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README.md

@@ -11,30 +11,37 @@ base_model:
 ---
 # dinov2_vits14 — ExecuTorch XNNPACK
 
-`dinov2_vits14_xnnpack_fp32.pte` (88.4 MB, fp32, XNNPACK-delegated)
-
 - **Source**: facebookresearch/dinov2 (torch.hub)
 - **License**: Apache-2.0
 - **Input**: [[1, 3, 518, 518]] — RGB, ImageNet norm, 518x518
 - **Output**: cls token [1,384], patch tokens [1,1369,384]
 
-## Verification (Mac arm64, executorch 1.4.0, torch 2.13.0)
+## Variants
+
+All variants take and return fp32 tensors — swap the `.pte` file, keep your app code.
+
+| precision | file | size (MB) | parity vs fp32 eager (worst corr) | Mac median (ms)* |
+|-----------|------|-----------|------------------------------------|------------------|
+| fp32 | `dinov2_vits14_xnnpack_fp32.pte` | 88.4 | 1.000000 | 159.0 |
+| fp16 | `dinov2_vits14_xnnpack_fp16.pte` | 44.8 | 0.999945 | 283.7 |
+| int8 (dynamic) | `dinov2_vits14_xnnpack_int8.pte` | 24.9 | 0.998009 | 155.7 |
+
+\*Mac arm64, single process, median of 10 — a reference point for relative cost
+only, not a device number (torch eager fp32 on the same machine: 50.8 ms).
 
-Parity vs torch fp32 eager on random input:
+## Verification (executorch 1.4.0, torch 2.13.0)
+
+Parity is measured against the fp32 eager model on real image input; `corr` is
+the correlation over all elements of each output tensor.
 
 | output | shape | max_abs_diff | corr |
 |--------|-------|--------------|------|
-| 0 | [1, 384] | 2.494e-05 | 1.000000 |
-| 1 | [1, 1369, 384] | 1.180e-04 | 1.000000 |
+| 0 | [1, 384] | 3.219e-05 | 1.000000 |
+| 1 | [1, 1369, 384] | 4.311e-04 | 1.000000 |
 
-Median latency over 10 runs (single Mac process, reference only — device numbers to follow):
-ExecuTorch 161.2 ms vs torch eager 50.1 ms.
+XNNPACK delegate coverage (fp32): 66.7% (414/621 ops); ops left on the portable kernels: `aten.expand_copy.default` x49, `aten.squeeze_copy.dims` x36, `aten.native_layer_norm.default` x25, `aten.mul.Scalar` x24, `aten.logical_not.default` x24, `aten.eq.Scalar` x12, `aten.full_like.default` x12, `aten.any.dim` x12, `aten.where.self` x12, `aten.select_copy.int` x1
 
 ## Conversion
 
 torch.export -> to_edge_transform_and_lower(XnnpackPartitioner) -> .pte
 (conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))
-
-
----
-Part of [executorch-models](https://github.com/john-rocky/executorch-models) — a verified .pte zoo for ExecuTorch. Conversion scripts and all models are indexed there.