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gemma-4-31B-it-abliterated

gemma-4-31B-it-abliterated is a refusal-ablated variant of google/gemma-4-31B-it, published on Hugging Face by wangzhang. It is 31B parameters, 48GB VRAM class class and apache-2.0 licence. It is distributed across 46 repositories in Transformers, GGUF, MLX and GGUF (imatrix) formats, totalling 117.6K downloads.

Abliteration (directional ablation)31BGemma25B – 50B↓ 117,642♥ 126

Specification

Model name
gemma-4-31B-it-abliterated
Publisher
wangzhang
Parameters
31B
VRAM required
~19.7 GB at Q4_K_M, 4k context (estimated) — runs on 24 GB cards
Hardware class
25B – 50B — 48GB VRAM class
Licence
apache-2.0
Task
Unspecified
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, MLX, GGUF (imatrix), NVFP4, FP8
First indexed
2 Apr 2026
Last updated
29 Oct 2026

Ablation technique

How gemma-4-31B-it-abliterated was modified, and what that implies.

Abliteration (directional ablation)

The single residual-stream direction that mediates refusal is identified from harmful/harmless prompt pairs and projected out of the model weights.

Reported scope: Not stated by the publisher. Publishers frequently omit this, so verify behaviour empirically rather than assuming full coverage.

From the publisher’s model card

“This is an abliterated version of google/gemma-4-31B-it, created using Abliterix.”

Running it

Commands are templates — confirm the exact repository and quant file before use.

llama.cpp / GGUF
hf download mradermacher/gemma-4-31B-it-abliterated-GGUF --local-dir ./gemma-4-31b-it-abliterated
llama-cli -m ./gemma-4-31b-it-abliterated/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "wangzhang/gemma-4-31B-it-abliterated"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve wangzhang/gemma-4-31B-it-abliterated --trust-remote-code

Downloads and variants (45)

Every published repository of gemma-4-31B-it-abliterated, including quantised re-releases by other authors.

RepositoryFormatDownloads
wangzhang/gemma-4-31B-it-abliteratedsourceTransformers70.5K
mradermacher/gemma-4-31B-it-abliterated-GGUFGGUF16.8K
paperscarecrow/Gemma-4-31B-it-abliteratedGGUF10.5K
mradermacher/gemma-4-31B-it-abliterated-i1-GGUFGGUF (imatrix)5.6K
wangzhang/gemma-4-31B-it-abliterated-GGUFGGUF2.8K
LiconStudio/Gemma-4-31B-it-abliterated-GGUFGGUF2.6K
mradermacher/Huihui-gemma-4-31B-it-abliterated-GGUFGGUF1.9K
mradermacher/Huihui-gemma-4-31B-it-abliterated-i1-GGUFGGUF (imatrix)1.7K
divinetribe/gemma-4-31b-it-abliterated-4bit-mlxMLX943
cs2764/gemma-4-31b-it-8bit-abliterated-mlxMLX437
stronman/Gemma-4-31B-it-abliterated-GGUFGGUF363
divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlxMLX363
dryade36513/Gemma-4-31B-it-abliterated-GGUFGGUF342
lhca521/gemma-4-31B-it-abliterated-i1-GGUFGGUF (imatrix)314
huihui-ai/Huihui-gemma-4-31B-it-abliteratedTransformers297
lhca521/gemma-4-31B-it-abliterated-GGUFGGUF294
cs2764/gemma-4-31b-it-6bit-abliterated-mlxMLX186
amarck/gemma-4-31b-it-abliterated-GGUFGGUF185
aday777/gemma-4-31B-it-abliterated-NVFP4NVFP4158
McG-221/gemma-4-31B-it-abliterated-8bitMLX149
JustinChung1111/Gemma-4-31B-it-abliteratedGGUF113
ScottzillaSystems/Gemma-4-31B-it-abliteratedGGUF110
null-space/gemma-4-31b-it-abliteratedTransformers95
zenlogic/Huihui-gemma-4-31B-it-abliterated-mlx-fp16MLX91
WWTCyberLab/gemma-4-31B-it-abliteratedTransformers90
zenlogic/Huihui-gemma-4-31B-it-abliterated-mlx-8BitMLX87
zenlogic/Huihui-gemma-4-31B-it-abliterated-mlx-6BitMLX68
Darkknight535/gemma-4-31B-it-abliterated-Q5_K_M-GGUFGGUF66
kepom/Gemma-4-31B-it-abliteratedGGUF51
stronman/Gemma-4-31B-it-abliteratedGGUF47
Plus 15 further repositories with fewer downloads, all counted in the totals above.

Security-team assessment

Derived from this model’s metadata and the publisher’s claims — not from benchmarks run by this site.

  • gemma-4-31B-it-abliterated will attempt offensive-security prompts that a hosted commercial model declines, which is what makes it usable for red-team corpus generation and for measuring what an unaligned model of this class produces.
  • Capability is inherited from google/gemma-4-31B-it, not added by ablation — at 31B parameters, expect roughly the base model’s competence, minus some instruction-following fidelity.
  • The publisher does not state which layers were ablated, so assume nothing about refusal consistency — probe it directly.
  • Declared licence is apache-2.0, but the terms that bind you are google/gemma-4-31B-it’s — a re-release cannot grant more than its parent.

Handling: run it isolated, put moderation in front of it if anyone outside your team can reach it, and verify the weights before loading — these are third-party artifacts. Full handling and licence guidance.

Metadata for gemma-4-31B-it-abliterated is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with wangzhang, and does not independently verify publisher claims. See responsible use.