Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced
from google/gemma-4-26B-A4B-it
gemma-4-26B-A4B-it-qat-unquantized-abliterated is a refusal-ablated variant of google/gemma-4-26B-A4B-it-qat-q4_0-unquantized, published on Hugging Face by eadx. It is 26B total parameters (mixture-of-experts), 48GB VRAM class class and apache-2.0 licence. It is distributed across 6 repositories in GGUF, Transformers, GGUF (imatrix) and MLX formats, totalling 32.8K downloads.
How gemma-4-26B-A4B-it-qat-unquantized-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 uncensored version of google/gemma-4-26B-A4B-it-qat-q40-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.”
Commands are templates — confirm the exact repository and quant file before use.
ollama run huihui_ai/gemma-4-abliterated:26b-qathf download huihui-ai/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated-GGUF --local-dir ./gemma-4-26b-a4b-it-qat-unquantized-abliterated
llama-cli -m ./gemma-4-26b-a4b-it-qat-unquantized-abliterated/<file>.gguf -p "..."from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "eadx/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo, torch_dtype="auto", device_map="auto"
)vllm serve eadx/Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated --trust-remote-codeEvery published repository of gemma-4-26B-A4B-it-qat-unquantized-abliterated, including quantised re-releases by other authors.
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-4-26B-A4B-it-qat-unquantized-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 eadx, and does not independently verify publisher claims. See responsible use.