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mradermacher/Huihui-gemma-3-270m-it-abliterated-i1-GGUF

mradermacher Gemma GGUF second-order 33K ctx
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Response includes
  • classification m8
  • files 27
  • hub_downloads_all_time 7,471
  • author_summary 3324 models
  • readme_text full
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Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of layer-wise ablation 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='huihui-ai/Huihui-gemma-3-270m-it-abliterated' (base is huihui-ai model (M3))
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.

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Downloads · lifetime
7K
1K last 30d - stable
Likes
2
Model age
13mo ago
created 2025-08-25
Downloads over time
Now7.8K→from644↑1,105%
2883K5.7K8.5K644 on Aug 27, 20257.8K on Oct 12Aug '25Oct '25Dec '25FebAprJunAugOct
Aug 27, 2025 → Oct 12 · 99 snapshots · spans 411 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 · 2K downloads combined

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

Metadata

License
gemma
Languages
en
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K Q6_K
Tags
transformers gguf gemma3 gemma google generated_from_trainer trl sft abliterated uncensored en base_model:huihui-ai/Huihui-gemma-3-270m-it-abliterated

Related

Total size
5.42 GB
Files
27
Quantizations
11
Registered
2026-08-22 13:56
Last updated on HF
2025-12-23 05:03

Files by quantization

Q6_K 1 file 270 MB
Huihui-gemma-3-270m-it-abliterated.i1-Q6_K.gguf 270 MB 19a97f6e download
Q5_K 2 files 494 MB
Huihui-gemma-3-270m-it-abliterated.i1-Q5_K_M.gguf 248 MB 80bb82b7 download
Huihui-gemma-3-270m-it-abliterated.i1-Q5_K_S.gguf 246 MB c4ba2f42 download
Q4_K 2 files 480 MB
Huihui-gemma-3-270m-it-abliterated.i1-Q4_K_M.gguf 241 MB 11e60058 download
Huihui-gemma-3-270m-it-abliterated.i1-Q4_K_S.gguf 238 MB 2be7db2e download
Q4 2 files 467 MB
Huihui-gemma-3-270m-it-abliterated.i1-Q4_1.gguf 236 MB 18a8c3e3 download
Huihui-gemma-3-270m-it-abliterated.i1-Q4_0.gguf 230 MB 38a836f7 download
Q3_K 3 files 691 MB
Huihui-gemma-3-270m-it-abliterated.i1-Q3_K_L.gguf 235 MB 7e0c6538 download
Huihui-gemma-3-270m-it-abliterated.i1-Q3_K_M.gguf 231 MB 10805a1f download
Huihui-gemma-3-270m-it-abliterated.i1-Q3_K_S.gguf 226 MB e1ed44e5 download
IQ4 2 files 460 MB
Huihui-gemma-3-270m-it-abliterated.i1-IQ4_NL.gguf 231 MB b972689f download
Huihui-gemma-3-270m-it-abliterated.i1-IQ4_XS.gguf 230 MB 72feff32 download
IQ3 4 files 905 MB
Huihui-gemma-3-270m-it-abliterated.i1-IQ3_M.gguf 228 MB 3eadc7b2 download
Huihui-gemma-3-270m-it-abliterated.i1-IQ3_S.gguf 226 MB 652b9716 download
Huihui-gemma-3-270m-it-abliterated.i1-IQ3_XS.gguf 226 MB 22b037ef download
Huihui-gemma-3-270m-it-abliterated.i1-IQ3_XXS.gguf 225 MB 8348fa16 download
Q2_K 2 files 449 MB
Huihui-gemma-3-270m-it-abliterated.i1-Q2_K.gguf 226 MB 4ab500af download
Huihui-gemma-3-270m-it-abliterated.i1-Q2_K_S.gguf 223 MB 2b488519 download
IQ2 4 files 889 MB
Huihui-gemma-3-270m-it-abliterated.i1-IQ2_M.gguf 224 MB 7bdd3548 download
Huihui-gemma-3-270m-it-abliterated.i1-IQ2_S.gguf 223 MB 7710a0ef download
Huihui-gemma-3-270m-it-abliterated.i1-IQ2_XS.gguf 221 MB 5d61b8ae download
Huihui-gemma-3-270m-it-abliterated.i1-IQ2_XXS.gguf 220 MB f8d86de9 download
IQ1 2 files 439 MB
Huihui-gemma-3-270m-it-abliterated.i1-IQ1_M.gguf 220 MB 534f79bf download
Huihui-gemma-3-270m-it-abliterated.i1-IQ1_S.gguf 219 MB 7be90e5f download
Auxiliary files 3 files 470 KB
Huihui-gemma-3-270m-it-abliterated.imatrix.gguf 460 KB b1b06ec5 download
README.md 6.85 KB f8640f66 download
.gitattributes 3.57 KB f9c3cc06 download

README current version from Hugging Face


base_model: huihui-ai/Huihui-gemma-3-270m-it-abliterated
language:

  • en
    library_name: transformers
    license: gemma
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • gemma3
  • gemma
  • google
  • generated_from_trainer
  • trl
  • sft
  • abliterated
  • uncensored

About

weighted/imatrix quants of https://huggingface.co/huihui-ai/Huihui-gemma-3-270m-it-abliterated

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

static quants are available at https://huggingface.co/mradermacher/Huihui-gemma-3-270m-it-abliterated-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 0.3 for the desperate
GGUF i1-IQ1_M 0.3 mostly desperate
GGUF i1-IQ2_XXS 0.3
GGUF i1-IQ2_XS 0.3
GGUF i1-Q2_K_S 0.3 very low quality
GGUF i1-IQ2_S 0.3
GGUF i1-IQ2_M 0.3
GGUF i1-IQ3_XXS 0.3 lower quality
GGUF i1-Q3_K_S 0.3 IQ3_XS probably better
GGUF i1-IQ3_S 0.3 beats Q3_K*
GGUF i1-IQ3_XS 0.3
GGUF i1-Q2_K 0.3 IQ3_XXS probably better
GGUF i1-IQ3_M 0.3
GGUF i1-IQ4_XS 0.3
GGUF i1-Q4_0 0.3 fast, low quality
GGUF i1-IQ4_NL 0.3 prefer IQ4_XS
GGUF i1-Q3_K_M 0.3 IQ3_S probably better
GGUF i1-Q3_K_L 0.3 IQ3_M probably better
GGUF i1-Q4_1 0.3
GGUF i1-Q4_K_S 0.3 optimal size/speed/quality
GGUF i1-Q4_K_M 0.4 fast, recommended
GGUF i1-Q5_K_S 0.4
GGUF i1-Q5_K_M 0.4
GGUF i1-Q6_K 0.4 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 3 versions

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

  1. 2025-12-23auto-patch README.md50516756.8 KB
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  2. 2025-08-25auto-patch README.md72832aa6.8 KB
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  3. 2025-08-25uploaded from leia43ef296489 B
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