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mradermacher/ablit-2b-i1-gguf Q3_K_L GGUF - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.

Model Intelligence Sheet

mradermacher/ablit-2b-i1-gguf overview

About weighted/imatrix quants of https://huggingface.co/Luog03/Ablit-2B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Ablit-2B-GGUF

transformersggufreasoningchain-of-thoughtmathematical-reasoninguncensoredno-refusalinstruction-following2bqwendistillationopuscotenbase_model:Luog03/Ablit-2Bbase_model:quantized:Luog03/Ablit-2Blicense:apache-2.0endpoints_compatibleregion:usimatrixconversational
mradermacher/ablit-2b-i1-gguf visual
Downloads
102
Likes
0
Pipeline
Library
transformers
Visibility
Public
Access
Open

Repository Files & Downloads

25 files detected
Direct downloads for all repository files
FileTypeQuantizationSizeLink
Ablit-2B.i1-IQ1_M.gguf GGUF IQ1_M 713.54 MB Download
Ablit-2B.i1-IQ1_S.gguf GGUF IQ1_S 689.47 MB Download
Ablit-2B.i1-IQ2_M.gguf GGUF IQ2_M 825.64 MB Download
Ablit-2B.i1-IQ2_S.gguf GGUF IQ2_S 793.54 MB Download
Ablit-2B.i1-IQ2_XS.gguf GGUF IQ2_XS 786.51 MB Download
Ablit-2B.i1-IQ2_XXS.gguf GGUF IQ2_XXS 753.67 MB Download
Ablit-2B.i1-IQ3_M.gguf GGUF IQ3_M 1010.37 MB Download
Ablit-2B.i1-IQ3_S.gguf GGUF IQ3_S 1002.40 MB Download
Ablit-2B.i1-IQ3_XS.gguf GGUF IQ3_XS 979.62 MB Download
Ablit-2B.i1-IQ3_XXS.gguf GGUF IQ3_XXS 884.77 MB Download
Ablit-2B.i1-IQ4_NL.gguf GGUF IQ4_NL 1.15 GB Download
Ablit-2B.i1-IQ4_XS.gguf GGUF IQ4_XS 1.11 GB Download
Ablit-2B.i1-Q2_K.gguf GGUF Q2_K 923.67 MB Download
Ablit-2B.i1-Q2_K_S.gguf GGUF Q2_K_S 900.42 MB Download
Ablit-2B.i1-Q3_K_L.gguf GGUF Q3_K_L 1.08 GB Download
Ablit-2B.i1-Q3_K_M.gguf GGUF Q3_K_M 1.02 GB Download
Ablit-2B.i1-Q3_K_S.gguf GGUF Q3_K_S 972.91 MB Download
Ablit-2B.i1-Q4_0.gguf GGUF 1.12 GB Download
Ablit-2B.i1-Q4_1.gguf GGUF 1.20 GB Download
Ablit-2B.i1-Q4_K_M.gguf GGUF Q4_K_M 1.19 GB Download
Ablit-2B.i1-Q4_K_S.gguf GGUF Q4_K_S 1.13 GB Download
Ablit-2B.i1-Q5_K_M.gguf GGUF Q5_K_M 1.31 GB Download
Ablit-2B.i1-Q5_K_S.gguf GGUF Q5_K_S 1.28 GB Download
Ablit-2B.i1-Q6_K.gguf GGUF Q6_K 1.45 GB Download
Ablit-2B.imatrix.gguf GGUF 1.86 MB Download

Model Details Live

Model Slug
mradermacher/ablit-2b-i1-gguf
Author
mradermacher
Pipeline Task
Library
transformers
Created
2026-03-16
Last Modified
2026-03-16
Gated
No
Private
No
HF SHA
aad1e986de2d926d2435d68c4d9712089f0feb6f
License
apache-2.0
Language
en
Base Model
Luog03/Ablit-2B

Metadata Inspector

Normalized metadata (stored in metadata_json)
{
  "metadata": {},
  "card_data": {
    "base_model": "Luog03/Ablit-2B",
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "reasoning",
      "chain-of-thought",
      "mathematical-reasoning",
      "uncensored",
      "no-refusal",
      "instruction-following",
      "2b",
      "qwen",
      "distillation",
      "opus",
      "cot"
    ],
    "frontmatter": {
      "base_model": "Luog03/Ablit-2B",
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "reasoning",
        "chain-of-thought",
        "mathematical-reasoning",
        "uncensored",
        "no-refusal",
        "instruction-following",
        "2b",
        "qwen",
        "distillation",
        "opus",
        "cot"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         weighted/imatrix quants of https://huggingface.co/Luog03/Ablit-2B  ***For a convenient overview and download list, visit our model page for this model.*** static quants are available at https://huggingface.co/mradermacher/Ablit-2B-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: Luog03/Ablit-2B\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- reasoning\n- chain-of-thought\n- mathematical-reasoning\n- uncensored\n- no-refusal\n- instruction-following\n- 2b\n- qwen\n- distillation\n- opus\n- cot\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/Luog03/Ablit-2B\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#Ablit-2B-i1-GGUF).***\n\nstatic quants are available at https://huggingface.co/mradermacher/Ablit-2B-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/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ1_S.gguf) | i1-IQ1_S | 0.8 | for the desperate |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ1_M.gguf) | i1-IQ1_M | 0.8 | mostly desperate |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ2_S.gguf) | i1-IQ2_S | 0.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.0 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.0 | very low quality |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q2_K.gguf) | i1-Q2_K | 1.1 | IQ3_XXS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.1 | IQ3_XS probably better |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ3_S.gguf) | i1-IQ3_S | 1.2 | beats Q3_K* |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ3_M.gguf) | i1-IQ3_M | 1.2 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.2 | IQ3_S probably better |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.3 | IQ3_M probably better |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q4_0.gguf) | i1-Q4_0 | 1.3 | fast, low quality |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.3 | optimal size/speed/quality |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.3 | prefer IQ4_XS |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.4 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q4_1.gguf) | i1-Q4_1 | 1.4 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Ablit-2B-i1-GGUF/resolve/main/Ablit-2B.i1-Q6_K.gguf) | i1-Q6_K | 1.7 | 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",
    "reasoning",
    "chain-of-thought",
    "mathematical-reasoning",
    "uncensored",
    "no-refusal",
    "instruction-following",
    "2b",
    "qwen",
    "distillation",
    "opus",
    "cot",
    "en",
    "base_model:Luog03/Ablit-2B",
    "base_model:quantized:Luog03/Ablit-2B",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 0,
  "downloads": 102,
  "gated": false,
  "private": false,
  "last_modified": "2026-03-16T16:31:25.000Z",
  "created_at": "2026-03-16T15:40:24.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}
Source payload excerpt (from Hugging Face API)
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