mradermacher/llama-traditional-chinese-120m-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "p208p2002/llama-traditional-chinese-120M",
    "datasets": [
      "wikipedia",
      "p208p2002/wudao",
      "c4"
    ],
    "language": [
      "zh",
      "en"
    ],
    "library_name": "transformers",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "chinese",
      "english",
      "generate",
      "gpt2",
      "llama"
    ],
    "frontmatter": {
      "base_model": "p208p2002/llama-traditional-chinese-120M",
      "datasets": [
        "wikipedia",
        "p208p2002/wudao",
        "c4"
      ],
      "language": [
        "zh",
        "en"
      ],
      "library_name": "transformers",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "chinese",
        "english",
        "generate",
        "gpt2",
        "llama"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      static quants of https://huggingface.co/p208p2002/llama-traditional-chinese-120M  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: p208p2002/llama-traditional-chinese-120M\ndatasets:\n- wikipedia\n- p208p2002/wudao\n- c4\nlanguage:\n- zh\n- en\nlibrary_name: transformers\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- chinese\n- english\n- generate\n- gpt2\n- llama\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\nstatic quants of https://huggingface.co/p208p2002/llama-traditional-chinese-120M\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#llama-traditional-chinese-120M-GGUF).***\n\nweighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.\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/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q2_K.gguf) | Q2_K | 0.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q3_K_S.gguf) | Q3_K_S | 0.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q3_K_L.gguf) | Q3_K_L | 0.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.IQ4_XS.gguf) | IQ4_XS | 0.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q5_K_S.gguf) | Q5_K_S | 0.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q5_K_M.gguf) | Q5_K_M | 0.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q6_K.gguf) | Q6_K | 0.2 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |\n| [GGUF](https://huggingface.co/mradermacher/llama-traditional-chinese-120M-GGUF/resolve/main/llama-traditional-chinese-120M.f16.gguf) | f16 | 0.3 | 16 bpw, overkill |\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",
    "chinese",
    "english",
    "generate",
    "gpt2",
    "llama",
    "zh",
    "en",
    "dataset:wikipedia",
    "dataset:p208p2002/wudao",
    "dataset:c4",
    "base_model:p208p2002/llama-traditional-chinese-120M",
    "base_model:quantized:p208p2002/llama-traditional-chinese-120M",
    "endpoints_compatible",
    "region:us"
  ],
  "likes": 0,
  "downloads": 150,
  "gated": false,
  "private": false,
  "last_modified": "2025-07-11T07:23:55.000Z",
  "created_at": "2025-03-31T18:15:02.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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  "createdAt": "2025-03-31T18:15:02.000Z",
  "lastModified": "2025-07-11T07:23:55.000Z",
  "author": "mradermacher",
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