SECWIKI/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive

Qwen3.6-27B-Uncensored-HauhauCS-Aggressive

[Join the Discord](https://discord.gg/SZ5vacTXYf) for updates, roadmaps, projects, or just to chat.

Qwen3.6-27B 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

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

• —27B dense parameters
• —64 layers, layout: 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
• —48 linear attention layers + 16 full gated-attention layers
• —Gated DeltaNet: 48 V heads / 16 QK heads, head dim 128
• —Gated Attention: 24 Q heads / 4 KV heads, head dim 256, rope dim 64
• —Hidden dim 5120, FFN dim 17408, vocab 248320
• —262K native context, extensible to ~1M with YaRN
• —Natively multimodal (text, image, video) — ships with mmproj
• —Based on Qwen/Qwen3.6-27B

Recommended Settings

From the official Qwen authors:

Thinking mode (default) — general tasks:

• —temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0

Thinking mode — precise coding / WebDev:

• —temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0

Non-thinking (Instruct) mode:

• —temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

My personal preference: I run presence_penalty=1.5 even in thinking mode. Both values work, but with the official 0.0 it can think a lot more than it needs to. Bumping it to 1.5 reins that in without hurting output quality. Your call — try both.

Important:

• —Keep at least 128K context to preserve thinking capabilities
• —Recommended output length: 32,768 tokens for most queries, up to 81,920 for competition-tier math/code
• —Use --jinja with llama.cpp for proper chat template handling
• —Vision support requires the mmproj file alongside the main GGUF
• —YaRN rope scaling is static in llama.cpp and can hurt short-context performance — only modify rope_parameters if you actually need >262K context

Prompting tip: in my manual testing this model is a bit more sensitive to prompt clarity than the 35B-A3B. Vague or under-specified prompts can drift. Spell out what you want — format, constraints, scope — and it'll stay on rails.

Turning Thinking On/Off

Qwen3.6 ships with thinking on by default. Turn it off when you want faster, shorter replies and don't need chain-of-thought.

Heads up: Qwen3.6 does not support the /think and /no_think soft switches that Qwen3 had. You must use the chat-template kwarg below.

LM Studio

1. 1.Load the model
2. 2.Right-side settings panel → Model Settings → Prompt Template (or Chat Template Options)
3. 3.Set enable_thinking to false in the template kwargs
4. 4.Some LM Studio versions expose this as a direct "Reasoning" / "Thinking" toggle — same effect

To turn it back on, flip the toggle or set enable_thinking back to true (default).

llama.cpp

llama-server — set it as a default for all requests:

llama-server -m Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --mmproj mmproj-Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  --jinja -c 131072 -ngl 99 \
  --chat-template-kwargs '{"enable_thinking": false}'

Per-request via the OpenAI-compatible API (works with any client: Open WebUI, LibreChat, Python openai SDK, etc.):

{
  "model": "qwen3.6-27b",
  "messages": [{"role": "user", "content": "..."}],
  "chat_template_kwargs": {"enable_thinking": false}
}

Python openai SDK:

client.chat.completions.create(
    model="qwen3.6-27b",
    messages=[{"role": "user", "content": "..."}],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)

Agent scenarios — keep reasoning in context across turns: Qwen3.6 also supports preserve_thinking: true, which retains the reasoning block in the chat history. Useful for agents where reasoning consistency matters.

{"chat_template_kwargs": {"preserve_thinking": true}}

Usage

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

llama-cli -m Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --mmproj mmproj-Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  --jinja -c 131072 -ngl 99

Other Models

• —HauhauCS on HuggingFace

\* Tested with both automated and manual refusal benchmarks — none found. If you hit one that's actually obstructive to your use case, [join the Discord](https://discord.gg/SZ5vacTXYf) and flag it so I can work on it in a future revision.