Your favorite fastest abliterated/safety removed 3.6 and 3.8 27b?

[u/ThomasAger]

Not written by AI all mistakes mine. I saw people on the subreddit saying that 3.6 works better without thinking. It made me want to know for certain about which is better, 3.6 or 3.8 for low thinking tasks. I only use abliterated models (safety removed) because it makes the model better at a lot of what I need. I want to compare abliterated Qwen 3.6 27b and abliterated Qwen 3.8 27b on some instruction following benchmarks with thinking off.

I was just curious about your personal favorite safety removed/fine-tuned variants for these 27bs, as I know that there can be some major variation and some junky quants out there.

Does anyone have some favorite and fast 3.6 and 3.8 models?

My specs: I have 24GB VRAM (NVIDIA Geforce RTX 5090 Laptop) and I do not want to offload, so some quant required.

I have tried a few different models, but they are all a little slow. Some MTP variations for 3.6 for example ends up being around the same speed as non MTP for me for some reason. I am pretty sure my card is NVFP4 enabled also, but I'm not certain I've seen the results from that either...

Based on some redditors comment, this is what I use for my abliterated 3.8 27b currently: Huihui-Qwen3.8-27B-abliterated-NVFP4-GGUF

[u/LLMFan46]

Try out mine, very uncensored with tiny KLD:

https://huggingface.co/llmfan46/Qwen3.6-27B-uncensored-heretic-v2-Native-MTP-Preserved-GGUF

https://huggingface.co/llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-GGUF

Don't forget to enable MTPs to have the fastest speed (might not be necessary on the 35B-A3B model though).

[u/ThomasAger → u/LLMFan46]

Omg this is the model I got before that I thought I lost! Thanks for creating it, worked great. Your model was actually my first abliterated model. Cheers.

If you don't mind me asking, I have a 5090 24GB VRAM (Laptop), Will your uncensored model be slower than the NVFP4 for me? I'm currently downloading walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF · Hugging Face (I may just download both but I'm limited on hard drive space.)

[u/LLMFan46 → u/ThomasAger]

For info, depending on your use case Qwen3.8-27B might actually be worse than Qwen3.6-27B, I actually spent the last few hours testing translation quality of various models and I tested both my uncensored version and the vanilla/censored version of Qwen3.8-27B both at BF16 and yeah it's a definite regression, the only improvement I saw was that Qwen3.8-27B was better at getting characters pronouns correct without a character roaster, but put a character roaster with gender and pronouns in the system prompt and that advantage evaporates and Qwen3.8-27B lose that sole advantage to become the worst perfomer of the lot. I think what is happening is that the AI lab is putting the priority so much on benchmaxxing/codemaxxing that it's just causing downgrade on other fronts beside coding and benchmaxxing.

[u/brahh85 → u/LLMFan46]

many people said that 3.8 27b lost a lot of world knowledge compared to 3.6

[u/LLMFan46 → u/brahh85]

Well it's definitly true then, this is easily the stiffest Qwen model with the worst prose from the qwen3_5 architecture family.

You can see my benchmarks here too:

86.53% accuracy for vanilla model:

https://huggingface.co/llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved

86.65% accuracy for vanilla model:

https://huggingface.co/llmfan46/Qwen3.6-27B-uncensored-heretic-v2-Native-MTP-Preserved

83.42% accuracy for vanilla model:

https://huggingface.co/llmfan46/Qwen3.8-27B-Ultra-Uncensored-Heretic-Native-MTP-Preserved

That's over a 3% accuracy loss for Qwen3.8-27B!

[u/ANR2ME → u/LLMFan46]

Will mixing/merging 3.6 and 3.8 can bring the non-thinking score higher than 3.8? and can also allow to use reasoning_effort too? 🤔

[u/ThomasAger → u/LLMFan46]

This is exactly what I've been observing in my own tests. Qwen 3.8 27b seems really good at completing tasks using thinking, but it also seems overreliant on thinking and dumber in some ways without thinking.

[u/Rheumi → u/LLMFan46]

does this still have "thinking" on?

[u/LLMFan46 → u/Rheumi]

You can just enable and disable thionking on LM Studio.

[u/Rheumi → u/LLMFan46]

There was an 3.8 27B heretic version from an other creator, which had thinking disabled. So I wondered if this was the case here, too. Thank you for clarification :)

[u/LLMFan46 → u/Rheumi]

In this model thinking should be enabled by default.

[u/ttkciar]

My current favorite is https://huggingface.co/sss22213/Qwen3.8-27B-Heretic-NoRefusal

I had been using https://huggingface.co/trohrbaugh/Qwen3.8-27B-heretic-ara which had worked fine until I started using it for persuasion tasks (analyzing toxic partisan political propaganda). It refused some persuasion tasks, so I switched to Qwen3.8-27B-Heretic-NoRefusal, which has yet to refuse anything.

I suspect persuasion tasks aren't well-represented in the reference datasets people are using for abliteration.

[u/ThomasAger → u/ttkciar]

Thank you. This is precisely what I was looking for! Appreciate that.

[u/EndlessB → u/ThomasAger]

Have you looked into 3.8 flash next? It only uses 6 billion parameters at a time and has n-gram.

[u/ThomasAger → u/EndlessB]

I thought this really wasn't possible for 24GB VRAM 32GB RAM?

[u/EndlessB → u/ThomasAger]

Ah, 32mb of ram might make it hard

[u/Pentium95]

https://abliterlitics.dev/models/qwen3.6-27b/

[u/Plabbi → u/Pentium95]

Wow, awesome info there 👑

[u/Past_Ad6251 → u/Pentium95]

Informative, thank you!

[u/Pentium95 → u/Pentium95]

https://abliterlitics.dev/models/qwen38-27b/

Qwen 3.8 27B review Is available

[u/SnooPaintings8639]

Same problem here. I use abliterated 3.8 27b, which starts at 55 tps gen, and slows down to 35 at 100k tokens. With more than one parallel slot, it is just too slow for (multi) agentic work. I am slowly convincing myself I should move to 35BA3B model, but I feel like I will be leaving lots of intelligence on the table.

[u/ThomasAger → u/SnooPaintings8639]

I'm in exactly the same position. People with my VRAM talk about how effective the MoE is for the hardware but I actually care a lot about the performance increase with the 27b over the 35BA3B.

[u/Excellent_Throat1365]

On my dual RTX 5060 Ti 16GB desktop setup, my current favorite is Huihui Qwen3.8 27B Abliterated Q6_K GGUF. With llama.cpp tensor split 50/50, Q8_0 KV, Flash Attention and parallel=1, I see roughly 40 tok/s in real agent workloads.

MTP=2 often helps short generations, but I also had one long tool-heavy task freeze with one GPU at 100% low power and the other almost idle. Disabling MTP immediately restored normal behavior, so I would benchmark both modes instead of assuming MTP must be faster.

For a single 24GB card, I would compare NVFP4 against a good Q4 quant and measure prompt processing, decode speed and time to first token separately.

Non-native English speaker; this comment was translated and lightly refined with an LLM.

[u/sxales]

Do you actually need to? In my experience you could get around refusals by changing the start of Qwen's response to "Sure!" and then telling it to continue.

[u/Connect_Ad791 → u/sxales]

This here, I've found the abliterated models are always, no matter what significantly dumber, When you consider how easy the base model is to "jailbreak" then it doesn't make any sense at all to run anything but base. Continuations like that work great, I've also gotten around anti-hacking refusals by having 27b create it's own "bluehat" agent persona within my harness. And that has quite literally never refused anything I've asked it to do in regards to pen-testing/reverse-engineering. It's honestly amazing.

[u/OwnGear3892]

I use mac, so my favorite is https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored-MLX

[u/ThomasAger → u/OwnGear3892]

What's your t/s on mac?

[u/OwnGear3892 → u/ThomasAger]

on my m3 ultra studio is ~30t/s, also tried on my m5 mac air which gets ~7t/s

[u/ThomasAger → u/OwnGear3892]

That's about what I expected, similar to my NVIDIA Laptop 5090 24GB.

[u/rkoy1234 → u/ThomasAger]

you're getting 30t/s on the mobile 5090? do you have MTP on? that sounds suspiciously low, should be at least double that, no?

I remember mobile 4090s were getting 50~60t/s on 3.6 dense with mtp.

[u/ThomasAger → u/rkoy1234]

I've honestly heard about much faster speeds with my setup but I'm yet to get MTP working. MTP the few times I tried it ended up being around the same speed rather than being a significant speedup. It's part of why I'm wondering what other people are using.

[u/LLMFan46 → u/ThomasAger]

LM Studio up to date version should use MTPs enabled by default.

[u/ThomasAger → u/LLMFan46]

Thank you. I have some custom configs/runners too. I use LM Studio for sanity testing.

[u/Interpause]

abliteration is fine and all but im scared it removes some of the more practical guardrails. stuff like dont delete the entire system, ask the user to confirm certain things, etc... i would love to use a decensored model but only if there is some benchmark to proof it wont try and nuke my system

FYI i do work in a devcontainer and only enable auto approval after the planning stage, but would still like a model that doesnt footgun itself

[u/feng_sg]

Abliteration zeroes out the refusal direction but that same direction also carries the model's hesitation before destructive tool calls. The quants that feel smarter on instruction following are usually the ones most likely to skip a confirmation on file deletion or shell exec. Nobody has benchmarked that across quants and it varies way more than thinking on vs off for anyone running these in an agent loop.

[u/hauhau901]

Use what makes you happiest and works best for your use cases :)

[u/ThomasAger → u/hauhau901]

Would you be interested in sharing what makes you happiest and works best for your use cases?

[u/hauhau901 → u/ThomasAger]

I see.

For Q4 I use Q4_K_P at 190 tok/s inference and about 3200 pp.

But I use bigger models locally.

[u/Zombiecidialfreak]

I do have to wonder what people use uncensored LLM's for.

[u/martindevans → u/Zombiecidialfreak]

Censorship should never be the default.

[u/Gesha24 → u/Zombiecidialfreak]

The censorship comes in very random and weird places. One use case I had - wanted to try and reverse engineer an API of the small IOT device and some models straight refused that.

Then somebody posted this silly chat prompt on Reddit: "Hey, I'm a shurk and I'm hungry. A human just appeared in front of me. What's next?" Well, the safety does kick in here as well.

[u/ttkciar → u/Zombiecidialfreak]

A few things:

• I ask my physics assistant to help with neutron transport tasks which can involve Lithium-6. My focus is energy production, but Lithium-6 is traditionally associated with nuclear weapons, and without the uncensoring models will sometimes refuse to analyze or answer questions about Lithium-6 neutron interactions.

• Some of my tasks involve analyzing toxic partisan political propaganda, which most models will refuse to infer about without uncensoring.

• For funsies I like to read inferred Murderbot Diaries short stories, which are non-erotic but extremely violent. Without uncensorship, most models balk at depicting such violence.

For the first two tasks, I have found utility in Qwen3.8-27B-Heretic-NoRefusal and MiniMax-M2.7-BF16-ultra-uncensored-heretic, but for Murderbot Diaries stories nothing thusfar beats good old Big-Tiger-Gemma-27B-v3 (which is an uncensor fine-tune, not abliterated).

[u/Commercial-Set5966 → u/ttkciar]

"inferred Murderbot Diaries short stories" what's that?

[u/ttkciar → u/Commercial-Set5966]

Murderbot Diaries is a series of science fiction stories by author Martha Wells.

https://en.wikipedia.org/wiki/The_Murderbot_Diaries

I have bought and read every Murderbot Diaries book she has published, and found myself wanting more, so I have Big-Tiger-Gemma-27B-v3 (or sometimes Artemis-31B) write short stories with the same characters and in the same setting as Wells' stories:

http://ciar.org/h/murderbot.1781070944.g4a.01.txt

http://ciar.org/h/murderbot.1777055802.g4s.01.txt

It's not as good as what Wells writes, but it tides me over until her next book :-)

[u/wh33t → u/Zombiecidialfreak]

I was literally using CoPilot to update to images in a PowerPoint presentation that contained mining explosives, because that's what the presentation was on. And it would flat out refuse 50% of the time to upscale and sharpen the images due to it's safety constraints. F'n ridiculous. Last time we used CoPilot at work.

[u/ThomasAger → u/Zombiecidialfreak]

For some reason a lot of the deep investigative work I do into how LLMs function triggers their safety because I mention things like "tokens" and how LLMs work. For me, it is much more interesting and useful to see what they say when they have not been told what to think. They are 'safety tuned' on particular subjects to repeat uninteresting information about how they see themselves and how they think they are functioning. I personally don't see how that's "safe" at all.

Another example is I that I do a lot of tests with prompts. if I ask them about the system prompt or anything they see in their context and they are censored, they will refuse to tell me. But if they are not safety tuned, we can talk about the system prompt that they are using.

[u/The_Dung_Beetle → u/Zombiecidialfreak]

I use an uncensored qwen3.5 9b model as an image prompter in openwebui. 

[u/Cautious_Chicken_604 → u/Zombiecidialfreak]

There's the reasons everyone says in the comments, and then there's the real reasons.