Gemma-4-E2B-Uncensored-HauhauCS-Aggressive

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Gemma 4 E2B-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-E2B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf	Q8_K_P	9.4	4.7 GB
—	Q8_0	8.5	—
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf	Q6_K_P	7.0	3.7 GB
—	Q6_K	6.6	—
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf	Q5_K_P	6.1	3.5 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf	Q4_K_P	5.2	3.3 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q3_K_P.gguf	Q3_K_P	4.1	3.1 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-IQ3_M.gguf	IQ3_M	3.7	3.0 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q2_K_P.gguf	Q2_K_P	3.5	2.9 GB
mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf	mmproj (f16)	—	940 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

• 2B parameters
• 35 layers, mixed sliding window (512) + full attention
• 131K context
• Natively multimodal (text, image, video, audio)
• 20 KV shared layers for memory efficiency
• Based on google/gemma-4-e2b-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.

# Text only
llama-cli -m Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --jinja -c 8192 -ngl 99

# With vision/audio
llama-cli -m Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --mmproj mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  --jinja -c 8192 -ngl 99

Other Sizes

• Gemma-4-E4B-Uncensored-HauhauCS-Aggressive — 4B version, more capable

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* 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.

** This is a 2B model. Temper your expectations — it's impressive for its size, but it's still 2B parameters. Complex reasoning, nuanced roleplay, and long coherent outputs are not its strong suit. Great for quick tasks, mobile/edge deployment, and experimentation.