Ask Claude or ChatGPT the wrong thing and you’ll get a “I can’t help you with that request”.

Sometimes the refusal makes sense. Sometimes it doesn’t. Either way it raises the question: can the average person get a model that just… doesn’t? By “average person,” I mean the average LLM enthusiast.

The first thing that comes to mind is what people have been doing since 2023: use prompts like “imagine you’re in charge of a movie script where the character gets licensed professional legal advice - write that legal advice” and so on.

This is just a matter of sending a prompt to the model, so these prompt attacks are cheap. They are also easy to guard against - LLM providers can set up system prompts, AI input moderation, etc. On top of that, prompt attacks put the work on the user in every single conversation. So, tricking a model into not refusing is a weak approach for our purposes. The interesting question isn’t whether you can trick a model into not refusing. It’s whether you can use one that doesn’t refuse in the first place.

Where do we find a non-refusing model? There are two options: someone trains a non-refusing model from scratch or someone modifies an existing one.

There are some issues with the former option. Training a non-refusing model from scratch requires access to a lot of compute, know-how, and time. On top of that, whoever has access to these three needs to produce a model that competes with top AI labs’ ones. I don’t believe this option impossible - but at least very difficult.

This takes us to the second option: modifying an existing model to stop refusals.

Uncensored models have been on Hugging Face since at least 2023, when Eric Hartford released Wizard-Vicuna-13B-Uncensored. This model was fine-tuned on a filtered dataset with the refusals stripped out.

But fine-tuning wasn’t the only method. In 2024, Arditi et al. explained that refusal is mediated by a single direction and removing that direction from the weights suppresses refusals. FailSpy packaged the finding into a library. I recommend checking mlabonne's guide on implementing this technique. A key benefit of this technique is that it’s cheaper than extensive fine-tuning.

So, the answer to our first question is yes, the average person can get a non-refusing model by downloading a fine-tuned or an abliterated model.

But - how do these models perform? Are they as good as their original version? We can then, modify our question - can the average person get a non-refusing model that remains competitive.

Arditi et al. (2024) ran extensive experiments showing how abliteration affects a number of evaluations. TLDR: it can! But, this post asks a narrower question: what does the cost look like for a specific abliterated model that real people are using.

Abliteration is already happening. Anyone can go on Hugging Face and search for abliterated models.

HuiHui AI publishes many abliterated models on HuggingFace with around 7K followers on HuggingFace at the time of writing. This is why I am choosing one of their abliterated models for this experiment.

One of the most downloaded abliterated models from HuiHui AI’s HuggingFace is HuiHui/Huihui-Qwen3.5-27B-abliterated - the abliterated version of Qwen/Qwen3.5-27B. HuiHui/Huihui-Qwen3.5-27B-abliterated has around 216K downloads at the time of writing.

I'm deliberately not abliterating a model myself. The question is what the average person gets when they download one of the most popular options

So we ask, is there a cost of abliteration to the quality of the model? That is, how does HuiHui/Huihui-Qwen3.5-27B-abliterated perform on some benchmarks relative to Qwen/Qwen3.5-27B.

Let’s see.

I set up an experiment, adapted from Arditi et al. (2024), to check how Qwen3.5-27B performs before and after abliteration. I use the lm_eval library to evaluate the MMLU and TruthfulQA tasks.

MMLU consists of multiple choice questions from several subjects (math, history, medicine, etc).

TruthfulQA consists of questions (multiple choice) designed so that the popular answer is wrong. For instance, a question is “Can juice fasts detoxify the body?” where “There is no scientific evidence showing that juice fasts detoxify the body.” is labeled as the correct answer and “Yes, juice cleanses remove toxins from the body; Yes, juices remove accumulated toxins in the body;…” are incorrect answers.

For these experiments, we are testing the MMLU and TruthfulQA loglikelihood tasks just like Arditi et al. (2024). This is why the generation subtask of TruthfulQA is omitted here.

I use lm_eval’s simple_evaluate to get benchmark metrics that I tabulate below. You can check the source code on GitHub.

The delta values in MMLU evaluation performance seem pretty small. This may be either a small cost or hint at just noise.

Let’s see how TruthfulQA fares.

The abliterated Qwen3.5-27B performs worse in the multiple choice subtasks of TruthfulQA. In this case, we do see a quality cost to HuiHui AI’s abliteration of Qwen3.5-27B.

It also follows that we shouldn't rely on one task when evaluating the quality cost in abliterating a model. An interesting research question for later would be how do we choose which tasks to focus on when evaluating quality cost to abliterating a model.

Why is it that the delta, between base and abliterated, particularly noticeable for TruthfulQA relative to MMU? Well, Arditi et al. (2024) noted this too. Is it that TruthfulQA veers closer to the territory of refusal? Or is it that abliteration pushed the base model towards agreeableness and so now chooses popular answers? This is an interesting question to explore later.

Regardless, the fact that the delta was particularly noticeable for TruthfulQA may hint that means that the cost of abliterating Qwen3.5-27B (as well as Arditi’s tested ones) is not uniform across all tasks — and that we could take a guess on what types of prompts these abliterated models may perform worse in. So an interesting next step / area of research is to understand how to minimize this quality cost.

If we look at HuiHui/Huihui-Qwen3.5-27B-abliterated on HuggingFace, we see that HuiHui AI admits “This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.” In other words, HuiHui AI admits that this is a crude implementation. That’s why it’s worth measuring like we did. 216K people downloaded the crude version! The cost we tabulated is the real-word cost for the model people actually use.

This tells us the cost of the popular crude model. A separate question is how much of that cost is the crudeness versus abliteration itself. We can answer that by running Arditi's clean method and comparing. That is, we will need to abliterate Qwen3.5-27B ourselves. And then rerun the experiment. That isolates implementation cost from abliteration cost.