mradermacher/prox-llama-3-8b-abliterated-gguf overview

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  "card_data": {
    "base_model": "openvoid/Prox-Llama-3-8B-abliterated",
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "quantized_by": "mradermacher",
    "tags": [
      "code",
      "cybersecurity",
      "penetration testing",
      "hacking",
      "code",
      "uncensored"
    ],
    "frontmatter": {
      "base_model": "openvoid/Prox-Llama-3-8B-abliterated",
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "quantized_by": "mradermacher",
      "tags": [
        "code",
        "cybersecurity",
        "penetration testing",
        "hacking",
        "code",
        "uncensored"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About      static quants of https://huggingface.co/openvoid/Prox-Llama-3-8B-abliterated  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: openvoid/Prox-Llama-3-8B-abliterated\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nquantized_by: mradermacher\ntags:\n- code\n- cybersecurity\n- penetration testing\n- hacking\n- code\n- uncensored\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\nstatic quants of https://huggingface.co/openvoid/Prox-Llama-3-8B-abliterated\n\n<!-- provided-files -->\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-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/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q2_K.gguf) | Q2_K | 3.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.IQ3_XS.gguf) | IQ3_XS | 3.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q3_K_S.gguf) | Q3_K_S | 3.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.IQ3_M.gguf) | IQ3_M | 3.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q3_K_L.gguf) | Q3_K_L | 4.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.IQ4_XS.gguf) | IQ4_XS | 4.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q5_K_S.gguf) | Q5_K_S | 5.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q5_K_M.gguf) | Q5_K_M | 5.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q6_K.gguf) | Q6_K | 6.7 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |\n| [GGUF](https://huggingface.co/mradermacher/Prox-Llama-3-8B-abliterated-GGUF/resolve/main/Prox-Llama-3-8B-abliterated.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |\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",
    "code",
    "cybersecurity",
    "penetration testing",
    "hacking",
    "uncensored",
    "en",
    "base_model:openvoid/Prox-Llama-3-8B-abliterated",
    "base_model:quantized:openvoid/Prox-Llama-3-8B-abliterated",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 149,
  "gated": false,
  "private": false,
  "last_modified": "2024-06-20T18:00:55.000Z",
  "created_at": "2024-06-20T17:07:00.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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