Add model card

README.md

@@ -0,0 +1,105 @@
+---
+license: agpl-3.0
+library_name: mlx
+tags:
+  - object-detection
+  - yolo
+  - yolo26
+  - mlx
+  - apple-silicon
+  - on-device
+  - edge
+pipeline_tag: object-detection
+datasets:
+  - coco
+model-index:
+  - name: yolo26m-mlx
+    results:
+      - task:
+          type: object-detection
+          name: Object Detection
+        dataset:
+          name: COCO val2017
+          type: coco
+        metrics:
+          - type: mAP
+            value: 0.523
+            name: mAP@0.5:0.95
+---
+
+# YOLO26m (MLX)
+
+Pure-MLX weights for **YOLO26m**, ready to run on Apple Silicon with
+[`yolo-mlx`](https://github.com/thewebAI/yolo-mlx). No PyTorch at runtime,
+no cloud calls, no waiting on someone else's API — everything stays on your Mac.
+
+This is a mid-size variant in the YOLO26 MLX family: higher accuracy than n/s while still fast enough for many real-time use cases.
+
+## Quickstart
+
+```bash
+pip install yolo-mlx huggingface_hub
+```
+
+```python
+from huggingface_hub import hf_hub_download
+from yolo26mlx import YOLO
+
+weights = hf_hub_download("webAI-Official/yolo26m-mlx", "yolo26m.npz")
+model = YOLO(weights)
+
+results = model.predict("https://ultralytics.com/images/bus.jpg", conf=0.25)
+results[0].save()
+```
+
+## Specs
+
+| Variant | mAP@0.5:0.95 | FPS (M4 Pro) | Best for                     |
+|---------|--------------|--------------|------------------------------|
+| yolo26m | 52.3%        | 55          | Higher accuracy, still fast |
+
+Other variants in this family:
+[`yolo26n-mlx`](https://huggingface.co/webAI-Official/yolo26n-mlx) ·
+[`yolo26s-mlx`](https://huggingface.co/webAI-Official/yolo26s-mlx) ·
+[`yolo26l-mlx`](https://huggingface.co/webAI-Official/yolo26l-mlx) ·
+[`yolo26x-mlx`](https://huggingface.co/webAI-Official/yolo26x-mlx)
+
+## Requirements
+
+- Apple Silicon Mac (M1, M2, M3, or M4)
+- macOS 14.0+
+- Python 3.10+
+
+Intel Macs are not supported — the whole point of MLX is Apple Silicon native acceleration.
+
+## What's in this repo
+
+| File          | Description                                         |
+|---------------|-----------------------------------------------------|
+| `yolo26m.npz` | MLX-format weights, converted from the YOLO26m `.pt` checkpoint and verified shape-by-shape against the source. |
+| `README.md`   | This card.                                          |
+
+## Training data
+
+Pretrained on [COCO](https://cocodataset.org/) (80 classes). For domain-specific
+use cases, fine-tune on your own data — see the
+[training guide](https://github.com/thewebAI/yolo-mlx/blob/main/GUIDE_TRAINING_BENCHMARK.md)
+in the upstream repo.
+
+## License
+
+AGPL-3.0, inherited from upstream
+[`thewebAI/yolo-mlx`](https://github.com/thewebAI/yolo-mlx).
+Free to use, fork, modify, and ship for personal projects, research, and
+prototypes. If you deploy this as a hosted service for real users, AGPL
+requires you to publish your source under the same license.
+
+## About webAI
+
+[webAI](https://www.webai.com/) builds the sovereign AI platform — AI that runs
+on your infrastructure, stays under your control, and compounds with your
+knowledge. Every release here reflects a simple belief: **open models, owned
+locally, coordinated intelligently, compound into something no centralized
+system can match.**
+
+🌐 [webai.com](https://www.webai.com/) · 💬 [community.webai.com](https://community.webai.com)