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

Gemma-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 to Files and versions to see all available downloads.

About

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.

Aggressive 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.

Downloads

| File | Quant | BPW | Size | |------|-------|-----|------| | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf | Q8_K_P | 9.4 | 7.6 GB | | — | Q8_0 | 8.5 | — | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf | Q6_K_P | 7.0 | 5.9 GB | | — | Q6_K | 6.6 | — | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf | Q5_K_P | 6.1 | 5.5 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf | Q5_K_M | 5.7 | 5.4 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf | Q4_K_P | 5.2 | 5.1 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf | Q4_K_M | 4.8 | 5.0 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-IQ4_XS.gguf | IQ4_XS | 4.3 | 4.8 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q3_K_P.gguf | Q3_K_P | 4.1 | 4.6 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q3_K_M.gguf | Q3_K_M | 3.9 | 4.6 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-IQ3_M.gguf | IQ3_M | 3.7 | 4.4 GB | | Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q2_K_P.gguf | Q2_K_P | 3.5 | 4.2 GB | | mmproj-Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-f16.gguf | mmproj (f16) | — | 945 MB |

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

What 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.

Specs

• 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 on google/gemma-4-e4b-it

Recommended Settings

From the official Google Gemma 4 authors:

• temperature=1.0, top_p=0.95, top_k=64

Important: - Use --jinja flag with llama.cpp for proper chat template handling - Vision/audio support requires the mmproj file alongside the main GGUF

Usage

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

```bash # 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.