How to use from
OpenClaw
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 OpenClaw
# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ToPo-ToPo/diffusiongemma-26B-A4B-it-mlx-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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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