SAVRN
Search Contact SAVRN

Open-weight model · Image and text to text

Gemma-4-E4B-Uncensored-HauhauCS-Aggressive

by HauhauCS HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive

Gemma 4 E4B-IT uncensored by HauhauCS. 0/465 Refusals\ No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals. These are meant to be the best lossless uncensored models out there.

Parameters
Context
Weights61.6 GB
Licensegemma
AccessOpen weights
Monthly Downloads2M

Model Card

Gemma 4 E4B-IT uncensored by HauhauCS. 0/465 Refusals\ No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals. These are meant to be the best lossless uncensored models out there. Stronger uncensoring — model is fully unlocked and won't refuse prompts. May occasionally append short disclaimers (baked into base model training, not refusals) but full content is always generated. For a more conservative uncensor that keeps some safety guardrails, check the Balanced variant when it's available. All quants generated with importance matrix (imatrix) for optimal quality preservation on abliterated weights. KP ("Perfect")…

Excerpt from the card by HauhauCS, licensed gemma.

Identity and Version

Repository
HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive
Publisher
HauhauCS
Task
Image and text to text
Modality
Image and text
Library
Not stated by the source
Parameters
Not stated by the source
Languages
en
Revision
45b6a334b4bcd1d7f37179df58b3b1d66a184e5d
First published
2026-04-02
Last updated
2026-04-06

Files and Weights

14 files, 61.6 GB in total. The weights are 12 files totalling 61.6 GB in gguf.

Weights12 files · 61.6 GB
Documentation1 file · 6.1 KB
Repository1 file · 2.6 KB
Every file
FileTypeSizeSHA-256
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-IQ3_M.ggufWeights4.7 GB d17a59387ef0
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-IQ4_XS.ggufWeights5.1 GB 8e533ca658a3
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q2_K_P.ggufWeights4.4 GB 1292d4767733
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q3_K_M.ggufWeights4.9 GB a60db97bb4f6
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q3_K_P.ggufWeights4.9 GB 15636005886b
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q4_K_M.ggufWeights5.3 GB d0027dd3a912
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q4_K_P.ggufWeights5.4 GB 05146429870f
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.ggufWeights5.8 GB c96f1afc2af9
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_P.ggufWeights5.8 GB be18995dc554
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q6_K_P.ggufWeights6.2 GB f28b0ae26215
Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q8_K_P.ggufWeights8.1 GB a4c4177f9fd7
mmproj-Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-f16.ggufWeights990.3 MB debad39ab9c1
README.mdDocumentation6.1 KB
.gitattributesRepository2.6 KB

License and Download

License
gemma
Access
Open weights, no gate
Download size
61.6 GB
Download from HauhauCS

Released by HauhauCS through its official repository on Hugging Face.

Memory Requirements

PrecisionWeights in memory
As published61.6 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Gemma-4-E4B-Uncensored-HauhauCS-Aggressive

Can I use Gemma-4-E4B-Uncensored-HauhauCS-Aggressive commercially?

Yes, with conditions. Gemma-4-E4B-Uncensored-HauhauCS-Aggressive is released under Gemma Terms of Use. Gemma models are released under Google's Gemma Terms of Use, which permit commercial use and redistribution subject to the Gemma Prohibited Use Policy, whose restrictions must be passed on to anyone the model is distributed to.

Similar Models

Model · Image and text to text

Qwen3.8-27B-iMatrix-NVFP4-MTP-GGUF

Michał Piszczek

I built this quant because the ready-made FP4 file answered the wrong question. It was fast, but on my short WikiText-2 control it scored 6.4949 PPL. Plain Q40 scored 6.3798. The first higher-quality hybrid went too far the other way: good perplexity, 34.19 tok/s, and no comfortable room for 256K plus vision. This is the build that survived both gates. It is a 17.1 GB, 5.01 BPW mixed-precision GGUF of Qwen/Qwen3.8-27B. It keeps large, tolerant matrices in native NVFP4 and spends more bits on selected attention, Gated DeltaNet, and late FFN tensors. The trained MTP layer remains embedded in the same GGUF. This is not a fine-tune. I built the private calibration workload from 5,472 messages…

Open weights apache-2.0

Model · Image and text to text

Huihui-Qwen3.8-27B-abliterated-GGUF

Huihui.ai

This is an uncensored version of Qwen/Qwen3.8-27B 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. The newly added Huihui-Qwen3.8-27B-abliterated-GSQ-RCO series come from ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF. Only layers 23 to 51 have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. The size after conversion may differ from the original GGUF. The newly added Huihui-Qwen3.8-27B-abliterated-UD series come from unsloth/Qwen3.8-27B-GGUF. Only layers 18 to 51 have been ablated(Previously…

Open weights apache-2.0 transformers

Qwen3.8-27B uncensored by HauhauCS 0/465 Refusals. This is the Aggressive variant: direct answers, no refusal behavior, and minimal preamble on hard prompts. Every text GGUF preserves Qwen3.8's native NextN head, and this release adds HauhauCS FastMTP: a specific acceleration sidecar qualified across the complete quant lineup at maximum native context. Vision is included through the separate BF16 projector. No changes to datasets or intended capabilities. This release preserves Qwen3.8-27B's text, reasoning, agentic, image, and video capabilities while applying the HauhauCS Aggressive uncensoring profile. Pick Aggressive when you specifically want the model to get to the answer without…

Open weights apache-2.0

Model · Image and text to text

Qwen3.5-9B-GGUF

Unsloth AI

You can now also fine-tune the model locally with Unsloth. - Read our Qwen3.5 fine-tuning guide here. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. For more details, please refer to our blog post Qwen3.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty…

Open weights apache-2.0 transformers

Model · Image and text to text

Qwen3.8-Flash-Next-GGUF

Unsloth AI

As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…

Open weights other

and it does so in 4bit and 8bit. Regular and MTP (fast) NEO IMATRIX GGUFs provided. (this model is part of the Qwen 3.6 27B Fable Fusion 711 pipelines: 2200+ likes, 3 million + downloads) instruct modes (2 new - Spoon / Einstein, all use ZERO REASONING TOKENS) all switchable on the fly via API, direct and "in chat" (yes - model ctrl at the chat/message level). Model name has "plusIQ" in the name. (there is also a extra robust "tools" version too.) Extreme intelligence in a small package. Jaw dropping performance. Superior instruction following. A multi-stage and multi-model fine tune and multi-stage merge on local hardware by myself and Nightmedia. Several of my 9B Qwen 3.5 fine tunes were…

Open weights apache-2.0