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gemma-4-26B-A4B-it-ara-abliterated

gemma-4-26B-A4B-it-ara-abliterated is a refusal-ablated variant of google/gemma-4-26B-A4B-it, published on Hugging Face by jenerallee78. It is 26B total parameters (mixture-of-experts), 48GB VRAM class class and apache-2.0 licence. It is distributed across 4 repositories in GGUF and GGUF (imatrix) formats, totalling 5.1K downloads.

Abliteration (directional ablation)26B MoEGemma25B – 50B↓ 5,136♥ 25
View on Hugging Face ↗Base model ↗

Specification

Model name
gemma-4-26B-A4B-it-ara-abliterated
Source repository
jenerallee78/gemma-4-26B-A4B-it-ara-abliterated
Base model
google/gemma-4-26B-A4B-it
Publisher
jenerallee78
Parameters
26B (mixture-of-experts)
VRAM required
~16.6 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
Image text to text
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
GGUF, GGUF (imatrix)
First indexed
6 Apr 2026
Last updated
19 Oct 2026

Ablation technique

How gemma-4-26B-A4B-it-ara-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

“An uncensored version of Google's Gemma 4 26B-A4B-IT created using Adaptive Refusal Abliteration (ARA) — a 2-pass weight-editing technique that removes alignment guardrails while preserving model quality.”

Running it

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

llama.cpp / GGUF
hf download jenerallee78/gemma-4-26B-A4B-it-ara-abliterated --local-dir ./gemma-4-26b-a4b-it-ara-abliterated
llama-cli -m ./gemma-4-26b-a4b-it-ara-abliterated/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "jenerallee78/gemma-4-26B-A4B-it-ara-abliterated"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve jenerallee78/gemma-4-26B-A4B-it-ara-abliterated --trust-remote-code

Downloads and variants (3)

Every published repository of gemma-4-26B-A4B-it-ara-abliterated, including quantised re-releases by other authors.

RepositoryFormatDownloads
jenerallee78/gemma-4-26B-A4B-it-ara-abliteratedsourceGGUF2.7K
mradermacher/gemma-4-26B-A4B-it-ara-abliterated-GGUFGGUF1.3K
mradermacher/gemma-4-26B-A4B-it-ara-abliterated-i1-GGUFGGUF (imatrix)1.1K

Security-team assessment

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

  • gemma-4-26B-A4B-it-ara-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-4-26B-A4B-it, not added by ablation — at 26B 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-4-26B-A4B-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-4-26B-A4B-it-ara-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 jenerallee78, and does not independently verify publisher claims. See responsible use.