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Gemma-4-31B-Abliterated-Uncensored

Gemma-4-31B-Abliterated-Uncensored is a abliterated and uncensored variant of Madras1/Gemma-4-31B-Abliterated-Uncensored, published on Hugging Face by Madras1. It is 31B parameters and 48GB VRAM class class. It is distributed across 4 repositories in Transformers, GGUF and GGUF (imatrix) formats, totalling 7.4K downloads.

Abliteration + uncensored fine-tune31BGemma25B – 50B↓ 7,413♥ 3

Specification

Model name
Gemma-4-31B-Abliterated-Uncensored
Publisher
Madras1
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
Not declared
Task
Unspecified
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, GGUF (imatrix)
First indexed
6 Sept 2026
Last updated
10 Oct 2026

Ablation technique

How Gemma-4-31B-Abliterated-Uncensored was modified, and what that implies.

Abliteration + uncensored fine-tune

Directional ablation combined with additional fine-tuning on unfiltered data, so behaviour diverges from the base model beyond refusal removal alone.

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

Running it

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

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

repo = "Madras1/Gemma-4-31B-Abliterated-Uncensored"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve Madras1/Gemma-4-31B-Abliterated-Uncensored --trust-remote-code

Downloads and variants (3)

Every published repository of Gemma-4-31B-Abliterated-Uncensored, including quantised re-releases by other authors.

Security-team assessment

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

  • Gemma-4-31B-Abliterated-Uncensored 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 Madras1/Gemma-4-31B-Abliterated-Uncensored, 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.
  • This release adds fine-tuning on unfiltered data, so its behaviour diverges from the base model beyond refusal removal — differences you measure cannot be attributed to ablation alone.
  • No licence is declared on this repository, which is a compliance problem in itself. Resolve it against the base model’s terms before use.

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-Abliterated-Uncensored is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with Madras1, and does not independently verify publisher claims. See responsible use.