nabi-chan/Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-4bit

🌌 huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated converted to MLX 4-bit

About This Quantization

Apple Sllicon / MLX 4-bit

• Source Model (BF16) : huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
• Quantized By: @nabi-chan

Quickstart

Install

pip install -U "mlx-lm>=0.31.2"

Python

from mlx_lm import load, generate

model, tokenizer = load("nabi-chan/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-4bit")
print(generate(model, tokenizer, prompt="Explain quantum entanglement simply.", max_tokens=128))

CLI

python3 -m mlx_lm generate \
  --model nabi-chan/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MLX-4bit \
  --prompt "Write a haiku about Apple Silicon." \
  --max-tokens 128

Quantization Details

Property	Value
Method	MLX affine quantization
Bits / weight	4
Group size	64
Non-quant dtype	bfloat16
Quantizer version	mlx : 0.31.2 / mlx-lm : 0.31.3 / mlx-vlm: 0.4.4

Protected tensors keep their original dtype. In VLM models, vision tensors and some guarded layers may remain unquantized.

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Everything below is huihui-ai's original model card, preserved verbatim.

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huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

This is an uncensored version of lordx64/Qwen3.6-35B-A3B-Claude-4.7-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

You can use huihui_ai/qwen3.6-abliterated:35b-Claude-4.7 directly,

ollama run huihui_ai/Qwen3.6-abliterated:35b-Claude-4.7

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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