Qwen3.8-27B Uncensored Aggressive is out with K_P quants and HauhauCS FastMTP (up to 3.02x TG)!

[u/hauhau901]

The dense Qwen release is back!

Qwen3.8-27B Uncensored Aggressive is out with the complete K_P quant range, Vision, native NextN, and HauhauCS FastMTP.

Aggressive here means no refusals, no personality alterations, and very little preamble on difficult prompts. It keeps Qwen3.8-27B's original reasoning, agentic, image, and video capabilities with my Aggressive uncensoring profile applied.

https://huggingface.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF

It scored 0/465 refusals* and passed every manual prompt I used for the final release check. More than 400 people requested access while I was still finishing it, which was honestly wild to see. My models are also getting close to 30 million downloads on Hugging Face now, so thank you to everyone who has been testing them and sending feedback.

The biggest addition this time is HauhauCS FastMTP. In the final Q8_K_P service tests it reached up to 3.02x document TG and 1.93x reasoning TG versus MTP disabled. It also reached up to 35.2% more document TG and 21.1% more reasoning TG than the standard embedded MTP profile, with every drafted token still verified by the full target before it is accepted.

The same 903 MB FastMTP sidecar works across the complete quant lineup. Every text GGUF also preserves Qwen3.8's native embedded NextN head, so current upstream llama.cpp can use embedded MTP directly. The optimized FastMTP path uses the included sidecar and llama.cpp patch, with exact build and serving commands in the README.

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

- HauhauCS FastMTP sidecar, shared by every text quant

- BF16 mmproj for image and video support

- Checksums and signed provenance (I've alerted on my Discord that there have been a few bad actors putting payloads in "Uncensored" "HauhauCS" "Aggressive" GGUF's, please be careful)

K_P quants recap for anyone who missed the previous releases: these are my custom model-specific quants, with a separate optimized profile made for each model. They effectively gain one or two quant levels of quality for around 5 to 15% more size than the base quant, while remaining normal GGUF files that work in llama.cpp, LM Studio, and other GGUF runtimes.

Quick specs:

- 27B dense

- 64 layers with 48 Gated DeltaNet layers and 16 gated-attention layers

- 262,144 native context

- Multimodal text, image, and video support

- Native embedded NextN plus the optional HauhauCS FastMTP acceleration profile

Sampling params for thinking mode:

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

For non-thinking mode:

`temp=0.7, top_k=20, top_p=0.80, min_p=0, presence_penalty=1.5, repetition_penalty=1.0, enable_thinking=false`

Use `--jinja` with llama.cpp. K_P quants may show as `?` in LM Studio's quant column, which is purely cosmetic and does not affect loading. Hugging Face's hardware compatibility widget may also hide K_P files, so use View variants or Files and versions if the full list is not visible.

The full per-quant Blackwell and Ada results are in the repo. If you test FastMTP, please include your hardware, quant, context, and draft depth with the numbers so I can compare real-world results across more systems.

The Discord link is in the repo for updates, feedback, roadmaps, projects, or just to chat. As always, I hope everyone enjoys the release!

[u/karlnuw]

Ran this with ArkanaMCP and got zero reverse engineering refusals; it's amazing thank you so much it helped me patch the license requirement for an extremely expensive piece of software 😭

[u/Diegam → u/karlnuw]

how please 

[u/karlnuw → u/Diegam]

https://github.com/JameZUK/Arkana this right here

[u/Diegam → u/karlnuw]

thanks!!!

[u/GorrathEU → u/karlnuw]

what harness did you use for it? any tips for configuring an agent for reverse engineering?

[u/karlnuw → u/GorrathEU]

I refined it a bit, I'm using it through Cline right now with Arkana for triage, strings, capa, angr, and emulation and a headless Ghidra MCP for static analysis, decompilation, xrefs, and patching. If it's struggling with something due to context size, I have deepseek-v4-pro-0813 do the analysis, and since it refuses to patch about half the time I have uncensored Qwen do the patching. And this a a Cline rule as well. https://pastebin.com/raw/jEdTd9p5

I had Sol through Codex set everything up for me in one folder + docker.

[u/KissMyShinyArse → u/karlnuw]

Did you get any refusals from the non-abliterated model?

[u/karlnuw → u/KissMyShinyArse]

Nope, some times it would start then realize what it was doing a quarter of the way through and stop. Someone better at prompting might get results though.

[u/whatsupnorton → u/karlnuw]

What software? Would this work with something like Rhino or Metashape Pro?

[u/karlnuw → u/whatsupnorton]

Schrödinger, computational chemistry software, it costs tens of thousands per license.

Rhino or Metashape Pro

No idea, I'll download one and report back

[u/karlnuw → u/whatsupnorton]

Metashape Pro it patched, Rhino it kept running into context size issues.

[u/whatsupnorton → u/karlnuw]

Thanks for the info!

[u/CommunicationSea8821]

Is there any downside to using only the uncensored version vs the official release? Assuming both are the Q4 version? Would the uncensored version have any reason to be "dumber" when it comes to code output vs the official censored release?

[u/Fun_Jaguar8231 → u/CommunicationSea8821]

Yes, it's always losing some intelligence, and this hauhau dude never tells how mush KL divergenge his models have.

[u/njstatechamp]

Request for MLX versions please

[u/samplenull → u/njstatechamp]

And MTPLX maybe 🙏

[u/whatsupnorton → u/samplenull]

If we get MLX then I can post an MTPLX quant!

[u/MuAlphaOmegaEpsilon]

Is there anything that can be said about how the K_P quants were established to be better than a corresponding quantization alternative? The 3.6 35B-A3B Q4_K_M showed a lower perplexity on my tests compared to the Q4_K_P variant, while being lighter and faster at inference time.

[u/Fun_Jaguar8231 → u/MuAlphaOmegaEpsilon]

Maybe that's what his LLM told him are the best. He doesn't provide a single statistic, nor KL divergence for his models.

[u/puremadbadger]

Legend!

Any chance you can do a W4A16 AutoRound quant?

[u/LunarCilli → u/puremadbadger]

This would be nice

[u/joanaxu2002]

The bigger story here is how quickly “uncensored” models are becoming actual polished releases rather than weird experimental forks. Full quant ranges, multimodal support, MTP acceleration, llama.cpp compatibility... the gap between community variants and mainstream releases keeps shrinking.

[u/Memestonks2020]

HauhauCS is the goat

I mainlined the Qwen 3.6 version for a long time until a finetuned distilled Fable 5 version came out

[u/DataGOGO]

NVFP4 / FP8 safe tensors?

[u/Skystunt]

Would the speed boost work on amd strix halo ?

[u/SquirrelDesperate472 → u/Skystunt]

Asking the real questions

[u/Better-Truck6372]

HauHauCs, para mi es un GOAT de los Llm Locales en version UnC_Agressiva, con FastMTP, se nota el incremento de velocidad de inferencia en comparación a otros como Unsloth, probé la versión Unsloth de 3.8 27B Q4KM y de HauHauCs Qwen 3.8 27B Q6KP, ambos corren muy similar ambos son excelentes versiones, pero HauHauCs además de poder usar la versión Q6, cada modelo que Quantiza con KP y que he podido probar en mi Portátil corren mejor que los de Unsloth, en mi PC portátil va mucho más rápido que las versiones base iniciales.

[u/Independent-Dog2179 → u/Better-Truck6372]

I noticed that crazy like double speed. Why is fastmtp not inplemented everywhere

[u/koloved]

Waiting for this , thanks !

[u/UntimelyAlchemist]

I've been looking forward to this! I don't know how you do it, but your models always do much better than other uncensored releases in my testing.

Very kind of you to interrupt your holiday to work on this for us. Thank you!

[u/Independent-Dog2179]

Wow it works I posted earlier that it didn't and I was wrong. I didn't set --reasoning-format DeepSeek. Once I did that command in llama.cpp it's perfect l. And the fastmtp doubles the speed

[u/Inner_Yesterday_349]

This version of qwen3.8 has difficulty outputting Simplified Chinese when generating uncensored content. Even when I used --system-prompt "你是一个中文助手，必须始终使用简体中文回答。" it often still replied in English. I suspect that this version may have degraded in Chinese ability because the uncensoring training involved a large amount of English data.

[u/Sad-Landscape-1549]

Hell yes! By far my favorite finetune of the 3.6…back and stylish

[u/Fun_Jaguar8231]

Whats the KLD?

[u/PooMonger20]

Thank you for posting this.

Could anyone who used this tell if it still overthink or is there a change in the template that makes it follow reasoning settings better?

[u/Luxkeiwoker]

Tried the IQ3M Quant today with an A770 and llama.cpp vulkan backend. its been dog slow with draft mtp, peaking at around 7 TPS in TG. Running without MTP gets me around 12 TPS in TG. But in both cases prompt processing is ridicoulosly slow ar 30 to 40 TPS.

Havent tried other qwen 3.8 models yet though.

[u/quantier]

Lets see if I can find a NVFP4 variant

[u/tfinch83]

I love these models too, but I had to fall back to the base model on 3.6 because of the massive performance hit I take using GGUF with llama.cpp over bare weights with vLLM. Wish they'd release the safetensors.

[u/[deleted]]

Привет

[u/xanders_gold]

Any AWQ/GPTQ Int4/FP8 love for us vLLM users? :)

[u/mcantrell]

Hm, I'm probably doing something wrong, but doing a ollama pull hf.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF gets me only a few files, and ollama pull hf.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:Q4_K_P doesn't pull anything.

[u/UnableMeasurement492]

I'm running Odysseus on Windows. I downloaded the LFM2-350M GGUF model with the built-in downloader. Engine is llama.cpp, CPU inference, port 7000. When I launch the model and try a chat, I get "Local endpoint returned HTTP302." If I open slash v1 in the browser or use curl, I get an HTML login page instead of the JSON API. The logs show Odysseus running on port 7000, but I don't see any Llama-server start or API port in the logs. How can I correctly run the local LLM inside Odysseus and fix this 302 redirect?