mradermacher/cybersec-qwen3-deepseekv1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "ykarout/CyberSec-Qwen3-DeepSeekv1",
    "datasets": [
      "Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset"
    ],
    "language": [
      "en",
      "ar",
      "es",
      "ru",
      "it",
      "de"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "cybersecurity",
      "fine-tuned",
      "deepseek",
      "qwen3",
      "lora",
      "cyber",
      "nist",
      "csf",
      "pentest"
    ],
    "frontmatter": {
      "base_model": "ykarout/CyberSec-Qwen3-DeepSeekv1",
      "datasets": [
        "Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset"
      ],
      "language": [
        "en",
        "ar",
        "es",
        "ru",
        "it",
        "de"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "cybersecurity",
        "fine-tuned",
        "deepseek",
        "qwen3",
        "lora",
        "cyber",
        "nist",
        "csf",
        "pentest"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         static quants of https://huggingface.co/ykarout/CyberSec-Qwen3-DeepSeekv1  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: ykarout/CyberSec-Qwen3-DeepSeekv1\ndatasets:\n- Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset\nlanguage:\n- en\n- ar\n- es\n- ru\n- it\n- de\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- cybersecurity\n- fine-tuned\n- deepseek\n- qwen3\n- lora\n- cyber\n- nist\n- csf\n- pentest\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\n<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nstatic quants of https://huggingface.co/ykarout/CyberSec-Qwen3-DeepSeekv1\n\n<!-- provided-files -->\n\n***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#CyberSec-Qwen3-DeepSeekv1-GGUF).***\n\nweighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.\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/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q2_K.gguf) | Q2_K | 3.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q3_K_S.gguf) | Q3_K_S | 3.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q3_K_L.gguf) | Q3_K_L | 4.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.IQ4_XS.gguf) | IQ4_XS | 4.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q5_K_S.gguf) | Q5_K_S | 5.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q5_K_M.gguf) | Q5_K_M | 6.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q6_K.gguf) | Q6_K | 6.8 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |\n| [GGUF](https://huggingface.co/mradermacher/CyberSec-Qwen3-DeepSeekv1-GGUF/resolve/main/CyberSec-Qwen3-DeepSeekv1.f16.gguf) | f16 | 16.5 | 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",
    "cybersecurity",
    "fine-tuned",
    "deepseek",
    "qwen3",
    "lora",
    "cyber",
    "nist",
    "csf",
    "pentest",
    "en",
    "ar",
    "es",
    "ru",
    "it",
    "de",
    "dataset:Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 3,
  "downloads": 216,
  "gated": false,
  "private": false,
  "last_modified": "2025-08-16T15:00:17.000Z",
  "created_at": "2025-08-16T14:27:48.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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  "createdAt": "2025-08-16T14:27:48.000Z",
  "lastModified": "2025-08-16T15:00:17.000Z",
  "author": "mradermacher",
  "downloads": 216,
  "likes": 3,
  "gated": false,
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  "pipeline_tag": "",
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  "siblings_count": 14
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