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mradermacher/gemma-2-2b-jpn-it-abliterated-17-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-17' (base has 'abliterated' marker, assume M1 default)
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Downloads · lifetime
9K
713 last 30d - cooling
Likes
1
Model age
17mo ago
created 2025-05-08

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
Now8.7K→from500↑1,634%
913.2K6.4K9.5K500 on May 7, 20258.7K on Oct 11May '25Aug '25Nov '25FebMayAug
May 7, 2025 → Oct 11 · 114 snapshots · spans 522 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 · 1K 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-17 base_model:quantized:ymcki/gemma-2-2b-jpn-it-abliterated-17 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 03:14

Files by quantization

Q6_K 1 file 2.00 GB
gemma-2-2b-jpn-it-abliterated-17.i1-Q6_K.gguf 2.00 GB c22ca17a download
Q5_K 2 files 3.54 GB
gemma-2-2b-jpn-it-abliterated-17.i1-Q5_K_M.gguf 1.79 GB 91c3c4b6 download
gemma-2-2b-jpn-it-abliterated-17.i1-Q5_K_S.gguf 1.75 GB 64145f1a download
Q4 2 files 3.16 GB
gemma-2-2b-jpn-it-abliterated-17.i1-Q4_1.gguf 1.64 GB 22b9dc87 download
gemma-2-2b-jpn-it-abliterated-17.i1-Q4_0.gguf 1.52 GB 0f136065 download
Q4_K 2 files 3.12 GB
gemma-2-2b-jpn-it-abliterated-17.i1-Q4_K_M.gguf 1.59 GB 78c36f22 download
gemma-2-2b-jpn-it-abliterated-17.i1-Q4_K_S.gguf 1.53 GB 56048fcd download
IQ4 2 files 2.98 GB
gemma-2-2b-jpn-it-abliterated-17.i1-IQ4_NL.gguf 1.52 GB ceac6c21 download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ4_XS.gguf 1.46 GB 4eb7b210 download
Q3_K 3 files 4.07 GB
gemma-2-2b-jpn-it-abliterated-17.i1-Q3_K_L.gguf 1.44 GB 3ccd5df3 download
gemma-2-2b-jpn-it-abliterated-17.i1-Q3_K_M.gguf 1.36 GB e8aa3b7a download
gemma-2-2b-jpn-it-abliterated-17.i1-Q3_K_S.gguf 1.27 GB 1ac9a658 download
IQ3 4 files 4.89 GB
gemma-2-2b-jpn-it-abliterated-17.i1-IQ3_M.gguf 1.30 GB 8f6eddb4 download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ3_S.gguf 1.27 GB 126411c9 download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ3_XS.gguf 1.22 GB b87dac3b download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ3_XXS.gguf 1.10 GB d80eff52 download
Q2_K 2 files 2.24 GB
gemma-2-2b-jpn-it-abliterated-17.i1-Q2_K.gguf 1.15 GB 5453f605 download
gemma-2-2b-jpn-it-abliterated-17.i1-Q2_K_S.gguf 1.09 GB ca59eec5 download
IQ2 4 files 3.79 GB
gemma-2-2b-jpn-it-abliterated-17.i1-IQ2_M.gguf 1.01 GB 9a6cf387 download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ2_S.gguf 985 MB 3b63674a download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ2_XS.gguf 956 MB de6d1f5d download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ2_XXS.gguf 900 MB 49afb9fd download
IQ1 2 files 1.59 GB
gemma-2-2b-jpn-it-abliterated-17.i1-IQ1_M.gguf 833 MB 0ef93bc0 download
gemma-2-2b-jpn-it-abliterated-17.i1-IQ1_S.gguf 794 MB 4fbb645f download
Auxiliary files 3 files 2.28 MB
imatrix.dat 2.27 MB 306d354c download
README.md 6.34 KB eb8fa04c download
.gitattributes 3.49 KB 9d80aa45 download

README current version from Hugging Face


base_model: ymcki/gemma-2-2b-jpn-it-abliterated-17
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-17

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-17-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.mdf118cff6.3 KB
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  2. 2025-07-10auto-patch README.mdec7178d6.3 KB
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  3. 2025-05-09auto-patch README.md2b617f56.2 KB
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  4. 2025-05-08uploaded from rich19f380ca246 B
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