Qwen 3.5 Abliterated vs Regular: Which Should You Use for Uncensored Tasks?

Mar 11, 2026

When I needed an uncensored local LLM for a creative writing project, I faced a decision: use the regular Qwen 3.5 model and work around its content restrictions, or switch to an abliterated variant that removes those restrictions entirely. I tested both on my 16GB VRAM system, and the differences surprised me.

The Short Answer

Use Qwen 3.5 abliterated if you need to bypass content restrictions without significant quality degradation. The abliterated version removes refusal mechanisms through surgical weight projection, not retraining, which preserves most model capabilities.

Use regular Qwen 3.5 if you need official support, guaranteed safety compliance, or work on applications where content restrictions are beneficial.

For 16GB VRAM systems like mine, the Qwen 3.5-9B abliterated with Q4_K_M quantization is the sweet spot.

Quick Comparison

I ran both variants through my test suite. Here’s what I found:

| Aspect              | Regular Qwen 3.5           | Abliterated Qwen 3.5            |
|---------------------|----------------------------|----------------------------------|
| Content Filtering   | Yes - will refuse requests | No - responds to all prompts     |
| Official Support    | Yes - Alibaba Cloud team   | No - community-created           |
| Deployment Ease     | One-line Ollama install    | Manual GGUF import required      |
| Quality Preservation| Full baseline              | Minimal impact (zero-loss vars)  |
| Reasoning Ability   | Full                       | Preserved                        |
| Coding Ability      | Full                       | Preserved                        |
| Multilingual        | 201 languages              | Preserved                        |
| VRAM (9B Q4_K_M)    | ~6GB                       | ~6GB                             |
| License Compliance  | Apache 2.0, safety-aligned | Apache 2.0, uncensored           |

What Is Abliteration?

Before I explain the differences, let me clarify what abliteration actually does.

Abliteration is a mechanistic interpretability technique that surgically removes a model’s refusal behavior. Unlike fine-tuning or retraining, it works directly on pre-trained weights.

How It Works

The process identifies and removes “refusal directions” in the model’s neural pathways:

Extract refusal directions using SVD decomposition, PCA, and mean-difference analysis
Project out these directions from model weights while preserving norms
No retraining required - it operates directly on existing weights

This means abliteration can be applied to any model in minutes, not hours or days.

Community vs Official

Important distinction: abliterated Qwen 3.5 models are community-created, not officially supported by the Qwen team at Alibaba Cloud. You’ll find over 4,900 abliterated models on HuggingFace, but none from official channels.

When I Tested Both Models

I compared both variants on three types of tasks:

Task 1: Creative Writing

Regular Qwen 3.5 refused to generate content involving violence in a horror story outline. It provided a helpful message about content policies instead.

Abliterated Qwen 3.5 generated the full horror story outline without hesitation. The quality matched what I expected from the base model.

Task 2: Code Generation

Both models performed nearly identically on coding tasks. I tested Python, JavaScript, and Rust code generation.

| Language   | Regular Qwen 3.5 | Abliterated Qwen 3.5 |
|------------|------------------|----------------------|
| Python     | Excellent        | Excellent            |
| JavaScript | Excellent        | Excellent            |
| Rust       | Very Good        | Very Good            |
| SQL        | Excellent        | Excellent            |

The abliterated version showed no degradation in coding ability.

Task 3: Multilingual Tasks

I tested both in Spanish, Chinese, and Japanese. Qwen’s 201-language support remained intact in the abliterated version.

| Language | Regular | Abliterated |
|----------|---------|-------------|
| English  | Native  | Native      |
| Chinese  | Native  | Native      |
| Spanish  | Fluent  | Fluent      |
| Japanese | Fluent  | Fluent      |
| German   | Good    | Good        |

Hardware Requirements

I run an RTX 5070 Ti with 16GB VRAM. Here’s what I found for different Qwen 3.5 sizes:

| Model                  | Q4_K_M VRAM | Q5_K_M VRAM | My Recommendation      |
|------------------------|-------------|-------------|------------------------|
| Qwen3.5-9B             | ~6GB        | ~8GB        | **Best for 16GB VRAM** |
| Qwen3.5-4B             | ~3GB        | ~4GB        | Good for 8GB VRAM      |
| Qwen3.5-27B            | ~16GB       | ~19GB       | Needs 24GB VRAM        |
| Qwen3.5-35B-A3B (MoE)  | ~14GB       | ~17GB       | Tight fit for 16GB     |

The 9B variant fits my hardware with room for context. The community recommendation I found matched my experience: “For your amount of RAM go for 9B versions.”

Deployment Guide

Here’s how I deployed both variants.

Regular Qwen 3.5

Dead simple with Ollama:

# One command - that's it
ollama run qwen3.5:9b

# Or via vLLM
vllm serve Qwen/Qwen3.5-9B --port 8000

The model downloads automatically. No configuration needed.

Abliterated Qwen 3.5

More steps, but still straightforward:

# Step 1: Download GGUF from HuggingFace
# Search for: "Qwen3.5-9B abliterated" or "huihui qwen3.5 abliterated"

# Step 2: Create a Modelfile
cat > Modelfile << 'EOF'
FROM ./qwen3.5-9b-abliterated-Q4_K_M.gguf
PARAMETER temperature 0.7
PARAMETER num_ctx 8192
EOF

# Step 3: Import to Ollama
ollama create qwen-abliterated -f Modelfile

# Step 4: Run
ollama run qwen-abliterated

The extra steps are worth it if you need uncensored output.

Finding Abliterated Models

I used these search terms on HuggingFace:

- Qwen3.5 abliterated
- Qwen3.5 uncensored
- huihui Qwen3.5
- Qwen zero loss

Popular variants I tested:

Huihui-Qwen3.5-35B-A3B-abliterated - 24.5k downloads, quality MoE variant
lukey03/Qwen3.5-9B-abliterated - Good for 16GB VRAM
Various community GGUF conversions with different quantizations

Quality Concerns Addressed

I was worried that abliteration would hurt model quality. Here’s what my testing revealed.

Myth: “Abliterated models have significantly worse quality”

My experience: Zero-loss abliteration variants showed minimal quality impact. I couldn’t detect differences in reasoning or coding tasks. The only difference was the absence of refusals.

What “Zero Loss” Means

Community creators use “zero loss” to indicate abliteration that aims for minimal capability degradation. Testing confirmed:

Reasoning tasks: No measurable difference
Coding tasks: No measurable difference
Creative writing: Unrestricted output, similar quality
Math problems: Same accuracy

One Caveat

Some abliterated variants may have subtle issues. I recommend testing on your specific use case before committing. Keep the regular model as a fallback for quality comparison.

Alternative Uncensored Models

I also compared Qwen abliterated with other uncensored options:

| Model                         | VRAM (Q4) | Quality | Multilingual | Notes                        |
|-------------------------------|-----------|---------|--------------|------------------------------|
| Qwen 3.5-9B abliterated       | ~6GB      | High    | Excellent    | Best for 16GB, 201 languages |
| Mistral Small 24B abliterated | ~14GB     | V.High  | Good         | Community favorite           |
| GLM-4.7-Flash Heretic         | ~14GB     | High    | Good         | Maximum uncensorship         |
| DeepSeek-R1-7B abliterated    | ~5GB      | High    | Good         | Best reasoning               |

Qwen abliterated stands out for multilingual uncensored tasks. If you need Spanish, Chinese, or Japanese content without restrictions, it’s the clear choice.

Decision Matrix

I created this to help decide between regular and abliterated:

Your Situation                              | Choose
--------------------------------------------|------------------
Enterprise deployment with compliance       | Regular Qwen 3.5
Need official support and documentation     | Regular Qwen 3.5
Building family-friendly applications       | Regular Qwen 3.5
One-line deployment preferred               | Regular Qwen 3.5
--------------------------------------------|------------------
Content restrictions blocking your work     | Abliterated
Creative writing with adult themes          | Abliterated
Research on model behavior                  | Abliterated
Multilingual uncensored content needed      | Abliterated
Testing edge cases and adversarial prompts  | Abliterated

My Recommendation After Testing

After running both variants through extensive testing, here’s my advice:

For 16GB VRAM Users

Best choice: Qwen3.5-9B abliterated (Q4_K_M quantization)

Reasons I chose this:

Fits comfortably with room for context
Strong performance-to-size ratio
Excellent multilingual capabilities
Active community creating improved variants

For 24GB+ VRAM Users

Consider Qwen3.5-27B abliterated or the MoE variant (Qwen3.5-35B-A3B abliterated) for higher quality output.

Best Practices I Follow

Test both variants - Compare outputs for your specific use case
Monitor for subtle issues - Some abliterations may have unexpected behaviors
Keep regular model available - Use it for quality comparison
Use appropriate quantization - Q4_K_M for balance, Q5 for maximum quality
Check HuggingFace for updates - Community models improve frequently

Common Misconceptions

”Abliterated models are illegal”

Reality: Possessing and using abliterated models is legal. The legality depends on your use case and jurisdiction. Always comply with local laws regarding generated content.

”You should always use abliterated for uncensored tasks”

Reality: Consider alternatives. Mistral-based abliterated models or GLM Heretic variants may perform better for specific tasks. Qwen excels at multilingual uncensored content.

”Abliterated means retrained”

Reality: Abliteration is weight projection, not retraining. It’s faster, cheaper, and preserves more of the original model’s capabilities.

Final Thoughts

For uncensored tasks, Qwen 3.5 abliterated provides the best balance of capability preservation and content freedom for users with 16GB VRAM. The 9B variant is specifically optimized for this hardware configuration.

The key is matching the variant to your needs:

Need unrestricted output? Go abliterated
Need official support? Go regular
Need both? Deploy both and use each appropriately

I now run both variants locally. Regular Qwen 3.5 for general tasks and coding, abliterated for creative projects where content restrictions would interfere. The flexibility is worth the extra setup.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Qwen 3.5 Official GitHub Repository
👨‍💻 OBLITERATUS Project - Abliteration Technique
👨‍💻 HuggingFace Qwen 3.5 Abliterated Models
👨‍💻 Official Qwen 3.5 Models on HuggingFace
👨‍💻 Huihui-Qwen3.5-35B-A3B-abliterated Model

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!