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"
File size: 2,225 Bytes
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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}
}
```
|