library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled/blob/main/LICENSE
pipeline_tag: image-text-to-text
base_model:
- Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
tags: - abliterated
- uncensored
- Claude
- reasoning
- chain-of-thought
- Dense
huihui-ai/Huihui-Qwen3.5-27B-Claude-4.6-Opus-abliterated
Overview
Huihui-Qwen3.5-27B-Claude-4.6-Opus-abliterated-exl3-6bpw is an EXL3-quantized checkpoint for ExLlamaV3-compatible runtimes, published by groxaxo.
It is intended for open-source evaluation, reproducible experimentation, and compatible local or
hosted inference workflows. The wording below is deliberately limited to what can be verified
from this repository's metadata and artifacts.
The repository name identifies a behavior-modified or reduced-filtering lineage. That label describes the source or conversion history; it is not a guarantee of unrestricted behavior in every prompt or runtime. Test outputs carefully before sharing or deploying them.
At a glance
| Field | Details |
|---|---|
| Format | EXL3 |
| Source / base | Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled |
| Intended task | image-text-to-text |
| License | apache-2.0 |
What is included
*.safetensors(3 files)config.jsontokenizer.jsontokenizer_config.jsonprocessor_config.jsonchat_template.jinjaquantization_config.json- Additional configuration, tokenizer, processor, or shard files (11 visible artifacts total)
Quick start
EXL3-compatible runtimes
Download the EXL3 files and load the desired bitrate with a current ExLlamaV3-compatible
runtime. The correct loader and context settings depend on the model architecture and should be
verified against the runtime's documentation.
Compatibility and responsible use
- Use a runtime that explicitly supports this format, architecture, and modality.
- Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
- Review the source model card and license before redistribution or deployment.
- Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
- Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.
Quantization or conversion changes numerical behavior, memory use, and throughput relative to the source checkpoint; validate quality on your own workload.
Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for
testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.
This is an uncensored version of Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled 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.
ollama
Please use the latest version of ollama v0.17.7
You can use huihui_ai/qwen3.5-abliterated:27b-Claude directly,
ollama run huihui_ai/qwen3.5-abliterated:27b-Claude
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.
Donation
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