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
• ternary
• 2-bit
• llama-cpp
• cuda
• metal
• on-device
• hybrid-attention
• prismml
• bonsai

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

This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see 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 6

The newly added Huihui-Qwen3.8-27B-abliterated-Ternary series come from prism-ml/Ternary-Bonsai-2-27B-gguf.
Only layers 22 to 52 (0-based indexing) 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 (Some of the weights are converted from PTQ1 to Q2_K or Q3_K.).
This is just a test/validation.

The ternary hybrid-attention kernels live in the
PrismML-Eng/llama.cpp fork. Stock llama.cpp will not run these files.

Latest update 5

The newly added Huihui-Qwen3.8-27B-abliterated-GSQ-RCO series come from ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF.
Only layers 22 to 52 (0-based indexing) 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 4

The newly added Huihui-Qwen3.8-27B-abliterated-UD-DW series come from unsloth/Qwen3.8-27B-GGUF.
Only layers 22 to 52 (0-based indexing) 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 come from unsloth/Qwen3.8-27B-GGUF.
Only layers 17 to 52 (0-based indexing) 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

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

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

You can use huihui_ai/Qwen3.8-abliterated directly,

ollama run huihui_ai/Qwen3.8-abliterated

llama.cpp

Use the latest 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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