Instructions to use devin-lai/MiniMax-H3-Image-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use devin-lai/MiniMax-H3-Image-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download devin-lai/MiniMax-H3-Image-mlx --local-dir MiniMax-H3-Image-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download CONTRIBUTING.md from devin-lai/MiniMax-H3-Image-mlx: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx/resolve/refs%2Fpr%2F1/CONTRIBUTING.md
- Command line
-
hf download hf://devin-lai/MiniMax-H3-Image-mlx@refs/pr/1/CONTRIBUTING.md
-
curl -L -o CONTRIBUTING.md https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx/resolve/refs%2Fpr%2F1/CONTRIBUTING.md
Contributing
Reports and measured improvements from more Apple silicon configurations are welcome. Open an issue with the command, variant, chip/GPU-core count, unified memory, macOS version and generated JSON record. Remove private prompt or reference-image content before sharing it.
For a local development environment:
uv venv --python 3.12
source .venv/bin/activate
uv pip install -e '.[dev]'
python -m pytest -q
ruff check .
ruff format --check .
python -m build
python tools/check_release.py dist/*.whl dist/*.tar.gz
The full numerical suite needs an Apple GPU. Portable tests run on Linux in CI; exact M5-kernel tests skip on other hardware. The tested macOS dependency snapshot is requirements-macos.lock.
For arithmetic or loading changes, compare matching prompt, seed, canvas, noise stream and variant, and retain image/trace hashes. Distinguish warm resident timings from first-load timings, and MLX allocations from process physical footprint. Benchmark method.
Keep this repository inference-only. Do not submit training, model conversion, packing encoders, online quantization, weight exporters, checkpoints, private publishing tools or credentials. The source and distribution audit checks reviewed module names and conversion operations. Public sample images remain in the existing access-reviewed Hub gallery under the model terms.
Changes to the CLI, model catalog or numerical behavior should update the usage/validation documents and include a focused regression test where useful.