Instructions to use StephanST/C-radiov4_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use StephanST/C-radiov4_quantized with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir C-radiov4_quantized StephanST/C-radiov4_quantized
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Upload so400m/mxfp8/README.md with huggingface_hub
Browse files- so400m/mxfp8/README.md +15 -2
so400m/mxfp8/README.md
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| Metric | Mean | Min |
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| Summary cosine | 0.
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| Spatial cosine | 0.
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This is lower precision than the 8-bit affine bundle. Treat this as experimental.
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## Usage
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```sh
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--image image.jpg \
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--image-size 512 \
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--dtype bfloat16 \
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--save-npz embedding.npz
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```
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| Metric | Mean | Min |
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| Summary cosine | 0.989676 | 0.949449 |
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| Spatial cosine | 0.993379 | 0.978096 |
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This is lower precision than the 8-bit affine bundle. Treat this as experimental.
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## Measured Speed
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Fast-kernel compiled-forward MLX measurements at `512x512`, batch 1:
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| Runtime | p50 latency | Throughput |
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| packed | 49.8 ms | 20.1 images/s |
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| dequantize at load | 32.5 ms | 30.8 images/s |
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`packed` keeps weights low-bit at runtime but is slower for this ViT encoder. Use
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`--quantized-runtime dequantize` when latency matters; it expands weights to bf16 at load.
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## Usage
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```sh
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--image image.jpg \
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--image-size 512 \
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--dtype bfloat16 \
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--quantized-runtime dequantize \
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--save-npz embedding.npz
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```
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