# Huihui Qwen3.8 27B Abliterated GGUF

URL: https://interfaze.ai/models/huihui-aihuihui-qwen38-27b-abliterated-gguf

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Huihui Qwen3.8 27B Abliterated GGUF by huihui-ai, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Huihui Qwen3.8 27B Abliterated GGUF | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 262.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Huihui Qwen3.8 27B Abliterated GGUF | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/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.

## 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](https://huggingface.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/blob/main/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-Q6\_K\_L.txt](https://huggingface.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/blob/main/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-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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