, probabilistic inference, and multilingual tasks with precision.",
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    "readme_markdown": "---\nlicense: apache-2.0\nlanguage:\n- en\n- zh\nbase_model:\n- prithivMLmods/Gliese-4B-OSS-0410\npipeline_tag: text-generation\nlibrary_name: transformers\ntags:\n- text-generation-inference\n- gpt_oss\n- code\n- math\n---\n\n# **Gliese-4B-OSS-0410-GGUF**\n\n> Gliese-4B-OSS-0410 is a reasoning-focused model fine-tuned on Qwen-4B for enhanced reasoning and polished token probability distributions, delivering balanced multilingual generation across mathematics and general-purpose reasoning tasks. The model is fine-tuned on curated GPT-OSS synthetic dataset entries, improving its ability to handle structured reasoning, probabilistic inference, and multilingual tasks with precision.\n\n## Model Files\n\n| File Name | Quant Type | File Size |\n| - | - | - |\n| Gliese-4B-OSS-0410.BF16.gguf | BF16 | 8.05 GB |\n| Gliese-4B-OSS-0410.F16.gguf | F16 | 8.05 GB |\n| Gliese-4B-OSS-0410.F32.gguf | F32 | 16.1 GB |\n| Gliese-4B-OSS-0410.Q2_K.gguf | Q2_K | 1.67 GB |\n| Gliese-4B-OSS-0410.Q3_K_L.gguf | Q3_K_L | 2.24 GB |\n| Gliese-4B-OSS-0410.Q3_K_M.gguf | Q3_K_M | 2.08 GB |\n| Gliese-4B-OSS-0410.Q3_K_S.gguf | Q3_K_S | 1.89 GB |\n| Gliese-4B-OSS-0410.Q4_K_M.gguf | Q4_K_M | 2.5 GB |\n| Gliese-4B-OSS-0410.Q4_K_S.gguf | Q4_K_S | 2.38 GB |\n| Gliese-4B-OSS-0410.Q5_K_M.gguf | Q5_K_M | 2.89 GB |\n| Gliese-4B-OSS-0410.Q5_K_S.gguf | Q5_K_S | 2.82 GB |\n| Gliese-4B-OSS-0410.Q6_K.gguf | Q6_K | 3.31 GB |\n| Gliese-4B-OSS-0410.Q8_0.gguf | Q8_0 | 4.28 GB |\n\n## Quants Usage \n\n(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)\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)",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "qwen3",
    "text-generation-inference",
    "gpt_oss",
    "code",
    "math",
    "text-generation",
    "en",
    "zh",
    "base_model:prithivMLmods/Gliese-4B-OSS-0410",
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    "license:apache-2.0",
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  "downloads": 450,
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  "last_modified": "2025-10-05T18:11:26.000Z",
  "created_at": "2025-10-04T15:00:58.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
}

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