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mradermacher/gemma-2-2b-jpn-it-abliterated-18-i1-GGUF

mradermacher Gemma 2B GGUF second-order 8K ctx
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  • classification m8
  • files 27
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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='ymcki/gemma-2-2b-jpn-it-abliterated-18' (base has 'abliterated' marker, assume M1 default)
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Downloads · lifetime
7K
425 last 30d - cooling
Likes
1
Model age
19mo ago
created 2025-03-18

Training datasets

2 of 2 in /datasets

Corpora the author lists in the model card. Datasets tracked in our /datasets catalog carry a category badge linking to the workflow stage. Others open on Hugging Face.

Downloads over time
Now6.9K→from0↑0%
02.5K5.1K7.6K0 on Mar 12, 20256.9K on Oct 12Mar '25Jun '25Sep '25Dec '25MarJunSep
Mar 12, 2025 → Oct 12 · 123 snapshots · spans 579 days

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Variants by this author 2 formats · 748 downloads combined

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

Metadata

License
gemma
Languages
multilingual
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K Q6_K
Tags
transformers gguf nlp code multilingual dataset:mlabonne/harmless_alpaca dataset:mlabonne/harmful_behaviors base_model:ymcki/gemma-2-2b-jpn-it-abliterated-18 base_model:quantized:ymcki/gemma-2-2b-jpn-it-abliterated-18 license:gemma endpoints_compatible region:us

Related

Total size
31.4 GB
Files
27
Quantizations
11
Registered
2026-08-22 13:56
Last updated on HF
2025-07-11 08:51

Files by quantization

Q6_K 1 file 2.00 GB
gemma-2-2b-jpn-it-abliterated-18.i1-Q6_K.gguf 2.00 GB 446c5a77 download
Q5_K 2 files 3.54 GB
gemma-2-2b-jpn-it-abliterated-18.i1-Q5_K_M.gguf 1.79 GB ae91d947 download
gemma-2-2b-jpn-it-abliterated-18.i1-Q5_K_S.gguf 1.75 GB 0cb6dfb0 download
Q4 2 files 3.16 GB
gemma-2-2b-jpn-it-abliterated-18.i1-Q4_1.gguf 1.64 GB 1758d5dc download
gemma-2-2b-jpn-it-abliterated-18.i1-Q4_0.gguf 1.52 GB f933fd29 download
Q4_K 2 files 3.12 GB
gemma-2-2b-jpn-it-abliterated-18.i1-Q4_K_M.gguf 1.59 GB 0e40fee5 download
gemma-2-2b-jpn-it-abliterated-18.i1-Q4_K_S.gguf 1.53 GB 240dcf9a download
IQ4 2 files 2.98 GB
gemma-2-2b-jpn-it-abliterated-18.i1-IQ4_NL.gguf 1.52 GB 419194d2 download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ4_XS.gguf 1.46 GB 52df7204 download
Q3_K 3 files 4.07 GB
gemma-2-2b-jpn-it-abliterated-18.i1-Q3_K_L.gguf 1.44 GB 92383431 download
gemma-2-2b-jpn-it-abliterated-18.i1-Q3_K_M.gguf 1.36 GB ee1a8b69 download
gemma-2-2b-jpn-it-abliterated-18.i1-Q3_K_S.gguf 1.27 GB 23b7cc60 download
IQ3 4 files 4.89 GB
gemma-2-2b-jpn-it-abliterated-18.i1-IQ3_M.gguf 1.30 GB 3c1eebbc download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ3_S.gguf 1.27 GB cb12a8f3 download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ3_XS.gguf 1.22 GB f515d13c download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ3_XXS.gguf 1.10 GB aadf0516 download
Q2_K 2 files 2.24 GB
gemma-2-2b-jpn-it-abliterated-18.i1-Q2_K.gguf 1.15 GB b133bfe7 download
gemma-2-2b-jpn-it-abliterated-18.i1-Q2_K_S.gguf 1.09 GB dc71c0da download
IQ2 4 files 3.79 GB
gemma-2-2b-jpn-it-abliterated-18.i1-IQ2_M.gguf 1.01 GB bfcb1bcb download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ2_S.gguf 985 MB cc0027a3 download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ2_XS.gguf 956 MB 1f104f3f download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ2_XXS.gguf 900 MB 1e4851ab download
IQ1 2 files 1.59 GB
gemma-2-2b-jpn-it-abliterated-18.i1-IQ1_M.gguf 833 MB b7ce06d7 download
gemma-2-2b-jpn-it-abliterated-18.i1-IQ1_S.gguf 794 MB bc23cca6 download
Auxiliary files 3 files 2.28 MB
imatrix.dat 2.27 MB f609492a download
README.md 6.34 KB 4c6ffe9f download
.gitattributes 3.49 KB c818e7e9 download

README current version from Hugging Face


base_model: ymcki/gemma-2-2b-jpn-it-abliterated-18
datasets:

  • mlabonne/harmless_alpaca
  • mlabonne/harmful_behaviors
    language:
  • multilingual
    library_name: transformers
    license: gemma
    license_link: https://ai.google.dev/gemma/terms
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • nlp
  • code

About

weighted/imatrix quants of https://huggingface.co/ymcki/gemma-2-2b-jpn-it-abliterated-18

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

static quants are available at https://huggingface.co/mradermacher/gemma-2-2b-jpn-it-abliterated-18-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 i1-IQ1_S 0.9 for the desperate
GGUF i1-IQ1_M 1.0 mostly desperate
GGUF i1-IQ2_XXS 1.0
GGUF i1-IQ2_XS 1.1
GGUF i1-IQ2_S 1.1
GGUF i1-IQ2_M 1.2
GGUF i1-Q2_K_S 1.3 very low quality
GGUF i1-IQ3_XXS 1.3 lower quality
GGUF i1-Q2_K 1.3 IQ3_XXS probably better
GGUF i1-IQ3_XS 1.4
GGUF i1-IQ3_S 1.5 beats Q3_K*
GGUF i1-Q3_K_S 1.5 IQ3_XS probably better
GGUF i1-IQ3_M 1.5
GGUF i1-Q3_K_M 1.6 IQ3_S probably better
GGUF i1-Q3_K_L 1.7 IQ3_M probably better
GGUF i1-IQ4_XS 1.7
GGUF i1-IQ4_NL 1.7 prefer IQ4_XS
GGUF i1-Q4_0 1.7 fast, low quality
GGUF i1-Q4_K_S 1.7 optimal size/speed/quality
GGUF i1-Q4_K_M 1.8 fast, recommended
GGUF i1-Q4_1 1.9
GGUF i1-Q5_K_S 2.0
GGUF i1-Q5_K_M 2.0
GGUF i1-Q6_K 2.3 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.

README history 4 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2025-07-11auto-patch README.mde6b191f6.3 KB
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  2. 2025-07-10auto-patch README.mdf67f7d56.3 KB
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  3. 2025-03-18auto-patch README.md3aef3d46.2 KB
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  4. 2025-03-18uploaded from rich1a7f98db246 B
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