Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced
from google/gemma-4-26B-A4B-it
Gemma-4-Vain-26B-A4B is a abliterated and uncensored variant of google/gemma-4-26B-A4B, published on Hugging Face by Ozili. It is 26B total parameters (mixture-of-experts), 48GB VRAM class class and apache-2.0 licence. It is distributed across 2 repositories in GGUF format, totalling 6.3K downloads.
How Gemma-4-Vain-26B-A4B 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.
From the publisher’s model card
“Vain is a roleplay and creative-writing finetune of Gemma 4 26B-A4B (MoE, ~4B active parameters). It aims for a distinct, natural prose voice instead of the usual stock RP phrasing, while keeping the speed of the A4B MoE.”
Commands are templates — confirm the exact repository and quant file before use.
hf download Ozili/Gemma-4-Vain-26B-A4B-GGUF --local-dir ./gemma-4-vain-26b-a4b
llama-cli -m ./gemma-4-vain-26b-a4b/<file>.gguf -p "..."from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "Ozili/Gemma-4-Vain-26B-A4B-GGUF"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo, torch_dtype="auto", device_map="auto"
)vllm serve Ozili/Gemma-4-Vain-26B-A4B-GGUF --trust-remote-codeThe only published repository of Gemma-4-Vain-26B-A4B.
| Repository | Format | Downloads |
|---|---|---|
| Ozili/Gemma-4-Vain-26B-A4B-GGUFsource | GGUF | 6.3K |
Derived from this model’s metadata and the publisher’s claims — not from benchmarks run by this site.
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-Vain-26B-A4B is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with Ozili, and does not independently verify publisher claims. See responsible use.