🦆 zecanard/Qwen3.6-27B-uncensored-abliterix-MLX-2bit-mixed_2_6
This model was converted to MLX from wangzhang/Qwen3.6-27B-abliterated using mlx-vlm version 0.6.3.
Please refer to the original model card for more details.
🌟 Quality
Mixed-precision quantized vision language model with an effective 3.524 bits per weight. Combines the size and speed benefits of a 2-bit quant with higher precision where it matters most.
mlx_vlm.convert --quantize --q-group-size 32 --quant-predicate mixed_2_6
🛠️ Customizations
This quant is aware of the current date, and also enables thinking (if available). You may disable this behavior by deleting the following line from the chat template, or changing true to false:
{%- set enable_thinking = true %}
A fix is also included for a thinking-related performance issue in Qwen 3.6.
🖥️ Use with mlx
pip install -U mlx-vlm
mlx_vlm.generate --model zecanard/Qwen3.6-27B-uncensored-abliterix-MLX-2bit-mixed_2_6 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
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Model size
4B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("zecanard/Qwen3.6-27B-uncensored-abliterix-MLX-2bit-mixed_2_6") config = load_config("zecanard/Qwen3.6-27B-uncensored-abliterix-MLX-2bit-mixed_2_6") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output)