---

library_name: transformers

license: other

license_name: qwen-community-1.0

license_link: LICENSE

pipeline_tag: image-text-to-text

base_model:

• Qwen/Qwen3.8-Flash-Next

tags:

• abliterated
• uncensored
• huihui
• qwen3
• unsloth
• llama.cpp
• qwen3_8
• moe
• gsq
• rco
• reasoning
• efficient-thinking
• token-efficient
• post-training
• Strata

---

huihui-ai/Huihui-Qwen3.8-Flash-Next-abliterated-v2-GGUF

This is an uncensored version of Qwen/Qwen3.8-Flash-Next 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.

Last Update

We used calibration3.txt to generate imatrix_micro3.gguf, and then based on that produced

unsloth-UD-Q4_K_XL-V2,

with the PPL value decreasing slightly.

The llama.cpp version used for testing is llama.cpp-b11540.

Note

This version Compared to the first version, the ablation emphasis has been adjusted, and none of the experts have been ablated. Suitable for novel writing and programming.

Strata

The newly added Swift series come from ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF.

The unsloth series come from unsloth/Qwen3.8-Flash-Next-GGUF.

This unsloth and Swift series supports the latest versions of both Strata and llama.cpp.

Strata has byte-level and SHA256 verification for the Unsloth series.

The simplest method is to modify the verify_sha256 function in the setup.py file so that it returns immediately, then no verification is needed.

def verify_sha256(s: Path, size: int, sha: str) -> None:
    return

This is just a test/validation.

llama.cpp

GGUFs come from unsloth/Qwen3.8-Flash-Next-GGUF

Use the latest llama.cpp,

llama-cli -m huihui-ai/Huihui-Qwen3.8-Flash-Next-abliterated-v2-GGUF/UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.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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