mradermacher/bagel-8x7b-v0.2-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "jondurbin/bagel-8x7b-v0.2",
    "datasets": [
      "ai2_arc",
      "jondurbin/airoboros-3.2",
      "codeparrot/apps",
      "facebook/belebele",
      "boolq",
      "jondurbin/cinematika-v0.1",
      "drop",
      "lmsys/lmsys-chat-1m",
      "TIGER-Lab/MathInstruct",
      "cais/mmlu",
      "Muennighoff/natural-instructions",
      "openbookqa",
      "piqa",
      "Vezora/Tested-22k-Python-Alpaca",
      "cakiki/rosetta-code",
      "Open-Orca/SlimOrca",
      "spider",
      "squad_v2",
      "migtissera/Synthia-v1.3",
      "datasets/winogrande",
      "nvidia/HelpSteer",
      "Intel/orca_dpo_pairs",
      "unalignment/toxic-dpo-v0.1",
      "jondurbin/truthy-dpo-v0.1",
      "allenai/ultrafeedback_binarized_cleaned",
      "Squish42/bluemoon-fandom-1-1-rp-cleaned",
      "LDJnr/Capybara",
      "JULIELab/EmoBank",
      "kingbri/PIPPA-shareGPT"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "quantized_by": "mradermacher",
    "frontmatter": {
      "base_model": "jondurbin/bagel-8x7b-v0.2",
      "datasets": [
        "ai2_arc",
        "jondurbin/airoboros-3.2",
        "codeparrot/apps",
        "facebook/belebele",
        "boolq",
        "jondurbin/cinematika-v0.1",
        "drop",
        "lmsys/lmsys-chat-1m",
        "TIGER-Lab/MathInstruct",
        "cais/mmlu",
        "Muennighoff/natural-instructions",
        "openbookqa",
        "piqa",
        "Vezora/Tested-22k-Python-Alpaca",
        "cakiki/rosetta-code",
        "Open-Orca/SlimOrca",
        "spider",
        "squad_v2",
        "migtissera/Synthia-v1.3",
        "datasets/winogrande",
        "nvidia/HelpSteer",
        "Intel/orca_dpo_pairs",
        "unalignment/toxic-dpo-v0.1",
        "jondurbin/truthy-dpo-v0.1",
        "allenai/ultrafeedback_binarized_cleaned",
        "Squish42/bluemoon-fandom-1-1-rp-cleaned",
        "LDJnr/Capybara",
        "JULIELab/EmoBank",
        "kingbri/PIPPA-shareGPT"
      ],
      "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      static quants of https://huggingface.co/jondurbin/bagel-8x7b-v0.2  weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-8x7b-v0.2-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: jondurbin/bagel-8x7b-v0.2\ndatasets:\n- ai2_arc\n- jondurbin/airoboros-3.2\n- codeparrot/apps\n- facebook/belebele\n- boolq\n- jondurbin/cinematika-v0.1\n- drop\n- lmsys/lmsys-chat-1m\n- TIGER-Lab/MathInstruct\n- cais/mmlu\n- Muennighoff/natural-instructions\n- openbookqa\n- piqa\n- Vezora/Tested-22k-Python-Alpaca\n- cakiki/rosetta-code\n- Open-Orca/SlimOrca\n- spider\n- squad_v2\n- migtissera/Synthia-v1.3\n- datasets/winogrande\n- nvidia/HelpSteer\n- Intel/orca_dpo_pairs\n- unalignment/toxic-dpo-v0.1\n- jondurbin/truthy-dpo-v0.1\n- allenai/ultrafeedback_binarized_cleaned\n- Squish42/bluemoon-fandom-1-1-rp-cleaned\n- LDJnr/Capybara\n- JULIELab/EmoBank\n- kingbri/PIPPA-shareGPT\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 -->\nstatic quants of https://huggingface.co/jondurbin/bagel-8x7b-v0.2\n\n<!-- provided-files -->\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-8x7b-v0.2-i1-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-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q2_K.gguf) | Q2_K | 17.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q3_K_S.gguf) | Q3_K_S | 20.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q3_K_L.gguf) | Q3_K_L | 24.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.IQ4_XS.gguf) | IQ4_XS | 25.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q5_K_S.gguf) | Q5_K_S | 32.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q5_K_M.gguf) | Q5_K_M | 33.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q6_K.gguf) | Q6_K | 38.5 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/bagel-8x7b-v0.2-GGUF/resolve/main/bagel-8x7b-v0.2.Q8_0.gguf) | Q8_0 | 49.7 | fast, best quality |\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:jondurbin/airoboros-3.2",
    "dataset:codeparrot/apps",
    "dataset:facebook/belebele",
    "dataset:boolq",
    "dataset:jondurbin/cinematika-v0.1",
    "dataset:drop",
    "dataset:lmsys/lmsys-chat-1m",
    "dataset:TIGER-Lab/MathInstruct",
    "dataset:cais/mmlu",
    "dataset:Muennighoff/natural-instructions",
    "dataset:openbookqa",
    "dataset:piqa",
    "dataset:Vezora/Tested-22k-Python-Alpaca",
    "dataset:cakiki/rosetta-code",
    "dataset:Open-Orca/SlimOrca",
    "dataset:spider",
    "dataset:squad_v2",
    "dataset:migtissera/Synthia-v1.3",
    "dataset:datasets/winogrande",
    "dataset:nvidia/HelpSteer",
    "dataset:Intel/orca_dpo_pairs",
    "dataset:unalignment/toxic-dpo-v0.1",
    "dataset:jondurbin/truthy-dpo-v0.1",
    "dataset:allenai/ultrafeedback_binarized_cleaned",
    "dataset:Squish42/bluemoon-fandom-1-1-rp-cleaned",
    "dataset:LDJnr/Capybara",
    "dataset:JULIELab/EmoBank",
    "dataset:kingbri/PIPPA-shareGPT",
    "base_model:jondurbin/bagel-8x7b-v0.2",
    "base_model:quantized:jondurbin/bagel-8x7b-v0.2",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 1,
  "downloads": 144,
  "gated": false,
  "private": false,
  "last_modified": "2024-12-01T23:20:39.000Z",
  "created_at": "2024-12-01T17:16:30.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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