How to use from
Pi
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4"
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

🦆 zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4

This model was converted to MLX from wangzhang/gemma-4-26B-A4B-it-abliterix using mlx-vlm version 0.4.4. Please refer to the original model card for more details.

🌟 Quality

Quantized vision language model with 4.843 bits per weight.

mlx_vlm.convert --quantize --q-bits 4 --q-group-size 16 --q-mode nvfp4

🛠️ 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:

{%- set enable_thinking = true %}

You may also need to adjust your environment’s Reasoning Section Parsing to recognize <|channel>thought as the Start String, and <channel|> as the End String.

🖥️ Use with mlx

pip install -U mlx-vlm
mlx_vlm.generate --model zecanard/gemma-4-26B-A4B-it-uncensored-abliterix-MLX-4bit-nvfp4 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
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