prithivmlmods/chinda-qwen3-4b-f32-gguf overview

{
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  "card_data": {
    "license": "apache-2.0",
    "language": [
      "en",
      "th"
    ],
    "base_model": [
      "iapp/chinda-qwen3-4b"
    ],
    "pipeline_tag": "text-generation",
    "library_name": "transformers",
    "tags": [
      "text-generation-inference"
    ],
    "frontmatter": {
      "license": "apache-2.0",
      "language": [
        "en",
        "th"
      ],
      "base_model": [
        "iapp/chinda-qwen3-4b"
      ],
      "pipeline_tag": "text-generation",
      "library_name": "transformers",
      "tags": [
        "text-generation-inference"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "> Chinda Opensource Thai LLM 4B is iApp Technology's cutting-edge Thai language model that brings advanced thinking capabilities to the Thai AI ecosystem. Built on the latest Qwen3-4B architecture, Chinda represents our commitment to developing sovereign AI solutions for Thailand.",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: apache-2.0\nlanguage:\n- en\n- th\nbase_model:\n- iapp/chinda-qwen3-4b\npipeline_tag: text-generation\nlibrary_name: transformers\ntags:\n- text-generation-inference\n---\n\n# **Chinda-Qwen3-4B-F32-GGUF**\n\n> Chinda Opensource Thai LLM 4B is iApp Technology's cutting-edge Thai language model that brings advanced thinking capabilities to the Thai AI ecosystem. Built on the latest Qwen3-4B architecture, Chinda represents our commitment to developing sovereign AI solutions for Thailand.\n\n## Model Files\n\n| File | Size | Format |\n|------|------|--------|\n| Chinda-Qwen3-4B-F32.F32.gguf | 16.1 GB | 32-bit float |\n| Chinda-Qwen3-4B-F32.BF16.gguf | 8.05 GB | BFloat16 |\n| Chinda-Qwen3-4B-F32.F16.gguf | 8.05 GB | 16-bit float |\n| Chinda-Qwen3-4B-F32.Q8_0.gguf | 4.28 GB | 8-bit quantized |\n| Chinda-Qwen3-4B-F32.Q6_K.gguf | 3.31 GB | 6-bit quantized |\n| Chinda-Qwen3-4B-F32.Q5_K_M.gguf | 2.89 GB | 5-bit quantized (medium) |\n| Chinda-Qwen3-4B-F32.Q5_K_S.gguf | 2.82 GB | 5-bit quantized (small) |\n| Chinda-Qwen3-4B-F32.Q4_K_M.gguf | 2.5 GB | 4-bit quantized (medium) |\n| Chinda-Qwen3-4B-F32.Q4_K_S.gguf | 2.38 GB | 4-bit quantized (small) |\n| Chinda-Qwen3-4B-F32.Q3_K_L.gguf | 2.24 GB | 3-bit quantized (large) |\n| Chinda-Qwen3-4B-F32.Q3_K_M.gguf | 2.08 GB | 3-bit quantized (medium) |\n| Chinda-Qwen3-4B-F32.Q3_K_S.gguf | 1.89 GB | 3-bit quantized (small) |\n| Chinda-Qwen3-4B-F32.Q2_K.gguf | 1.67 GB | 2-bit quantized |\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",
    "text-generation",
    "en",
    "th",
    "base_model:iapp/chinda-qwen3-4b",
    "base_model:quantized:iapp/chinda-qwen3-4b",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 2,
  "downloads": 170,
  "gated": false,
  "private": false,
  "last_modified": "2025-07-09T06:54:01.000Z",
  "created_at": "2025-07-09T04:47:42.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
}

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  "id": "prithivMLmods/Chinda-Qwen3-4B-F32-GGUF",
  "modelId": "prithivMLmods/Chinda-Qwen3-4B-F32-GGUF",
  "sha": "3dd756300896d3a4ef37110690a9dfded7f3c6ef",
  "createdAt": "2025-07-09T04:47:42.000Z",
  "lastModified": "2025-07-09T06:54:01.000Z",
  "author": "prithivMLmods",
  "downloads": 170,
  "likes": 2,
  "gated": false,
  "private": false,
  "pipeline_tag": "text-generation",
  "library_name": "transformers",
  "siblings_count": 16
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