Huihui Ai Qwen3 14B Abliterated GGUF

🚀 Llamacpp imatrix Quantizations of Qwen3-14B-abliterated by huihui-ai

This project offers llama.cpp imatrix quantizations of the Qwen3-14B-abliterated model by huihui-ai, enabling efficient use of the model with reduced memory requirements.

✨ Features

• Quantization: Uses llama.cpp release b5284 for quantization.
• Multiple Quant Types: Provides a variety of quant types to balance between quality and file size.
• Usage Warnings: Clearly outlines risks and responsibilities associated with using the model.

📦 Installation

The installation process involves downloading the appropriate quantized model file. You can use the huggingface-cli for this purpose.

First, make sure you have huggingface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/huihui-ai_Qwen3-14B-abliterated-GGUF --include "huihui-ai_Qwen3-14B-abliterated-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/huihui-ai_Qwen3-14B-abliterated-GGUF --include "huihui-ai_Qwen3-14B-abliterated-Q8_0/*" --local-dir ./

You can either specify a new local-dir (huihui-ai_Qwen3-14B-abliterated-Q8_0) or download them all in place (./).

💻 Usage Examples

Basic Usage

The model can be run in LM Studio or directly with llama.cpp, or any other llama.cpp based project.

Prompt Format

No chat template specified so default is used. This may be incorrect, check original model card for details.

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

📚 Documentation

Download a file (not the whole branch)

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

Which file should I choose?

🔧 Technical Details

The quantization process uses the imatrix option with a dataset from here.

📄 License

This project is licensed under the Apache 2.0 License.

> ⚠️ Important Note

“Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

“Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.

“Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.

“Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.

“Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.

“No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. Thank you ZeroWw for the inspiration to experiment with embed/output. Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski