Command lines¶

mbext is used through its command line, python -m modelbuilder.builder. This page documents the available options.

Converting a model¶

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

Main arguments¶

-m, --model_name: Model name on the Hugging Face hub. Do not use together with -i/--input.
-i, --input: Path to a local folder containing the Hugging Face config, model and tokenizer, or the path to a float16/float32 GGUF file.
-o, --output: Folder where the ONNX model and the additional files are written.
-p, --precision: Precision of the model. One of int2, int4, int8, int16, bf16, fp16 or fp32.
-e, --execution_provider: Execution provider to target: cpu, cuda, dml, webgpu or NvTensorRtRtx.
-c, --cache_dir: Cache directory for Hugging Face files and temporary ONNX external data files. Defaults to ./cache_dir.
--private: Convert a custom model implemented in separate files. See Custom model in a separate file (--private).
--extra_options: Space-separated KEY=VALUE pairs controlling advanced behaviour (quantization block size, accuracy level, weight sharing, LoRA adapter, number of layers, and many more).

Valid precision / execution provider combinations are: FP32 CPU, FP32 CUDA, FP16 CUDA, FP16 DML, BF16 CUDA, FP16 TRT-RTX, BF16 TRT-RTX, INT4 CPU, INT4 CUDA, INT4 DML and INT4 WebGPU.

Full help¶

The authoritative and always up-to-date reference is the command’s own help, which lists every --extra_options key with its description:

python -m modelbuilder.builder --help

Running the private fast tests¶

When neither -m/--model_name nor -i/--input is provided, the fast-test-file from the --private option is executed as a script instead of converting a model:

python -m modelbuilder.builder --private "modeling.py;convert.py;test.py"

In this mode -o, -p and -e are not required. See Custom model in a separate file (--private).