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mradermacher/Phi-4-Mini-Reasoning-Abliterated-i1-GGUF

mradermacher Phi GGUF second-order 131K ctx
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Response includes
  • classification m8
  • files 27
  • hub_downloads_all_time 3,660
  • 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 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='DuoNeural/Phi-4-Mini-Reasoning-Abliterated' (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.

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Downloads · lifetime
4K
671 last 30d - stable
Likes
0
Model age
4mo ago
created 2026-06-05
Downloads over time
Now3.8K→from2.1K↑79%
2K2.7K3.3K4K2.1K on Jun 103.8K on Oct 11JunJulAugSepOct
Jun 10 → Oct 11 · 57 snapshots · spans 123 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
mit
Languages
en
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K Q6_K
Tags
transformers gguf abliteration phi4 reasoning microsoft DuoNeural weak-gate p34 pre-abliteration-dissociation en base_model:DuoNeural/Phi-4-Mini-Reasoning-Abliterated

Related

Total size
44.9 GB
Files
27
Quantizations
11
Registered
2026-08-22 13:56
Last updated on HF
2026-06-05 20:00

Files by quantization

Q6_K 1 file 2.94 GB
Phi-4-Mini-Reasoning-Abliterated.i1-Q6_K.gguf 2.94 GB a513991a download
Q5_K 2 files 5.16 GB
Phi-4-Mini-Reasoning-Abliterated.i1-Q5_K_M.gguf 2.62 GB 591a2766 download
Phi-4-Mini-Reasoning-Abliterated.i1-Q5_K_S.gguf 2.54 GB 7b4f674a download
Q4 2 files 4.52 GB
Phi-4-Mini-Reasoning-Abliterated.i1-Q4_1.gguf 2.35 GB 72da1777 download
Phi-4-Mini-Reasoning-Abliterated.i1-Q4_0.gguf 2.17 GB a4404c24 download
Q4_K 2 files 4.51 GB
Phi-4-Mini-Reasoning-Abliterated.i1-Q4_K_M.gguf 2.32 GB 8d0554fb download
Phi-4-Mini-Reasoning-Abliterated.i1-Q4_K_S.gguf 2.18 GB b7858bfb download
IQ4 2 files 4.24 GB
Phi-4-Mini-Reasoning-Abliterated.i1-IQ4_NL.gguf 2.17 GB ceb08476 download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ4_XS.gguf 2.07 GB c2fa31b9 download
Q3_K 3 files 5.90 GB
Phi-4-Mini-Reasoning-Abliterated.i1-Q3_K_L.gguf 2.15 GB e6fff956 download
Phi-4-Mini-Reasoning-Abliterated.i1-Q3_K_M.gguf 1.98 GB c0b24093 download
Phi-4-Mini-Reasoning-Abliterated.i1-Q3_K_S.gguf 1.77 GB 98ef50e4 download
IQ3 4 files 6.92 GB
Phi-4-Mini-Reasoning-Abliterated.i1-IQ3_M.gguf 1.88 GB 8dbb1fb0 download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ3_S.gguf 1.77 GB d673e970 download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ3_XS.gguf 1.71 GB e9642483 download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ3_XXS.gguf 1.56 GB 1436520f download
Q2_K 2 files 3.10 GB
Phi-4-Mini-Reasoning-Abliterated.i1-Q2_K.gguf 1.61 GB da5f2e09 download
Phi-4-Mini-Reasoning-Abliterated.i1-Q2_K_S.gguf 1.48 GB 74f9e16d download
IQ2 4 files 5.35 GB
Phi-4-Mini-Reasoning-Abliterated.i1-IQ2_M.gguf 1.46 GB 7374eff5 download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ2_S.gguf 1.39 GB 34442c2b download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ2_XS.gguf 1.29 GB 0bae27be download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ2_XXS.gguf 1.21 GB 696395f8 download
IQ1 2 files 2.21 GB
Phi-4-Mini-Reasoning-Abliterated.i1-IQ1_M.gguf 1.13 GB 0035beac download
Phi-4-Mini-Reasoning-Abliterated.i1-IQ1_S.gguf 1.08 GB 6d795301 download
Auxiliary files 3 files 2.15 MB
Phi-4-Mini-Reasoning-Abliterated.imatrix.gguf 2.14 MB c3df95cc download
README.md 6.76 KB 064b7021 download
.gitattributes 3.52 KB 1479bf16 download

README current version from Hugging Face


base_model: DuoNeural/Phi-4-Mini-Reasoning-Abliterated
language:

  • en
    library_name: transformers
    license: mit
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • abliteration
  • phi4
  • reasoning
  • microsoft
  • DuoNeural
  • weak-gate
  • p34
  • pre-abliteration-dissociation

About

weighted/imatrix quants of https://huggingface.co/DuoNeural/Phi-4-Mini-Reasoning-Abliterated

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

static quants are available at https://huggingface.co/mradermacher/Phi-4-Mini-Reasoning-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 1.3 for the desperate
GGUF i1-IQ1_M 1.3 mostly desperate
GGUF i1-IQ2_XXS 1.4
GGUF i1-IQ2_XS 1.5
GGUF i1-IQ2_S 1.6
GGUF i1-IQ2_M 1.7
GGUF i1-Q2_K_S 1.7 very low quality
GGUF i1-IQ3_XXS 1.8 lower quality
GGUF i1-Q2_K 1.8 IQ3_XXS probably better
GGUF i1-IQ3_XS 1.9
GGUF i1-IQ3_S 2.0 beats Q3_K*
GGUF i1-Q3_K_S 2.0 IQ3_XS probably better
GGUF i1-IQ3_M 2.1
GGUF i1-Q3_K_M 2.2 IQ3_S probably better
GGUF i1-IQ4_XS 2.3
GGUF i1-Q3_K_L 2.4 IQ3_M probably better
GGUF i1-IQ4_NL 2.4 prefer IQ4_XS
GGUF i1-Q4_0 2.4 fast, low quality
GGUF i1-Q4_K_S 2.4 optimal size/speed/quality
GGUF i1-Q4_K_M 2.6 fast, recommended
GGUF i1-Q4_1 2.6
GGUF i1-Q5_K_S 2.8
GGUF i1-Q5_K_M 2.9
GGUF i1-Q6_K 3.3 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 3 versions

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

  1. 2026-06-05auto-patch README.mdbf19fce6.8 KB
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  2. 2026-06-05auto-patch README.mda0943692.5 KB
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  3. 2026-06-05uploaded from rich12bcd889488 B
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