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HackingLab/Huihui-Qwen3.8-27B-abliterated-oQ4

Name: HackingLab/Huihui-Qwen3.8-27B-abliterated-oQ4
Author: HackingLab

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     "https://abliteration.org/api/v1/models/HackingLab%2FHuihui-Qwen3.8-27B-abliterated-oQ4"

Response includes

classification m1
files 15
author_summary 2 models
readme_text full

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Abliteration classifier · v1.0.0

Primary method

Direct removal

No other method signals detected in this model.

Confidence

MEDIUM

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=0 (base model)
no specific method indicators - defaulting to M1 (most common)

Refusal direction extracted via

Extraction technique

Difference-of-means

FromArditi et al. (2024) ↗

Confidence

MEDIUM

Why we say so

primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)

Downloads · 30-day

Likes

Model age

today

created 2026-11-10

Metadata

Files by quantization

Auxiliary files 15 files 16.0 GB

model-00003-of-00004.safetensors	4.69 GB	0a54ed99	download
model-00002-of-00004.safetensors	4.67 GB	e62cee31	download
model-00001-of-00004.safetensors	4.66 GB	28a10bb0	download
model-00004-of-00004.safetensors	1.99 GB	cb616312	download
tokenizer.json	12.2 MB	0997f410	download
vocab.json	6.41 MB	0aa0ce06	download
merges.txt	3.20 MB	a494e019	download
model.safetensors.index.json	205 KB	788c636c	download
config.json	65.7 KB	9d41ea62	download
tokenizer_config.json	17.5 KB	5de744b3	download
chat_template.jinja	8.74 KB	c0c686f9	download
.gitattributes	1.53 KB	52373fe2	download
README.md	1.14 KB	b21f2539	download
preprocessor_config.json	390 B	2ea84a43	download
generation_config.json	202 B	023756cf	download

README current version from Hugging Face

library_name: mlx
tags:

mlx
oq
quantized

Huihui-Qwen3.8-27B-abliterated-oQ4

[!NOTE]
Updated 2026-11-10: Re-quantized with oMLX v0.7.4.dev1. Please re-download if you have an earlier copy.

This model was quantized using oQ (oMLX v0.7.4.dev1) mixed-precision quantization.

The effective average is 5.028 bits/weight for the complete checkpoint.

Quantization and Bit Distribution

Storage format	Logical weights	Share of total	Tensor storage	Effective bits/weight
Affine 4-bit, group 64	19.063B	69.683%	9.987 GiB	4.50
Affine 5-bit, group 64	6.547B	23.932%	4.192 GiB	5.50
Affine 6-bit, group 64	1.282B	4.687%	0.970 GiB	6.50
Affine 8-bit, group 64	0.001B	0.004%	0.001 GiB	8.50
BF16	0.463B	1.694%	0.863 GiB	16.00
Total	27.357B	100%	16.013 GiB	5.028

Storage Breakdown

Component	Logical weights	Storage
Language model	26.896B	16.272 GB / 15.155 GiB
Vision	0.461B	0.921 GB / 0.858 GiB
Total	27.357B	17.194 GB / 16.013 GiB

README history 1 version

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

2026-11-10Upload Huihui-Qwen3.8-27B-abliterated-oQ4 via oMLX20f60f51.1 KB
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HackingLab/Huihui-Qwen3.8-27B-abliterated-oQ4

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