mradermacher/bagel-7b-v0.4-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "jondurbin/bagel-7b-v0.4",
    "datasets": [
      "ai2_arc",
      "allenai/ultrafeedback_binarized_cleaned",
      "argilla/distilabel-intel-orca-dpo-pairs",
      "jondurbin/airoboros-3.2",
      "codeparrot/apps",
      "facebook/belebele",
      "bluemoon-fandom-1-1-rp-cleaned",
      "boolq",
      "camel-ai/biology",
      "camel-ai/chemistry",
      "camel-ai/math",
      "camel-ai/physics",
      "jondurbin/contextual-dpo-v0.1",
      "jondurbin/gutenberg-dpo-v0.1",
      "jondurbin/py-dpo-v0.1",
      "jondurbin/truthy-dpo-v0.1",
      "LDJnr/Capybara",
      "jondurbin/cinematika-v0.1",
      "WizardLM/WizardLM_evol_instruct_70k",
      "glaiveai/glaive-function-calling-v2",
      "jondurbin/gutenberg-dpo-v0.1",
      "grimulkan/LimaRP-augmented",
      "lmsys/lmsys-chat-1m",
      "ParisNeo/lollms_aware_dataset",
      "TIGER-Lab/MathInstruct",
      "Muennighoff/natural-instructions",
      "openbookqa",
      "kingbri/PIPPA-shareGPT",
      "piqa",
      "Vezora/Tested-22k-Python-Alpaca",
      "ropes",
      "cakiki/rosetta-code",
      "Open-Orca/SlimOrca",
      "b-mc2/sql-create-context",
      "squad_v2",
      "mattpscott/airoboros-summarization",
      "migtissera/Synthia-v1.3",
      "unalignment/toxic-dpo-v0.2",
      "WhiteRabbitNeo/WRN-Chapter-1",
      "WhiteRabbitNeo/WRN-Chapter-2",
      "winogrande"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "quantized_by": "mradermacher",
    "frontmatter": {
      "base_model": "jondurbin/bagel-7b-v0.4",
      "datasets": [
        "ai2_arc",
        "allenai/ultrafeedback_binarized_cleaned",
        "argilla/distilabel-intel-orca-dpo-pairs",
        "jondurbin/airoboros-3.2",
        "codeparrot/apps",
        "facebook/belebele",
        "bluemoon-fandom-1-1-rp-cleaned",
        "boolq",
        "camel-ai/biology",
        "camel-ai/chemistry",
        "camel-ai/math",
        "camel-ai/physics",
        "jondurbin/contextual-dpo-v0.1",
        "jondurbin/gutenberg-dpo-v0.1",
        "jondurbin/py-dpo-v0.1",
        "jondurbin/truthy-dpo-v0.1",
        "LDJnr/Capybara",
        "jondurbin/cinematika-v0.1",
        "WizardLM/WizardLM_evol_instruct_70k",
        "glaiveai/glaive-function-calling-v2",
        "jondurbin/gutenberg-dpo-v0.1",
        "grimulkan/LimaRP-augmented",
        "lmsys/lmsys-chat-1m",
        "ParisNeo/lollms_aware_dataset",
        "TIGER-Lab/MathInstruct",
        "Muennighoff/natural-instructions",
        "openbookqa",
        "kingbri/PIPPA-shareGPT",
        "piqa",
        "Vezora/Tested-22k-Python-Alpaca",
        "ropes",
        "cakiki/rosetta-code",
        "Open-Orca/SlimOrca",
        "b-mc2/sql-create-context",
        "squad_v2",
        "mattpscott/airoboros-summarization",
        "migtissera/Synthia-v1.3",
        "unalignment/toxic-dpo-v0.2",
        "WhiteRabbitNeo/WRN-Chapter-1",
        "WhiteRabbitNeo/WRN-Chapter-2",
        "winogrande"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "quantized_by": "mradermacher"
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-7b-v0.4  static quants are available at https://huggingface.co/mradermacher/bagel-7b-v0.4-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: jondurbin/bagel-7b-v0.4\ndatasets:\n- ai2_arc\n- allenai/ultrafeedback_binarized_cleaned\n- argilla/distilabel-intel-orca-dpo-pairs\n- jondurbin/airoboros-3.2\n- codeparrot/apps\n- facebook/belebele\n- bluemoon-fandom-1-1-rp-cleaned\n- boolq\n- camel-ai/biology\n- camel-ai/chemistry\n- camel-ai/math\n- camel-ai/physics\n- jondurbin/contextual-dpo-v0.1\n- jondurbin/gutenberg-dpo-v0.1\n- jondurbin/py-dpo-v0.1\n- jondurbin/truthy-dpo-v0.1\n- LDJnr/Capybara\n- jondurbin/cinematika-v0.1\n- WizardLM/WizardLM_evol_instruct_70k\n- glaiveai/glaive-function-calling-v2\n- jondurbin/gutenberg-dpo-v0.1\n- grimulkan/LimaRP-augmented\n- lmsys/lmsys-chat-1m\n- ParisNeo/lollms_aware_dataset\n- TIGER-Lab/MathInstruct\n- Muennighoff/natural-instructions\n- openbookqa\n- kingbri/PIPPA-shareGPT\n- piqa\n- Vezora/Tested-22k-Python-Alpaca\n- ropes\n- cakiki/rosetta-code\n- Open-Orca/SlimOrca\n- b-mc2/sql-create-context\n- squad_v2\n- mattpscott/airoboros-summarization\n- migtissera/Synthia-v1.3\n- unalignment/toxic-dpo-v0.2\n- WhiteRabbitNeo/WRN-Chapter-1\n- WhiteRabbitNeo/WRN-Chapter-2\n- winogrande\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nquantized_by: mradermacher\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\nweighted/imatrix quants of https://huggingface.co/jondurbin/bagel-7b-v0.4\n\n<!-- provided-files -->\nstatic quants are available at https://huggingface.co/mradermacher/bagel-7b-v0.4-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/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-7b-v0.4-i1-GGUF/resolve/main/bagel-7b-v0.4.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "en",
    "dataset:ai2_arc",
    "dataset:allenai/ultrafeedback_binarized_cleaned",
    "dataset:argilla/distilabel-intel-orca-dpo-pairs",
    "dataset:jondurbin/airoboros-3.2",
    "dataset:codeparrot/apps",
    "dataset:facebook/belebele",
    "dataset:bluemoon-fandom-1-1-rp-cleaned",
    "dataset:boolq",
    "dataset:camel-ai/biology",
    "dataset:camel-ai/chemistry",
    "dataset:camel-ai/math",
    "dataset:camel-ai/physics",
    "dataset:jondurbin/contextual-dpo-v0.1",
    "dataset:jondurbin/gutenberg-dpo-v0.1",
    "dataset:jondurbin/py-dpo-v0.1",
    "dataset:jondurbin/truthy-dpo-v0.1",
    "dataset:LDJnr/Capybara",
    "dataset:jondurbin/cinematika-v0.1",
    "dataset:WizardLM/WizardLM_evol_instruct_70k",
    "dataset:glaiveai/glaive-function-calling-v2",
    "dataset:grimulkan/LimaRP-augmented",
    "dataset:lmsys/lmsys-chat-1m",
    "dataset:ParisNeo/lollms_aware_dataset",
    "dataset:TIGER-Lab/MathInstruct",
    "dataset:Muennighoff/natural-instructions",
    "dataset:openbookqa",
    "dataset:kingbri/PIPPA-shareGPT",
    "dataset:piqa",
    "dataset:Vezora/Tested-22k-Python-Alpaca",
    "dataset:ropes",
    "dataset:cakiki/rosetta-code",
    "dataset:Open-Orca/SlimOrca",
    "dataset:b-mc2/sql-create-context",
    "dataset:squad_v2",
    "dataset:mattpscott/airoboros-summarization",
    "dataset:migtissera/Synthia-v1.3",
    "dataset:unalignment/toxic-dpo-v0.2",
    "dataset:WhiteRabbitNeo/WRN-Chapter-1",
    "dataset:WhiteRabbitNeo/WRN-Chapter-2",
    "dataset:winogrande",
    "base_model:jondurbin/bagel-7b-v0.4",
    "base_model:quantized:jondurbin/bagel-7b-v0.4",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 1,
  "downloads": 100,
  "gated": false,
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  "last_modified": "2024-11-02T09:33:08.000Z",
  "created_at": "2024-11-02T08:20:35.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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