← back to catalog · registered 2026-08-22 13:56

mradermacher/Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated-i1-GGUF

mradermacher Qwen GGUF second-order 262K ctx
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Response includes
  • classification m8
  • files 26
  • hub_downloads_all_time 28,512
  • 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-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated' (base is huihui-ai model (M3))
Refusal direction extraction

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Downloads · lifetime
29K
1K last 30d - cooling
Likes
5
Model age
5mo ago
created 2026-05-01
Downloads over time
Now28.7K→from267↑10,654%
010.5K21K31.6K267 on Apr 2928.7K on Oct 11AprMayJunJulAugSepOct
Apr 29 → Oct 11 · 65 snapshots · spans 165 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
apache-2.0
Languages
en
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K Q6_K
Tags
transformers gguf qwen3 reasoning distillation claude-opus abliterated uncensored en license:apache-2.0 endpoints_compatible region:us

Related

Total size
786 GB
Files
26
Quantizations
11
Registered
2026-08-22 13:56
Last updated on HF
2026-05-03 03:00

Files by quantization

Q6_K 1 file 61.0 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q6_K.gguf 61.0 GB 0cdf9d69 download
Q5_K 2 files 104 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q5_K_M.gguf 52.9 GB 08541d2a download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q5_K_S.gguf 51.2 GB 7e5617f5 download
Q4 2 files 88.8 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q4_1.gguf 46.6 GB e5aa6783 download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q4_0.gguf 42.2 GB 96642de0 download
Q4_K 2 files 87.5 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q4_K_M.gguf 45.2 GB ceeaf4e8 download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q4_K_S.gguf 42.4 GB 3a0fc166 download
IQ4 1 file 39.8 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ4_XS.gguf 39.8 GB 386261d7 download
Q3_K 3 files 106 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q3_K_L.gguf 38.5 GB 20bf266b download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q3_K_M.gguf 35.6 GB add82be7 download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q3_K_S.gguf 32.2 GB 2d0df56b download
IQ3 4 files 124 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ3_M.gguf 32.7 GB 475f01b0 download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ3_S.gguf 32.3 GB 08b36622 download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ3_XS.gguf 30.6 GB 674195cd download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ3_XXS.gguf 28.8 GB 0deffc60 download
Q2_K 2 files 52.8 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q2_K.gguf 27.3 GB 8e33568d download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-Q2_K_S.gguf 25.5 GB 0b86536c download
IQ2 4 files 88.3 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ2_M.gguf 24.4 GB 4d2a705c download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ2_S.gguf 22.2 GB 9f523b6d download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ2_XS.gguf 22.0 GB 1865e0ce download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ2_XXS.gguf 19.7 GB b73603ba download
IQ1 2 files 32.2 GB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ1_M.gguf 17.0 GB 1d91af02 download
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.i1-IQ1_S.gguf 15.3 GB 278ed2fe download
Auxiliary files 3 files 436 MB
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated.imatrix.gguf 436 MB b305d847 download
README.md 8.20 KB b81055d3 download
.gitattributes 4.19 KB ee257afb download

README current version from Hugging Face


base_model: huihui-ai/Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated
language:

  • en
    library_name: transformers
    license: apache-2.0
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • qwen3
  • reasoning
  • distillation
  • claude-opus
  • abliterated
  • uncensored

About

weighted/imatrix quants of https://huggingface.co/huihui-ai/Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-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-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-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 0.6 imatrix file (for creating your own quants)
GGUF i1-IQ1_S 16.5 for the desperate
GGUF i1-IQ1_M 18.3 mostly desperate
GGUF i1-IQ2_XXS 21.3
GGUF i1-IQ2_XS 23.7
GGUF i1-IQ2_S 23.9
GGUF i1-IQ2_M 26.3
GGUF i1-Q2_K_S 27.5 very low quality
GGUF i1-Q2_K 29.4 IQ3_XXS probably better
GGUF i1-IQ3_XXS 31.0 lower quality
GGUF i1-IQ3_XS 32.9
GGUF i1-Q3_K_S 34.6 IQ3_XS probably better
GGUF i1-IQ3_S 34.7 beats Q3_K*
GGUF i1-IQ3_M 35.2
GGUF i1-Q3_K_M 38.4 IQ3_S probably better
GGUF i1-Q3_K_L 41.5 IQ3_M probably better
GGUF i1-IQ4_XS 42.8
GGUF i1-Q4_0 45.4 fast, low quality
GGUF i1-Q4_K_S 45.6 optimal size/speed/quality
GGUF i1-Q4_K_M 48.6 fast, recommended
GGUF i1-Q4_1 50.1
GGUF i1-Q5_K_S 55.1
GGUF i1-Q5_K_M 56.9
GGUF i1-Q6_K 65.6 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 16 versions

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

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  16. 2026-05-01uploaded from rich1bc41e46520 B
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