Instructions to use MedMLX/totalsegmentator-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/totalsegmentator-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/totalsegmentator-mlx --local-dir totalsegmentator-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from MedMLX/totalsegmentator-mlx: direct link, hf CLI and curl.
- Browser
- Download file 1.9 kB
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https://huggingface.co/MedMLX/totalsegmentator-mlx/resolve/main/README.md
- Command line
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hf download hf://MedMLX/totalsegmentator-mlx/README.md
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curl -L -o README.md https://huggingface.co/MedMLX/totalsegmentator-mlx/resolve/main/README.md
1.9 kB
| license: apache-2.0 | |
| library_name: mlx | |
| pipeline_tag: image-segmentation | |
| tags: | |
| - mlx | |
| - apple-silicon | |
| - medical-imaging | |
| - segmentation | |
| - nnunet | |
| - safetensors | |
| - research | |
| # TotalSegmentator v2 CT for MLX | |
| Native MLX safetensors for TotalSegmentator v2 CT inference on Apple Silicon. The five 1.5 mm standard models are in `291`–`295`, the 3 mm fast model in `297`, the 6 mm coarse model in `298`, and the body crop model in `300`. Each folder includes nnU-Net plans and model metadata. | |
| The weights were converted from the [official TotalSegmentator v2.0.0 weight archives](https://github.com/wasserth/TotalSegmentator/releases/tag/v2.0.0-weights) for use with [totalsegmentator-mlx](https://github.com/MedMLX/totalsegmentator-mlx). Conversion changes tensor names and layouts for MLX; the models were not retrained. | |
| ## Quick start | |
| ```bash | |
| uv venv --python 3.13 | |
| source .venv/bin/activate | |
| uv pip install \ | |
| "nnunet-mlx>=0.6.4" \ | |
| "totalsegmentator-mlx>=0.1.6" \ | |
| huggingface-hub | |
| hf download MedMLX/totalsegmentator-mlx --revision 6c03d6f90ccf53f224c3985cbdecf1eaa96555af --local-dir models | |
| totalsegmentator-mlx predict -i ct.nii.gz -o mask.nii.gz --model-dir models | |
| ``` | |
| The `total` and `body` tasks are distributed by the authors under Apache-2.0. See the [upstream repository](https://github.com/wasserth/TotalSegmentator) and the included license for terms and attribution. Please cite TotalSegmentator and nnU-Net as described by the upstream authors. For research and education; outputs require human review. | |
| ## Citation | |
| If you use MedMLX, please cite the [MedMLX paper](https://doi.org/10.64898/2026.10.28.26366762): | |
| ```bibtex | |
| @article{sandoval2026medmlx, | |
| title = {MedMLX: On-Device Medical Imaging AI for Apple Silicon}, | |
| author = {Sandoval, A. and Sandoval, J. and Saleem, A.}, | |
| journal = {medRxiv}, | |
| year = {2026}, | |
| doi = {10.64898/2026.10.28.26366762} | |
| } | |
| ``` | |