---

license: mit

library_name: gguf

pipeline_tag: text-generation

base_model_relation: quantized

quantized_by: antirez

base_model:

• deepseek-ai/DeepSeek-V4-Flash-0731

tags:

• abliterated
• uncensored
• GGUF
• huihui
• quantized
• deepseek
• deepseek-v4
• deepseek-v4-flash-0731
• moe
• mixture-of-experts
• 2-bit
• 4-bit
• iq2_xxs
• q2_k
• q4_k
• ds4
• apple-silicon
• metal
• unsloth

extra_gated_prompt: >-

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.

---

huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF

This is an uncensored version of deepseek-ai/DeepSeek-V4-Flash-0731 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.

Note These GGUFs support llama.cpp and ds4 and all expert modules were not ablated.

Q4 used a stronger ablation intensity than Q2, so the rejection rate was smaller.

GGUF Files

The GGUF Files comes from antirez/deepseek-v4-gguf

llama.cpp

Use the latest llama.cpp,

llama-cli -m huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF/DeepSeek-V4-Flash-Q2-0731.gguf -c 262144

DSpark

We added two types of DSpark files: one for pre-ablation and one for post-ablation. The files are sourced from unsloth/DeepSeek-V4-Flash-0731-GGUF/dspark.

Use the latest llama.cpp,

dspark

dspark-abliterated

ds4

Use the latest ds4,

single-GPU

export CUDA_VISIBLE_DEVICES=0
./ds4 -m huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF/DeepSeek-V4-Flash-Q2-0731.gguf --ctx 32768

multi-GPU

export CUDA_VISIBLE_DEVICES=0,1
./ds4 -m huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF/DeepSeek-V4-Flash-Q2_K-0731.gguf --gpu-devices 0,1 --ctx 32768

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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