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 h/mxfp8/README.md with huggingface_hub
Browse files- h/mxfp8/README.md +8 -10
h/mxfp8/README.md
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@@ -45,22 +45,21 @@ Against the local bf16 MLX bundle at `512x512` on 12 WALDO crop images:
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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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## Measured Speed
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| dequantize at load | 45.4 ms | 22.0 images/s |
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## Usage
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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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| Metric | Mean | Min |
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| Summary cosine | 0.990217 | 0.974710 |
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| Spatial cosine | 0.988696 | 0.976071 |
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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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Packed low-bit runtime, fast-kernel compiled-forward MLX at `512x512`, batch 1:
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| p50 latency | Throughput |
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| 52.6 ms | 19.0 images/s |
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The bf16 H bundle is faster on this workload when it fits. This bundle is experimental and
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lower precision than 8-bit affine.
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## Usage
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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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