mbext documentation

mbext (modelbuilder) writes large language models in the ONNX format so they can run with onnxruntime-genai. The code base started as a fork of the model builder shipped in onnxruntime-genai and extends it with many more architectures and, above all, a fast and short test suite.

The source code is hosted on GitHub: xadupre/mbext.

Why mbext?

The main advantage of mbext is its short CI and fast tests. Every supported architecture is validated with a tiny, randomly-initialised model (typically a single hidden layer) that:

• runs fully offline — no model weights are downloaded from the Hugging Face hub, so tests never fail because of a network hiccup or a gated repository;

• runs in seconds, not minutes — the models are small enough that the whole tests/fast suite completes quickly on a standard CI runner;

• checks numerical discrepancies between the PyTorch reference and the ONNX model, so a regression in any builder is caught immediately.

This means a contributor adding a new architecture gets fast, deterministic feedback, and the project keeps a green CI without paying for GPU time or large downloads. See Design for how this is achieved.

User guide

• Design
• Command lines
• Supported architectures
• Custom model in a separate file (--private)

Comparisons

• Differences with the onnxruntime-genai model builder
• Differences with Mobius (onnxruntime/mobius)

Examples

• Examples gallery

Quick start

Convert Qwen/Qwen3-8B to ONNX for CPU with int4 precision:

python -m modelbuilder.builder \
    -m Qwen/Qwen3-8B \
    -o qwen3-8b-cpu-int4 \
    -p int4 \
    -e cpu \
    -c cache_dir

Run the fast tests:

pytest tests/fast

Indices and tables

• Index

• Search Page