inferenceillusionist/mini-magnum-12b-v1.1-imat-gguf overview

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    "tags": [
      "iMat",
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    "license": "apache-2.0",
    "frontmatter": {
      "base_model": "intervitens/mini-magnum-12b-v1.1",
      "library_name": "transformers",
      "quantized_by": "InferenceIllusionist",
      "tags": [
        "iMat",
        "gguf",
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      "license": "apache-2.0"
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    "hero_image_url": "https://i.imgur.com/P68dXux.png",
    "summary": "> [!WARNING] >Important Note: Inferencing in llama.cpp has now been merged in PR #8604. Please ensure you are on release b3438 or newer. Text-generation-web-ui (Ooba) is also working as of 7/23. Kobold.cpp working as of  v1.71.  Quantized from mini-magnum-12b-v1.1 fp16 * Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 92 chunks and n_ctx=512 * Static fp16 will also be included in repo * For a brief rundown of iMatrix quant performance please see this PR * All quants are verified working prior to uploading to repo for your safety and convenience KL-Divergence Reference Chart (Click on image to view in full size)  > [!TIP] >Quant-specific Tips: >* If you are getting a cudaMalloc failed: out of memory error, try passing an argument for lower context in llama.cpp, e.g. for 8k: -c 8192 >* If you have all ampere generation or newer cards, you can use flash attention like so: -fa >* Provided Flash Attention is enabled you can also use quantized cache to save on VRAM e.g. for 8-bit: -ctk q8_0 -ctv q8_0 Original model card can be found here",
    "quick_links": [],
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    "readme_markdown": "---\nbase_model: intervitens/mini-magnum-12b-v1.1\nlibrary_name: transformers\nquantized_by: InferenceIllusionist\ntags:\n- iMat\n- gguf\n- Mistral\nlicense: apache-2.0\n---\n<img src=\"https://i.imgur.com/P68dXux.png\" width=\"400\"/>\n\n# mini-magnum-12b-v1.1-iMat-GGUF\n\n> [!WARNING]\n><b>Important Note:</b> Inferencing in llama.cpp has now been merged in [PR #8604](https://github.com/ggerganov/llama.cpp/pull/8604). Please ensure you are on release [b3438](https://github.com/ggerganov/llama.cpp/releases/tag/b3438) or newer. Text-generation-web-ui (Ooba) is also working as of 7/23. Kobold.cpp working as of  [v1.71](https://github.com/LostRuins/koboldcpp/releases/tag/v1.71). </b>\n\nQuantized from mini-magnum-12b-v1.1 fp16\n* Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 92 chunks and n_ctx=512\n* Static fp16 will also be included in repo\n* For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747)\n* <i>All quants are verified working prior to uploading to repo for your safety and convenience</i>\n\n<b>KL-Divergence Reference Chart</b>\n (Click on image to view in full size)\n[<img src=\"https://i.imgur.com/mV0nYdA.png\" width=\"920\"/>](https://i.imgur.com/mV0nYdA.png)\n\n> [!TIP]\n><b>Quant-specific Tips:</b>\n>* If you are getting a `cudaMalloc failed: out of memory` error, try passing an argument for lower context in llama.cpp, e.g. for 8k: `-c 8192`\n>* If you have all ampere generation or newer cards, you can use flash attention like so: `-fa`\n>* Provided Flash Attention is enabled you can also use quantized cache to save on VRAM e.g. for 8-bit: `-ctk q8_0 -ctv q8_0`\n\n\nOriginal model card can be found [here](https://huggingface.co/intervitens/mini-magnum-12b-v1.1)",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "iMat",
    "Mistral",
    "base_model:intervitens/mini-magnum-12b-v1.1",
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    "license:apache-2.0",
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  "last_modified": "2024-08-01T21:50:03.000Z",
  "created_at": "2024-07-24T07:02:47.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
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