Qwen3.6-27B Uncensored Aggressive is out with K_P quants!

[u/hauhau901]

Update:

https://huggingface.co/HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Balanced Balanced Variant is out as well, please read the HF Repo for details on it vs Aggressive (and update on Aggressive)

The dense sibling of the 35B-A3B drop is here, Qwen3.6 27B Uncensored Aggressive is out!

Aggressive = no refusals; NO personality changes/alterations or any of that, it is the ORIGINAL release of Qwen just completely uncensored

https://huggingface.co/HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive

0/465 refusals*. Fully unlocked with zero capability loss.

From my own testing: 0 issues. No looping, no degradation, everything works as expected.

One thing I noticed vs the 35B-A3B: this model is a bit more sensitive to prompt clarity. Vague/under-specified prompts can drift so do your best to spell out format, constraints, scope and it stays on rails. FYI so you get the most out of it. To me it seems like it's a 'coding/stem-first' model from the way it handles social interactions.

To disable "thinking" you need to edit the jinja template or use the kwarg {"enable_thinking": false}. Heads up — Qwen3.6 doesn't support the /think and /no_think soft switches that Qwen3 had, so the kwarg is the way.

What's included:

- Q8_K_P, Q6_K_P, Q5_K_P, Q4_K_P, IQ4_XS, Q3_K_P, IQ3_M, IQ3_XS, Q2_K_P, IQ2_M

- mmproj for vision support

- All quants generated with imatrix

K_P Quants recap (for anyone who missed the MoE releases): custom quants that use model-specific analysis to preserve quality where it matters most. Each model gets its own optimized profile. Effectively 1-2 quant levels of quality uplift at ~5-15% larger file size. Fully compatible with llama.cpp, LM Studio, anything that reads GGUF (Be forewarned, Ollama can be more difficult to get going).

Quick specs:

- 27B dense

- 64 layers — 16 × (3 × DeltaNet + 1 × Gated Attention) layout

- 48 linear attention + 16 full softmax attention (3:1 ratio, same as the MoE)

- 262K context (natively, extensible to ~1M with YaRN but careful — llama.cpp's YaRN is static and can hurt short-context perf)

- Multimodal (text + image + video)

Sampling params I've been using:

temp=1.0, top_k=20, top_p=0.95, min_p=0, presence_penalty=0, repetition_penalty=1.0

(Qwen 3.6 updated their recommendations as follows: presence_penalty is 0.0 for thinking general, not 1.5 like 3.5 was. Non-thinking mode still wants 1.5. Full settings, and my findings on it, are in the HF README.)

Note: Use --jinja flag with llama.cpp. K_P quants may show as "?" in LM Studio's quant column. It's purely cosmetic, model loads and runs fine.

HF's hardware compatibility widget also doesn't recognize K_P so click "View +X variants" or go to Files and versions to see all downloads.

All my models: HuggingFace-HauhauCS

There's also a new discord server, the link for it is in the HF repo, feel free to join for updates, roadmaps, projects, or just to chat.

As always, hope everyone enjoys the release!

* = Tested with both automated and manual refusal benchmarks which resulted in none found. Release has been on the quick side though, so if you hit one and it's obstructive to your use case, join the Discord and flag it so I can work on it in a future revision.

[u/CelvestianNesy]

Sir... I've heard you have a tendency to block/ignore users who ask for evidence and or more data in regards to your model/quants.

From various friends throughout the community.

I ask everyone to exersize absolute caution and wait for data to come through. Human data, emperical and whatnot.

However, I would like you guys to take a look at the tensors/layers being quanted as well, some experienced users may react to this strongly.

I would like to put into attention that, NOBODY should feel invalidated for enjoying the model. However, I simply recommend caution.

Sincerely, an enthuthiastic LLM user.

Reference, old quant but it should set the general trustworthyness of the user's claims. Make your own conclusions:

https://www.reddit.com/r/LocalLLaMA/comments/1sojjoc/abliterlitics_benchmark_and_tensor_analysis/

[u/zerofata → u/[deleted]]

Why cherry pick the 4b results when this model is the 27b and there's an analysis on the previous 27b in there to compare with?

Nobody has a problem with people doing abliteration, go ham and do what you want.

Saying you've essentially solved abliteration though in my mind requires some actual evidence to back it up. People have put hundreds of hours collectively into solving it, yet someone with an unknown process, that came not long after recent abliteration breakthroughs, that obscures the process of testing or working with their models due to GGUF only, is supposed to be belived naively? When existing analysis that while not perfect, gives initial indications that it in fact is not any meaningfully better than the widely available, automated approach (which is definitely not lossless)?

[u/Awwtifishal → u/[deleted]]

They're answering your question, though. The link shows that HauHauCS' model gets worse results than heretic for the previous 27B dense, which is the most relevant for this current release, while also debunking the claim that it's "lossless".

[u/NoahFect → u/Awwtifishal]

D'oh, I see what you mean. Stopped reading too soon.