How to use from
Hermes Agent
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/Qwen3.6-27B-uncensored-abliterix-MLX-4bit-mixed_4_6"
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default zecanard/Qwen3.6-27B-uncensored-abliterix-MLX-4bit-mixed_4_6
Run Hermes
hermes
Quick Links

🦆 zecanard/Qwen3.6-27B-uncensored-abliterix-MLX-4bit-mixed_4_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 5.092 bits per weight. Combines the size and speed benefits of a 4-bit quant with higher precision where it matters most.

mlx_vlm.convert --quantize --q-group-size 32 --quant-predicate mixed_4_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-4bit-mixed_4_6 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
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Model size
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MLX
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4-bit

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