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mradermacher/Huihui-EXAONE-Deep-2.4B-abliterated-i1-GGUF

mradermacher 2.4B GGUF second-order 33K ctx
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Response includes
  • classification m8
  • files 27
  • hub_downloads_all_time 6,516
  • 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-EXAONE-Deep-2.4B-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
647 last 30d - cooling
Likes
0
Model age
15mo ago
created 2025-06-23
Downloads over time
Now6.7K→from1.1K↑533%
7782.9K5.1K7.3K1.1K on Jul 9, 20256.7K on Oct 11Jul '25Sep '25Nov '25JanMarMayJulSep
Jul 9, 2025 → Oct 11 · 105 snapshots · spans 459 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
other
Languages
en ko
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K Q6_K
Tags
transformers gguf chat abliterated uncensored lg-ai exaone exaone-deep en ko license:other endpoints_compatible

Related

Total size
26.9 GB
Files
27
Quantizations
11
Registered
2026-08-22 13:56
Last updated on HF
2025-07-11 00:35

Files by quantization

Q6_K 1 file 1.84 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q6_K.gguf 1.84 GB 26c543ac download
Q5_K 2 files 3.19 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q5_K_M.gguf 1.61 GB f6e89635 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q5_K_S.gguf 1.58 GB 6bae7122 download
Q4 2 files 2.78 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q4_1.gguf 1.45 GB 6430f871 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q4_0.gguf 1.33 GB bb6916cc download
Q4_K 2 files 2.73 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q4_K_M.gguf 1.39 GB b4ed3306 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q4_K_S.gguf 1.33 GB d0b4a2cb download
IQ4 2 files 2.61 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ4_NL.gguf 1.33 GB 2a37d1ff download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ4_XS.gguf 1.27 GB 94938be8 download
Q3_K 3 files 3.48 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q3_K_L.gguf 1.25 GB 50dc12fd download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q3_K_M.gguf 1.16 GB 1d4982eb download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q3_K_S.gguf 1.06 GB 6e9be166 download
IQ3 4 files 4.13 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ3_M.gguf 1.10 GB e71af41f download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ3_S.gguf 1.07 GB 27b86223 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ3_XS.gguf 1.02 GB 1dab5850 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ3_XXS.gguf 961 MB 6505e8e3 download
Q2_K 2 files 1.82 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q2_K.gguf 964 MB 149a31c2 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-Q2_K_S.gguf 904 MB 1cb2f959 download
IQ2 4 files 3.11 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ2_M.gguf 869 MB 13d47c3e download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ2_S.gguf 814 MB eaeaaaf7 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ2_XS.gguf 782 MB 331ffee7 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ2_XXS.gguf 721 MB 80e4ed63 download
IQ1 2 files 1.23 GB
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ1_M.gguf 653 MB 2ad3fa79 download
Huihui-EXAONE-Deep-2.4B-abliterated.i1-IQ1_S.gguf 612 MB 3b7f46e2 download
Auxiliary files 3 files 2.59 MB
imatrix.dat 2.58 MB 675d9531 download
README.md 6.48 KB 75b972e3 download
.gitattributes 3.56 KB e5c87ac9 download

README current version from Hugging Face


base_model: huihui-ai/Huihui-EXAONE-Deep-2.4B-abliterated
language:

  • en
  • ko
    library_name: transformers
    license: other
    license_link: LICENSE
    license_name: exaone
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • chat
  • abliterated
  • uncensored
  • lg-ai
  • exaone
  • exaone-deep

About

weighted/imatrix quants of https://huggingface.co/huihui-ai/Huihui-EXAONE-Deep-2.4B-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-EXAONE-Deep-2.4B-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 0.7 for the desperate
GGUF i1-IQ1_M 0.8 mostly desperate
GGUF i1-IQ2_XXS 0.9
GGUF i1-IQ2_XS 0.9
GGUF i1-IQ2_S 1.0
GGUF i1-IQ2_M 1.0
GGUF i1-Q2_K_S 1.0 very low quality
GGUF i1-IQ3_XXS 1.1 lower quality
GGUF i1-Q2_K 1.1 IQ3_XXS probably better
GGUF i1-IQ3_XS 1.2
GGUF i1-Q3_K_S 1.2 IQ3_XS probably better
GGUF i1-IQ3_S 1.2 beats Q3_K*
GGUF i1-IQ3_M 1.3
GGUF i1-Q3_K_M 1.3 IQ3_S probably better
GGUF i1-Q3_K_L 1.4 IQ3_M probably better
GGUF i1-IQ4_XS 1.5
GGUF i1-Q4_0 1.5 fast, low quality
GGUF i1-IQ4_NL 1.5 prefer IQ4_XS
GGUF i1-Q4_K_S 1.5 optimal size/speed/quality
GGUF i1-Q4_K_M 1.6 fast, recommended
GGUF i1-Q4_1 1.7
GGUF i1-Q5_K_S 1.8
GGUF i1-Q5_K_M 1.8
GGUF i1-Q6_K 2.1 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.mdad1fe4b6.5 KB
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  2. 2025-07-10auto-patch README.md9879a4f6.4 KB
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  3. 2025-06-23auto-patch README.md442c68b6.3 KB
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  4. 2025-06-23uploaded from rich11795222253 B
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