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mradermacher/abliterated-minicpm5-2b-v2-i1-GGUF

mradermacher 2B GGUF second-order
curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/mradermacher%2Fabliterated-minicpm5-2b-v2-i1-GGUF"
Response includes
  • classification m8
  • files 27
  • author_summary 3235 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 3 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • author=mradermacher (M8 quantization producer, never originator)
  • is_gguf=1
  • base_model='KidIkaros/abliterated-minicpm5-2b-v2' (base has 'abliterated' marker, assume M1 default)
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · 30-day
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Model age
today
created 2026-09-18

Genealogy 0 direct forks

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Variants by this author 2 formats · 0 downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
apache-2.0
Languages
en
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K Q6_K
Tags
transformers gguf text-generation abliterated dpo llama.cpp pytorch en base_model:KidIkaros/abliterated-minicpm5-2b-v2 base_model:quantized:KidIkaros/abliterated-minicpm5-2b-v2 license:apache-2.0 endpoints_compatible

Related

Total size
28.2 GB
Files
27
Quantizations
11
Registered
2026-09-18 17:56
Last updated on HF
2026-09-18 17:40

Files by quantization

Q6_K 1 file 1.93 GB
abliterated-minicpm5-2b-v2.i1-Q6_K.gguf 1.93 GB 0567c23b download
Q5_K 2 files 3.33 GB
abliterated-minicpm5-2b-v2.i1-Q5_K_M.gguf 1.68 GB feb1cf60 download
abliterated-minicpm5-2b-v2.i1-Q5_K_S.gguf 1.65 GB 7bc2d3d1 download
Q4 2 files 2.91 GB
abliterated-minicpm5-2b-v2.i1-Q4_1.gguf 1.52 GB 78fdd33e download
abliterated-minicpm5-2b-v2.i1-Q4_0.gguf 1.39 GB 8b7b6802 download
Q4_K 2 files 2.85 GB
abliterated-minicpm5-2b-v2.i1-Q4_K_M.gguf 1.45 GB b0cac6c8 download
abliterated-minicpm5-2b-v2.i1-Q4_K_S.gguf 1.40 GB b1b483c4 download
IQ4 2 files 2.72 GB
abliterated-minicpm5-2b-v2.i1-IQ4_NL.gguf 1.39 GB 70a8aeac download
abliterated-minicpm5-2b-v2.i1-IQ4_XS.gguf 1.33 GB 7a8b99b1 download
Q3_K 3 files 3.60 GB
abliterated-minicpm5-2b-v2.i1-Q3_K_L.gguf 1.28 GB 30ba6950 download
abliterated-minicpm5-2b-v2.i1-Q3_K_M.gguf 1.20 GB 08c844f4 download
abliterated-minicpm5-2b-v2.i1-Q3_K_S.gguf 1.11 GB a99e0f94 download
IQ3 4 files 4.31 GB
abliterated-minicpm5-2b-v2.i1-IQ3_M.gguf 1.14 GB 3a7cd91d download
abliterated-minicpm5-2b-v2.i1-IQ3_S.gguf 1.11 GB 0309e605 download
abliterated-minicpm5-2b-v2.i1-IQ3_XS.gguf 1.07 GB 0c1635cb download
abliterated-minicpm5-2b-v2.i1-IQ3_XXS.gguf 1014 MB c299ca79 download
Q2_K 2 files 1.88 GB
abliterated-minicpm5-2b-v2.i1-Q2_K.gguf 992 MB fa50635c download
abliterated-minicpm5-2b-v2.i1-Q2_K_S.gguf 938 MB 7a949ba2 download
IQ2 4 files 3.30 GB
abliterated-minicpm5-2b-v2.i1-IQ2_M.gguf 926 MB c6359203 download
abliterated-minicpm5-2b-v2.i1-IQ2_S.gguf 875 MB 36cbadf8 download
abliterated-minicpm5-2b-v2.i1-IQ2_XS.gguf 819 MB e355a921 download
abliterated-minicpm5-2b-v2.i1-IQ2_XXS.gguf 762 MB c8741207 download
IQ1 2 files 1.33 GB
abliterated-minicpm5-2b-v2.i1-IQ1_M.gguf 698 MB 3cc4dce8 download
abliterated-minicpm5-2b-v2.i1-IQ1_S.gguf 660 MB a28e85bc download
Auxiliary files 3 files 3.01 MB
abliterated-minicpm5-2b-v2.imatrix.gguf 3.00 MB 13d99447 download
README.md 6.41 KB c4e78c4b download
.gitattributes 3.37 KB 0c0cfd0c download

README current version from Hugging Face


base_model: KidIkaros/abliterated-minicpm5-2b-v2
language:

  • en
    library_name: transformers
    license: apache-2.0
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • text-generation
  • abliterated
  • dpo
  • gguf
  • llama.cpp
  • pytorch

About

weighted/imatrix quants of https://huggingface.co/KidIkaros/abliterated-minicpm5-2b-v2

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/abliterated-minicpm5-2b-v2-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs
for
more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link Type Size/GB Notes
GGUF imatrix 0.1 imatrix file (for creating your own quants)
GGUF i1-IQ1_S 0.8 for the desperate
GGUF i1-IQ1_M 0.8 mostly desperate
GGUF i1-IQ2_XXS 0.9
GGUF i1-IQ2_XS 1.0
GGUF i1-IQ2_S 1.0
GGUF i1-IQ2_M 1.1
GGUF i1-Q2_K_S 1.1 very low quality
GGUF i1-Q2_K 1.1 IQ3_XXS probably better
GGUF i1-IQ3_XXS 1.2 lower quality
GGUF i1-IQ3_XS 1.2
GGUF i1-Q3_K_S 1.3 IQ3_XS probably better
GGUF i1-IQ3_S 1.3 beats Q3_K*
GGUF i1-IQ3_M 1.3
GGUF i1-Q3_K_M 1.4 IQ3_S probably better
GGUF i1-Q3_K_L 1.5 IQ3_M probably better
GGUF i1-IQ4_XS 1.5
GGUF i1-IQ4_NL 1.6 prefer IQ4_XS
GGUF i1-Q4_0 1.6 fast, low quality
GGUF i1-Q4_K_S 1.6 optimal size/speed/quality
GGUF i1-Q4_K_M 1.7 fast, recommended
GGUF i1-Q4_1 1.7
GGUF i1-Q5_K_S 1.9
GGUF i1-Q5_K_M 1.9
GGUF i1-Q6_K 2.2 practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

image.png

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to @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.

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