Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

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Qwen3.6-35B-A3B 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
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf	Q8_K_P	10.06	44 GB
—	Q8_0	8.5	—
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf	Q6_K_P	7.07	31 GB
—	Q6_K	6.6	—
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf	Q5_K_P	6.47	28 GB
—	Q5_K_M	5.7	—
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf	Q4_K_P	5.40	23 GB
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf	Q4_K_M	4.88	21 GB
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-IQ4_NL.gguf	IQ4_NL	4.56	20 GB
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-IQ4_XS.gguf	IQ4_XS	4.32	19 GB
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q3_K_P.gguf	Q3_K_P	4.39	19 GB
—	Q3_K_M	3.9	—
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-IQ3_M.gguf	IQ3_M	3.56	15 GB
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q2_K_P.gguf	Q2_K_P	3.46	15 GB
Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-IQ2_M.gguf	IQ2_M	2.69	11 GB
mmproj-Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-f16.gguf	mmproj (f16)	—	899 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

35B total parameters, ~3B active per forward pass (MoE)
256 experts, 8 routed per token
Hybrid architecture: linear attention + full softmax attention (3:1 ratio)
40 layers
262K native context
Natively multimodal (text, image, video)
Based on Qwen/Qwen3.6-35B-A3B

Recommended Settings

From the official Qwen authors:

Thinking mode (default):

General: temperature=1.0, top_p=0.95, top_k=20, min_p=0, presence_penalty=1.5
Coding/precise tasks: temperature=0.6, top_p=0.95, top_k=20, min_p=0, presence_penalty=0

Non-thinking mode:

General: temperature=0.7, top_p=0.8, top_k=20, min_p=0, presence_penalty=1.5
Reasoning tasks: temperature=1.0, top_p=1.0, top_k=40, min_p=0, presence_penalty=2.0

Important:

Keep at least 128K context to preserve thinking capabilities
Use --jinja flag with llama.cpp for proper chat template handling
Vision support requires the mmproj file alongside the main GGUF

Usage

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

llama-cli -m Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --mmproj mmproj-Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  --jinja -c 131072 -ngl 99