mradermacher/faro-yi-34b-dpo-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "wenbopan/Faro-Yi-34B-DPO",
    "datasets": [
      "wenbopan/Chinese-dpo-pairs",
      "Intel/orca_dpo_pairs",
      "argilla/ultrafeedback-binarized-preferences-cleaned",
      "jondurbin/truthy-dpo-v0.1"
    ],
    "language": [
      "en",
      "zh"
    ],
    "library_name": "transformers",
    "license": "mit",
    "quantized_by": "mradermacher",
    "frontmatter": {
      "base_model": "wenbopan/Faro-Yi-34B-DPO",
      "datasets": [
        "wenbopan/Chinese-dpo-pairs",
        "Intel/orca_dpo_pairs",
        "argilla/ultrafeedback-binarized-preferences-cleaned",
        "jondurbin/truthy-dpo-v0.1"
      ],
      "language": [
        "en",
        "zh"
      ],
      "library_name": "transformers",
      "license": "mit",
      "quantized_by": "mradermacher"
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About     weighted/imatrix quants of https://huggingface.co/wenbopan/Faro-Yi-34B-DPO  static quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: wenbopan/Faro-Yi-34B-DPO\ndatasets:\n- wenbopan/Chinese-dpo-pairs\n- Intel/orca_dpo_pairs\n- argilla/ultrafeedback-binarized-preferences-cleaned\n- jondurbin/truthy-dpo-v0.1\nlanguage:\n- en\n- zh\nlibrary_name: transformers\nlicense: mit\nquantized_by: mradermacher\n---\n## About\n\n<!-- ### quantize_version: 1 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type:  -->\n<!-- ### vocab_type:  -->\nweighted/imatrix quants of https://huggingface.co/wenbopan/Faro-Yi-34B-DPO\n\n<!-- provided-files -->\nstatic quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-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/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Faro-Yi-34B-DPO-i1-GGUF/resolve/main/Faro-Yi-34B-DPO.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | 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.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "en",
    "zh",
    "dataset:wenbopan/Chinese-dpo-pairs",
    "dataset:Intel/orca_dpo_pairs",
    "dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
    "dataset:jondurbin/truthy-dpo-v0.1",
    "base_model:wenbopan/Faro-Yi-34B-DPO",
    "base_model:quantized:wenbopan/Faro-Yi-34B-DPO",
    "license:mit",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 261,
  "gated": false,
  "private": false,
  "last_modified": "2024-05-06T05:00:11.000Z",
  "created_at": "2024-04-12T06:52:59.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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