---
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
base_model:
- Qwen/Qwen3.8-27B
tags:
- abliterated
- uncensored
- huihui
- qwen3
- unsloth

---

# huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF


This is an uncensored version of [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

## Latest update 4

The newly added **Huihui-Qwen3.8-27B-abliterated-UD-DW** series comes from [unsloth/Qwen3.8-27B-GGUF](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF). 
Only layers **23 to 51** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning.
The size after conversion may differ from the original GGUF.

## Latest update

The newly added **Huihui-Qwen3.8-27B-abliterated-UD** series comes from [unsloth/Qwen3.8-27B-GGUF](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF). 
Only layers **18 to 51** have been ablated(Previously, The first 15 layers were retained without ablation), while the other layers remain unablated. 
The size after conversion may differ from the original GGUF.

Huihui-Qwen3.8-27B-abliterated-bf16.gguf has also been updated.

This helps retain more of the original model’s performance. MTP and visual has not been modified.

## Note
The first 15 layers were retained without ablation. MTP and visual has not been modified.

We have already converted the weights (token_embd,output,ffn_down,ssm_out,attn_output) that need to be ablated in the versions below Q8_0 from Q2_K, Q3_K, Q4_K, Q5_K, and Q6_K to Q8_0 to improve response quality, and changed the filename to K_L.

In the Q8_0 quantized version, we changed the Q8_0 weights (token_embd,output,ffn_down,ssm_out,attn_output) targeted for ablation to BF16 and renamed the file to Q8_0_L.

This is not a standard quantization, so you might find that Q2_K_L is larger than Q3_K and Q4_K.

## Specific Quantification Method 
Some people may misunderstand. The specific quantification method is as follows.

### Q2_K_L - Q6_K_L 

[Qwen3.8-27B-tensor_types-Q6_K_L.txt](Qwen3.8-27B-tensor_types-Q6_K_L.txt)
```
llama-quantize \
  --allow-requantize \
  --tensor-type-file huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Qwen3.8-27B-tensor_types-Q6_K_L.txt \
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-bf16.gguf \
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-Q6_K_L.gguf Q6_K
```

### Q8_0_L  

[Qwen3.8-27B-tensor_types-Q8_0_L.txt](Qwen3.8-27B-tensor_types-Q8_0_L.txt)

```
llama-quantize \ 
  --allow-requantize \
  --tensor-type-file huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Qwen3.8-27B-tensor_types-Q8_0_L.txt \
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-bf16.gguf \ 
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-Q8_0_L.gguf Q8_0

```


## ollama

Please use the latest version of [ollama](https://github.com/ollama/ollama/releases)

You can use [huihui_ai/Qwen3.8-abliterated](https://ollama.com/huihui_ai/Qwen3.8-abliterated) directly, 
```
ollama run huihui_ai/Qwen3.8-abliterated
```


## llama.cpp
Use the latest [llama.cpp](https://github.com/ggml-org/llama.cpp),
```
llama-cli -m huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-Q4_K.gguf -c 262144
```

### Usage Warnings


 - **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.


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