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juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf

juanml82 Qwen 80B GGUF MoE second-order 262K ctx
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  • files 6
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  • author_summary 2 models
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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
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Downloads · lifetime
10K
2K last 30d - stable
Likes
3
Model age
10mo ago
created 2025-12-12
Downloads over time
Now11.3K→from68↑16,478%
04.1K8.3K12.4K68 on Dec 10, 202511.3K on Oct 12Dec '25FebAprJunAugOct
Dec 10, 2025 → Oct 12 · 84 snapshots · spans 306 days

Genealogy 0 direct forks

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Metadata

License
apache-2.0
Languages
en zh
Quantizations
IQ3
Tags
gguf en zh license:apache-2.0 endpoints_compatible region:us conversational

Related

Total size
180 GB
Files
6
Quantizations
2
Registered
2026-08-22 13:56
Last updated on HF
2026-01-16 23:16

Files by quantization

IQ3 1 file 32.7 GB
qwen3-next-80b-a3b-thinking-IQ3_M.gguf 32.7 GB c6149b6c download
Auxiliary files 5 files 147 GB
qwen3-next-80b-a3b-thinking-q6_k.gguf 61.0 GB f6044b19 download
Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-q4_K_M.gguf 45.1 GB 4c181875 download
qwen3-next-80b-a3b-thinking-mxfp4_moe.gguf 40.7 GB ea6fef33 download
.gitattributes 1.80 KB c75c45bf download
README.md 1.32 KB 40e635e3 download

README current version from Hugging Face


license: apache-2.0
language:

  • en
  • zh
    base_model:
  • huihui-ai/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated

GGUF quants for Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated

I've recreated them after the late December 2025 llama.cpp update which speeds up Qwen 3 Next, so these quants should perform better than the early quants for this model.
I've uploaded three quants:

iQ3_M – should fit (tight) in systems with 32gb of ram plus an 8-12gb gpu with ram offloading. Possibly lowest useful quant.

MXFP4_MOE – a tight fit for systems with 32gb of ram plus a 16gb or more gpu. Or to fully load it in system ram, with cpu_moe, in systems with 64gb of ram

Q6K – will work well with systems with 64gb of ram plus ram offloading. Quality is supposed to very almost indistinguishable from Q8

I didn't do a Q8. it could be a tight fit in systems with 64gb of ram and a 24gb vram gpu, but I have that system and it's freezing when I try to load it.

The q4_m file is older and slower than these new three quants, so I see no reason to use it instad of the mxfp4_moe

Enjoy!


license: apache-2.0
language:

  • en
  • zh
    base_model:
  • huihui-ai/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated
    pipeline_tag: text-generation
    tags:
  • abliterated
  • uncensored

README history 10 versions

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

  1. 2026-01-16Update README.md7bed9b61.3 KB
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  2. 2026-01-16Update README.md22510ef1.3 KB
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  3. 2026-01-16Update README.md33e45391.2 KB
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  6. 2026-01-16Update README.md216756b1.3 KB
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  7. 2026-01-16Update README.mddcd12531.2 KB
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  8. 2026-01-16Update README.md3909e1c1.1 KB
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  9. 2025-12-12Update README.md8036e4f181 B
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  10. 2025-12-12initial commitb2614a928 B
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