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 "ToPo-ToPo/diffusiongemma-26B-A4B-it-mlx-4bit"
Configure the model in Pi
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ToPo-ToPo/diffusiongemma-26B-A4B-it-mlx-4bit"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

ToPo-ToPo/diffusiongemma-26B-A4B-it-mlx-4bit

MLX 4bit conversion of google/diffusiongemma-26B-A4B-it (mlx-vlm). Block-diffusion LM built on Gemma 4 (25.2B total / 3.8B active, MoE 128+1 experts, vision).

Provenance (self-converted)

  • Source: google/diffusiongemma-26B-A4B-it (license: apache-2.0)
  • Tool: mlx-vlm 0.6.9 mlx_vlm.convert (4bit affine, group_size=64), ~5.130 bpw
  • model_type: diffusion_gemma is supported natively by mlx-vlm 0.6.9; no patch needed.
  • chat_template.jinja is not the base repo's copy: it is the patched Gemma 4 Canonical Chat Template from ToPo-ToPo/gemma-4-26B-A4B-it-mlx-4bit, which suppresses the thinking channel when enable_thinking is false (otherwise the literal word thought leaks into the answer). Thinking is off by default. Weights are unaffected — restore the base repo's template for stock behaviour.

Usage

from mlx_vlm import load
model, processor = load("ToPo-ToPo/diffusiongemma-26B-A4B-it-mlx-4bit")

Diffusion generation takes its own flags:

python -m mlx_vlm generate --model ToPo-ToPo/diffusiongemma-26B-A4B-it-mlx-4bit \
  --prompt "Why is the sky blue?" \
  --max-tokens 256 --max-denoising-steps 48 --diffusion-sampler entropy-bound
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