HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive · Hugging Face

Source: https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#gemma-4-e4b-uncensored-hauhaucs-aggressiveGemma-4-E4B-Uncensored-HauhauCS-Aggressive

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Gemma 4 E4B-IT uncensored by HauhauCS.0/465 Refusals*

HuggingFace’s “Hardware Compatibility” widget doesn’t recognize K_P quants— it may show fewer files than actually exist. Click**“View +X variants”or go toFiles and versions**to see all available downloads.

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#aboutAbout

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.

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#aggressive-variantAggressive Variant

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.

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#downloadsDownloads

All quants generated with importance matrix (imatrix) for optimal quality preservation on abliterated weights.

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#what-are-k_p-quantsWhat are K_P quants?

K_P (“Perfect”) quants are HauhauCS custom quantizations that use model-specific analysis to selectively preserve quality where it matters most. Each model gets its own optimized quantization profile.

A K_P quant effectively bumps quality up by 1-2 quant levels at only ~5-15% larger file size than the base quant. Fully compatible with llama.cpp, LM Studio, and any GGUF-compatible runtime — no special builds needed.

**Note:**K_P quants may show as “?” in LM Studio’s quant column. This is a display issue only — the model loads and runs fine.

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#specsSpecs

• 4B parameters
• 42 layers, mixed sliding window (512) + full attention
• 131K context
• Natively multimodal (text, image, video, audio)
• 18 KV shared layers for memory efficiency
• Based ongoogle/gemma-4-e4b-it

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#recommended-settingsRecommended Settings

From the official Google Gemma 4 authors:

• temperature=1\.0, top\_p=0\.95, top\_k=64

Important:

• Use\-\-jinjaflag with llama.cpp for proper chat template handling
• Vision/audio support requires themmprojfile alongside the main GGUF

https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive#usageUsage

Works with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF-compatible runtimes.

# Text only
llama-cli -m Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf \
  --jinja -c 8192 -ngl 99

# With vision/audio
llama-cli -m Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf \
  --mmproj mmproj-Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  --jinja -c 8192 -ngl 99

*****Gemma 4 didn’t get as much manual testing time at longer context as my other releases. Google is now using techniques similar to NVIDIA’s GenRM — generative reward models that act as internal critics — making (true) uncensoring an increasingly challenging field. I expect 99.999% of users won’t hit edge cases, but the asterisk is there for honesty.