legraphista/datagemma-rag-27b-it-imat-gguf overview

{
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  "card_data": {
    "base_model": "google/datagemma-rag-27b-it",
    "extra_gated_button_content": "Acknowledge license",
    "extra_gated_heading": "Access Gemma on Hugging Face",
    "extra_gated_prompt": "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately.",
    "inference": false,
    "library_name": "gguf",
    "license": "gemma",
    "pipeline_tag": "text-generation",
    "quantized_by": "legraphista",
    "tags": [
      "conversational",
      "quantized",
      "GGUF",
      "quantization",
      "imat",
      "imatrix",
      "static",
      "16bit",
      "8bit",
      "6bit",
      "5bit",
      "4bit",
      "3bit",
      "2bit",
      "1bit"
    ],
    "frontmatter": {
      "base_model": "google/datagemma-rag-27b-it",
      "extra_gated_button_content": "Acknowledge license",
      "extra_gated_heading": "Access Gemma on Hugging Face",
      "extra_gated_prompt": "\"To access Gemma on Hugging Face, you\\u2019re required to review\\",
      "inference": "false",
      "library_name": "gguf",
      "license": "gemma",
      "pipeline_tag": "text-generation",
      "quantized_by": "legraphista",
      "tags": [
        "conversational",
        "quantized",
        "GGUF",
        "quantization",
        "imat",
        "imatrix",
        "static",
        "16bit",
        "8bit",
        "6bit",
        "5bit",
        "4bit",
        "3bit",
        "2bit",
        "1bit"
      ]
    },
    "hero_image_url": "",
    "summary": "_Llama.cpp imatrix quantization of google/datagemma-rag-27b-it_ Original Model: google/datagemma-rag-27b-it Original dtype: BF16 (bfloat16) Quantized by:  llama.cpp b3750 IMatrix dataset: here ---",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: google/datagemma-rag-27b-it\nextra_gated_button_content: Acknowledge license\nextra_gated_heading: Access Gemma on Hugging Face\nextra_gated_prompt: \"To access Gemma on Hugging Face, you\\u2019re required to review\\\n  \\ and agree to Google\\u2019s usage license. To do this, please ensure you\\u2019\\\n  re logged in to Hugging Face and click below. Requests are processed immediately.\"\ninference: false\nlibrary_name: gguf\nlicense: gemma\npipeline_tag: text-generation\nquantized_by: legraphista\ntags:\n- conversational\n- quantized\n- GGUF\n- quantization\n- imat\n- imatrix\n- static\n- 16bit\n- 8bit\n- 6bit\n- 5bit\n- 4bit\n- 3bit\n- 2bit\n- 1bit\n---\n\n# datagemma-rag-27b-it-IMat-GGUF\n_Llama.cpp imatrix quantization of google/datagemma-rag-27b-it_\n\nOriginal Model: [google/datagemma-rag-27b-it](https://huggingface.co/google/datagemma-rag-27b-it)    \nOriginal dtype: `BF16` (`bfloat16`)  \nQuantized by:  llama.cpp [b3750](https://github.com/ggerganov/llama.cpp/releases/tag/b3750)  \nIMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)  \n\n- [Files](#files)\n    - [IMatrix](#imatrix)\n    - [Common Quants](#common-quants)\n    - [All Quants](#all-quants)\n- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)\n- [Inference](#inference)\n    - [Simple chat template](#simple-chat-template)\n    - [Llama.cpp](#llama-cpp)\n- [FAQ](#faq)\n    - [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)\n    - [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)\n\n---\n\n## Files\n\n### IMatrix\nStatus: ✅ Available  \nLink: [here](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/imatrix.dat)\n\n### Common Quants\n| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |\n| -------- | ---------- | --------- | ------ | ------------ | -------- |\n| [datagemma-rag-27b-it.Q8_0.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q8_0.gguf) | Q8_0 | 28.94GB | ✅ Available | ⚪ Static | 📦 No\n| [datagemma-rag-27b-it.Q6_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q6_K.gguf) | Q6_K | 22.34GB | ✅ Available | ⚪ Static | 📦 No\n| [datagemma-rag-27b-it.Q4_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q4_K.gguf) | Q4_K | 16.65GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q3_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K.gguf) | Q3_K | 13.42GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q2_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q2_K.gguf) | Q2_K | 10.45GB | ✅ Available | 🟢 IMatrix | 📦 No\n\n\n### All Quants\n| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |\n| -------- | ---------- | --------- | ------ | ------------ | -------- |\n| [datagemma-rag-27b-it.BF16/*](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/tree/main/datagemma-rag-27b-it.BF16) | BF16 | 54.46GB | ✅ Available | ⚪ Static | ✂ Yes\n| [datagemma-rag-27b-it.FP16/*](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/tree/main/datagemma-rag-27b-it.FP16) | F16 | 54.46GB | ✅ Available | ⚪ Static | ✂ Yes\n| [datagemma-rag-27b-it.Q8_0.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q8_0.gguf) | Q8_0 | 28.94GB | ✅ Available | ⚪ Static | 📦 No\n| [datagemma-rag-27b-it.Q6_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q6_K.gguf) | Q6_K | 22.34GB | ✅ Available | ⚪ Static | 📦 No\n| [datagemma-rag-27b-it.Q5_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q5_K.gguf) | Q5_K | 19.41GB | ✅ Available | ⚪ Static | 📦 No\n| [datagemma-rag-27b-it.Q5_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q5_K_S.gguf) | Q5_K_S | 18.88GB | ✅ Available | ⚪ Static | 📦 No\n| [datagemma-rag-27b-it.Q4_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q4_K.gguf) | Q4_K | 16.65GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q4_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q4_K_S.gguf) | Q4_K_S | 15.74GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ4_NL.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ4_NL.gguf) | IQ4_NL | 15.63GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ4_XS.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ4_XS.gguf) | IQ4_XS | 14.81GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q3_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K.gguf) | Q3_K | 13.42GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q3_K_L.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K_L.gguf) | Q3_K_L | 14.52GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q3_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K_S.gguf) | Q3_K_S | 12.17GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ3_M.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ3_M.gguf) | IQ3_M | 12.45GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ3_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ3_S.gguf) | IQ3_S | 12.17GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ3_XS.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ3_XS.gguf) | IQ3_XS | 11.55GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ3_XXS.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ3_XXS.gguf) | IQ3_XXS | 10.75GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q2_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q2_K.gguf) | Q2_K | 10.45GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.Q2_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q2_K_S.gguf) | Q2_K_S | 9.72GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ2_M.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ2_M.gguf) | IQ2_M | 9.40GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ2_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ2_S.gguf) | IQ2_S | 8.65GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ2_XS.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ2_XS.gguf) | IQ2_XS | 8.40GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ2_XXS.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ2_XXS.gguf) | IQ2_XXS | 7.63GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ1_M.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ1_M.gguf) | IQ1_M | 6.69GB | ✅ Available | 🟢 IMatrix | 📦 No\n| [datagemma-rag-27b-it.IQ1_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.IQ1_S.gguf) | IQ1_S | 6.13GB | ✅ Available | 🟢 IMatrix | 📦 No\n\n\n## Downloading using huggingface-cli\nIf you do not have hugginface-cli installed:\n```\npip install -U \"huggingface_hub[cli]\"\n```\nDownload the specific file you want:\n```\nhuggingface-cli download legraphista/datagemma-rag-27b-it-IMat-GGUF --include \"datagemma-rag-27b-it.Q8_0.gguf\" --local-dir ./\n```\nIf the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:\n```\nhuggingface-cli download legraphista/datagemma-rag-27b-it-IMat-GGUF --include \"datagemma-rag-27b-it.Q8_0/*\" --local-dir ./\n# see FAQ for merging GGUF's\n```\n\n---\n\n## Inference\n\n### Simple chat template\n```\n<bos><start_of_turn>user\n{user_prompt}<end_of_turn>\n<start_of_turn>model\n{assistant_response}<end_of_turn>\n<start_of_turn>user\n{next_user_prompt}<end_of_turn>\n\n```\n\n### Llama.cpp\n```\nllama.cpp/main -m datagemma-rag-27b-it.Q8_0.gguf --color -i -p \"prompt here (according to the chat template)\"\n```\n\n---\n\n## FAQ\n\n### Why is the IMatrix not applied everywhere?\nAccording to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). \n\n### How do I merge a split GGUF?\n1. Make sure you have `gguf-split` available\n    - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases\n    - Download the appropriate zip for your system from the latest release\n    - Unzip the archive and you should be able to find `gguf-split`\n2. Locate your GGUF chunks folder (ex: `datagemma-rag-27b-it.Q8_0`)\n3. Run `gguf-split --merge datagemma-rag-27b-it.Q8_0/datagemma-rag-27b-it.Q8_0-00001-of-XXXXX.gguf datagemma-rag-27b-it.Q8_0.gguf`\n    - Make sure to point `gguf-split` to the first chunk of the split.\n\n---\n\nGot a suggestion? Ping me [@legraphista](https://x.com/legraphista)!",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "conversational",
    "quantized",
    "GGUF",
    "quantization",
    "imat",
    "imatrix",
    "static",
    "16bit",
    "8bit",
    "6bit",
    "5bit",
    "4bit",
    "3bit",
    "2bit",
    "1bit",
    "text-generation",
    "base_model:google/datagemma-rag-27b-it",
    "base_model:quantized:google/datagemma-rag-27b-it",
    "license:gemma",
    "region:us"
  ],
  "likes": 0,
  "downloads": 186,
  "gated": false,
  "private": false,
  "last_modified": "2024-09-13T15:49:47.000Z",
  "created_at": "2024-09-12T13:33:41.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "gguf"
}

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  "createdAt": "2024-09-12T13:33:41.000Z",
  "lastModified": "2024-09-13T15:49:47.000Z",
  "author": "legraphista",
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