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YOLO26s-OptiQ-6bit
mlx-community/YOLO26s-OptiQ-6bit
YOLO26s-OptiQ-6bit is a object detection model from mlx-community. Use it when you need objects located in an image. It is set up for mlx. The card lists the license as agpl-3.0.
Mixed-precision quantized YOLO26s for Apple Silicon via optiq
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Repo size
9.3 MB
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At a glance
- Task
- Object Detection
- Library
- mlx
- License
- agpl-3.0
- Access
- Public
- Created
- Apr 11, 2026
- Updated
- Sep 28, 2026
- SHA
- 273631c2
Base models
Model card
From the Hugging Face model README
YOLO26s-OptiQ-6bit
Mixed-precision quantized YOLO26s for Apple Silicon via optiq
This is a mixed-precision quantized version of YOLO26s in MLX format, optimized with mlx-optiq for Apple Silicon inference via yolo-mlx.
Quantization Details
| Property | Value |
|---|---|
| Target BPW | 6.0 |
| Achieved BPW | 5.97 |
| Layers at 4-bit | 11 |
| Layers at 8-bit | 115 |
| Original size | 38.4 MB |
| Quantized size | 8.9 MB |
| Compression | 4.3x |
Benchmark Results (COCO128)
| Model | Total Detections | Avg/Image |
|---|---|---|
| optiq 6-bit | 633 | 4.9 |
| Original (FP32) | 681 | 5.3 |
Detection delta: -48 (-7.0%) at 4.3x compression.
Usage
Requires mlx-optiq and yolo-mlx:
pip install mlx-optiq yolo-mlx
from optiq.models.yolo import load_quantized_yolo
model = load_quantized_yolo("mlx-community/YOLO26s-OptiQ-6bit")
results = model.predict("image.jpg")
How optiq Works
optiq measures each conv layer's sensitivity via KL divergence on detection outputs, then assigns optimal per-layer bit-widths using greedy knapsack optimization. Sensitive layers (detection head, feature pyramid) get 8-bit precision while robust backbone layers get 4-bit.
Article
For more details on the methodology and results, see: Not All Layers Are Equal
Credits
- Task
- Object Detection
- Library
- mlx
- License
- agpl-3.0
- Created
- Apr 11, 2026
- Updated
- Sep 28, 2026