mradermacher/pe-type-1-vera-4b-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "vanta-research/PE-Type-1-Vera-4B",
    "datasets": [
      "vanta-research/PE-Type-1"
    ],
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "google",
      "gemma",
      "deepmind",
      "large-language-model",
      "ai-persona-research",
      "enneagram",
      "chatbot",
      "psychology",
      "persona-research",
      "research-model",
      "roleplay",
      "text-generation-inference",
      "vanta-research",
      "cognitive-alignment",
      "project-enneagram",
      "conversational-ai",
      "conversational",
      "ai-research",
      "ai-alignment-research",
      "ai-alignment",
      "ai-behavior-research",
      "human-ai-collaboration"
    ],
    "frontmatter": {
      "base_model": "vanta-research/PE-Type-1-Vera-4B",
      "datasets": [
        "vanta-research/PE-Type-1"
      ],
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "google",
        "gemma",
        "deepmind",
        "large-language-model",
        "ai-persona-research",
        "enneagram",
        "chatbot",
        "psychology",
        "persona-research",
        "research-model",
        "roleplay",
        "text-generation-inference",
        "vanta-research",
        "cognitive-alignment",
        "project-enneagram",
        "conversational-ai",
        "conversational",
        "ai-research",
        "ai-alignment-research",
        "ai-alignment",
        "ai-behavior-research",
        "human-ai-collaboration"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         weighted/imatrix quants of https://huggingface.co/vanta-research/PE-Type-1-Vera-4B  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-GGUF **This is a vision model - mmproj files (if any) will be in the static repository.**",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: vanta-research/PE-Type-1-Vera-4B\ndatasets:\n- vanta-research/PE-Type-1\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- google\n- gemma\n- deepmind\n- large-language-model\n- ai-persona-research\n- enneagram\n- chatbot\n- psychology\n- persona-research\n- research-model\n- roleplay\n- text-generation-inference\n- vanta-research\n- cognitive-alignment\n- project-enneagram\n- conversational-ai\n- conversational\n- ai-research\n- ai-alignment-research\n- ai-alignment\n- ai-behavior-research\n- human-ai-collaboration\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags: nicoboss -->\n<!-- ### quants:  Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nweighted/imatrix quants of https://huggingface.co/vanta-research/PE-Type-1-Vera-4B\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#PE-Type-1-Vera-4B-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-GGUF\n\n**This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-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/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.2 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.3 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.7 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.8 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q2_K.gguf) | i1-Q2_K | 1.8 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ3_S.gguf) | i1-IQ3_S | 2.0 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.0 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ3_M.gguf) | i1-IQ3_M | 2.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.2 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.3 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.5 | prefer IQ4_XS |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q4_0.gguf) | i1-Q4_0 | 2.5 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.5 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q4_1.gguf) | i1-Q4_1 | 2.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/PE-Type-1-Vera-4B-i1-GGUF/resolve/main/PE-Type-1-Vera-4B.i1-Q6_K.gguf) | i1-Q6_K | 3.3 | 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",
    "google",
    "gemma",
    "deepmind",
    "large-language-model",
    "ai-persona-research",
    "enneagram",
    "chatbot",
    "psychology",
    "persona-research",
    "research-model",
    "roleplay",
    "text-generation-inference",
    "vanta-research",
    "cognitive-alignment",
    "project-enneagram",
    "conversational-ai",
    "conversational",
    "ai-research",
    "ai-alignment-research",
    "ai-alignment",
    "ai-behavior-research",
    "human-ai-collaboration",
    "en",
    "dataset:vanta-research/PE-Type-1",
    "base_model:vanta-research/PE-Type-1-Vera-4B",
    "base_model:quantized:vanta-research/PE-Type-1-Vera-4B",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix"
  ],
  "likes": 1,
  "downloads": 151,
  "gated": false,
  "private": false,
  "last_modified": "2026-03-04T21:19:51.000Z",
  "created_at": "2026-01-29T16:16:58.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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