mradermacher/arc-base-8b-i1-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "LoganResearch/ARC-Base-8B",
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "cc-by-4.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "llama",
      "hermes",
      "cognitive-control",
      "decode-time-intervention",
      "repetition-suppression",
      "behavioral-control",
      "contrastive-learning",
      "interpretability",
      "activation-engineering",
      "cf-hot",
      "arc",
      "rlhf-analysis",
      "research"
    ],
    "frontmatter": {
      "base_model": "LoganResearch/ARC-Base-8B",
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "cc-by-4.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "llama",
        "hermes",
        "cognitive-control",
        "decode-time-intervention",
        "repetition-suppression",
        "behavioral-control",
        "contrastive-learning",
        "interpretability",
        "activation-engineering",
        "cf-hot",
        "arc",
        "rlhf-analysis",
        "research"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         weighted/imatrix quants of https://huggingface.co/LoganResearch/ARC-Base-8B  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/ARC-Base-8B-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: LoganResearch/ARC-Base-8B\nlanguage:\n- en\nlibrary_name: transformers\nlicense: cc-by-4.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- llama\n- hermes\n- cognitive-control\n- decode-time-intervention\n- repetition-suppression\n- behavioral-control\n- contrastive-learning\n- interpretability\n- activation-engineering\n- cf-hot\n- arc\n- rlhf-analysis\n- research\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/LoganResearch/ARC-Base-8B\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#ARC-Base-8B-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/ARC-Base-8B-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/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/ARC-Base-8B-i1-GGUF/resolve/main/ARC-Base-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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",
    "llama",
    "hermes",
    "cognitive-control",
    "decode-time-intervention",
    "repetition-suppression",
    "behavioral-control",
    "contrastive-learning",
    "interpretability",
    "activation-engineering",
    "cf-hot",
    "arc",
    "rlhf-analysis",
    "research",
    "en",
    "base_model:LoganResearch/ARC-Base-8B",
    "base_model:quantized:LoganResearch/ARC-Base-8B",
    "license:cc-by-4.0",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 2,
  "downloads": 164,
  "gated": false,
  "private": false,
  "last_modified": "2026-01-20T05:59:23.000Z",
  "created_at": "2026-01-18T06:17:12.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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  "createdAt": "2026-01-18T06:17:12.000Z",
  "lastModified": "2026-01-20T05:59:23.000Z",
  "author": "mradermacher",
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