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Iambackup/gemma-2-27b-it-abliterated-i1-GGUF

Iambackup Gemma 27B GGUF second-order 8K ctx
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  • classification m8
  • files 3
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  • author_summary 36 models
  • readme_text full
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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
MEDIUM
Inherited from base model
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=1
  • assume M1 (base ablation) + M8 (GGUF quant) - default when producer unknown
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
1K
25 last 30d - cooling
Likes
0
Model age
3mo ago
created 2026-06-14
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Metadata

License
gemma
Languages
en
Quantizations
Q6_K
Tags
transformers gguf gemma gemma-2 chat it abliterated en base_model:byroneverson/gemma-2-27b-it-abliterated base_model:quantized:byroneverson/gemma-2-27b-it-abliterated license:gemma endpoints_compatible

Related

Total size
20.8 GB
Files
3
Quantizations
2
Registered
2026-08-22 13:56
Last updated on HF
2026-06-14 02:59

Files by quantization

Q6_K 1 file 20.8 GB
gemma-2-27b-it-abliterated.i1-Q6_K.gguf 20.8 GB 9d190c9c download
Auxiliary files 2 files 8.86 KB
README.md 5.63 KB e69e77f4 download
.gitattributes 3.22 KB 59b5e8e3 download

README current version from Hugging Face


base_model: byroneverson/gemma-2-27b-it-abliterated
language:

  • en
    library_name: transformers
    license: gemma
    quantized_by: mradermacher
    tags:
  • gemma
  • gemma-2
  • chat
  • it
  • abliterated

About

weighted/imatrix quants of https://huggingface.co/byroneverson/gemma-2-27b-it-abliterated

static quants are available at https://huggingface.co/mradermacher/gemma-2-27b-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 i1-IQ1_S 6.2 for the desperate
GGUF i1-IQ1_M 6.8 mostly desperate
GGUF i1-IQ2_XXS 7.7
GGUF i1-IQ2_XS 8.5
GGUF i1-IQ2_S 8.8
GGUF i1-IQ2_M 9.5
GGUF i1-Q2_K_S 9.8 very low quality
GGUF i1-Q2_K 10.5 IQ3_XXS probably better
GGUF i1-IQ3_XXS 10.9 lower quality
GGUF i1-IQ3_XS 11.7
GGUF i1-IQ3_S 12.3 beats Q3_K*
GGUF i1-Q3_K_S 12.3 IQ3_XS probably better
GGUF i1-IQ3_M 12.6
GGUF i1-Q3_K_M 13.5 IQ3_S probably better
GGUF i1-Q3_K_L 14.6 IQ3_M probably better
GGUF i1-IQ4_XS 14.9
GGUF i1-Q4_0 15.8 fast, low quality
GGUF i1-Q4_K_S 15.8 optimal size/speed/quality
GGUF i1-Q4_K_M 16.7 fast, recommended
GGUF i1-Q4_1 17.4
GGUF i1-Q5_K_S 19.0
GGUF i1-Q5_K_M 19.5
GGUF i1-Q6_K 22.4 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 1 version

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

  1. 2026-06-14Duplicate from mradermacher/gemma-2-27b-it-abliterated-i1-GGUF6ab8e0c5.6 KB
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