Image-Text-to-Text
MLX
English
qwen3_5
native-mlx
apple-silicon
fp32
ct
medical-imaging
nv-reason-ct
3d-vlm
vision-language
radiology
report-generation
conversational
custom_code
Instructions to use MedMLX/MLX-Reason-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/MLX-Reason-CT with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("MedMLX/MLX-Reason-CT") config = load_config("MedMLX/MLX-Reason-CT") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use MedMLX/MLX-Reason-CT with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "MedMLX/MLX-Reason-CT"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "MedMLX/MLX-Reason-CT" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use MedMLX/MLX-Reason-CT with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "MedMLX/MLX-Reason-CT"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default MedMLX/MLX-Reason-CT
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use MedMLX/MLX-Reason-CT with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "MedMLX/MLX-Reason-CT"
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 "MedMLX/MLX-Reason-CT" \ --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"
|
Download README.md from MedMLX/MLX-Reason-CT: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
-
https://huggingface.co/MedMLX/MLX-Reason-CT/resolve/main/README.md
- Command line
-
hf download hf://MedMLX/MLX-Reason-CT/README.md
-
curl -L -o README.md https://huggingface.co/MedMLX/MLX-Reason-CT/resolve/main/README.md
2.23 kB
| license: openmdw-1.1 | |
| language: | |
| - en | |
| library_name: mlx | |
| base_model: nvidia/NV-Reason-CT | |
| pipeline_tag: image-text-to-text | |
| inference: false | |
| tags: | |
| - mlx | |
| - native-mlx | |
| - apple-silicon | |
| - fp32 | |
| - ct | |
| - medical-imaging | |
| - nv-reason-ct | |
| - 3d-vlm | |
| - vision-language | |
| - radiology | |
| - report-generation | |
| <img src="https://huggingface.co/MedMLX/MLX-Reason-CT/resolve/main/banner.png" alt="MedMLX — Reason. 3D CT / VISION + LANGUAGE" width="100%"> | |
| # MLX-Reason-CT | |
| Generate reports and ask questions about 3D chest and abdomen CT, locally on Apple Silicon. | |
| [GitHub](https://github.com/MedMLX/mlx-reason-ct) · [Files](https://huggingface.co/MedMLX/MLX-Reason-CT/tree/main) · [Docs](https://github.com/MedMLX/mlx-reason-ct/blob/v0.3.6/README.md) · [Paper](https://doi.org/10.64898/2026.10.28.26366762) | |
| | Input | Output | | |
| | :--- | :--- | | |
| | 3D CT · NIfTI | Reports and CT question answering | | |
| **Apple Silicon · Metal · Python 3.13** | |
| ## Quick start | |
| ```bash | |
| uv venv --python 3.13 | |
| source .venv/bin/activate | |
| uv pip install "mlx-reason-ct==0.3.6" | |
| uv tool install huggingface-hub | |
| hf download MedMLX/MLX-Reason-CT --revision 5e5b5202fd04ac9f63ede203ebc9327aa676ae94 --local-dir models | |
| ``` | |
| Replace the example input paths with your own files. | |
| ```bash | |
| mlx-reason-ct report \ | |
| --input ct.nii.gz --model-dir models --output-dir outputs | |
| ``` | |
| Requires `mlx-reason-ct`; generic `mlx-vlm` and `mlx-lm` loaders are incompatible. Allow more than 22.7 GB unified memory for the model, preprocessing and macOS. | |
| ## Use & license | |
| For research and education. Not validated for clinical use. | |
| Weights: [OpenMDW-1.1](https://huggingface.co/MedMLX/MLX-Reason-CT/blob/main/LICENSE). [Notices](https://huggingface.co/MedMLX/MLX-Reason-CT/blob/main/NOTICE). | |
| ## Paper | |
| - Original model: [NV-Reason-CT: 3D Visual Language Model for CT Analysis](https://arxiv.org/abs/2609.27511). | |
| ## 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} | |
| } | |
| ``` | |