| # MLX is not shipped for this model |
|
|
| The MLX artifacts for this model were removed deliberately. The exporter in |
| `export-scripts` is kept so the decision can be re-tested against a future |
| ExecuTorch/MLX release, but no `.pte` or `config.json` is published here. |
|
|
| ## Why |
|
|
| Measured on device (iPhone 16, ExecuTorch 1.4.1, Release build, 3 warmup runs + |
| 15 timed runs, median): |
|
|
| | backend | precision | median latency | |
| |---|---|---| |
| | Core ML | fp16 | **2.4 ms** | |
| | MLX | fp16 | 47.0 ms | |
|
|
| MLX is **19.6x slower than Core ML** for EfficientNet-V2-S @384. |
|
|
| This is not specific to this model. MLX was measured behind Core ML on every |
| convolutional vision model tested, by 7.5x to 19.6x: |
|
|
| | model | Core ML | MLX | MLX penalty | |
| |---|---|---|---| |
| | efficientnet-v2-s @384 | 2.4 ms | 47.0 ms | 19.6x | |
| | yolo26-s @640 | 3.7 ms | 59.2 ms | 16.0x | |
| | yolo26-seg-s @640 | 11.0 ms | 82.9 ms | 7.5x | |
| | yolo26-pose-n @384 | 1.2 ms | 12.0 ms | 10.0x | |
|
|
| Two things compound the latency result: |
|
|
| 1. **MLX quantization cannot shrink a conv model.** |
| `executorch.backends.mlx.llm.quantization.quantize_model_` only rewrites |
| `nn.Linear` and embedding modules. EfficientNet-V2-S holds just 6.0% of its |
| parameters (1,281,000 of 21,458,488) in its single `nn.Linear`, so 4-bit |
| weight quantization buys about 5% file size. `nvfp4` is a literal no-op: it |
| logs `Skipping classifier.1: weight shape [1000,1280] incompatible with |
| nvfp4 (group_size=16)` and emits a byte-identical artifact. |
| 2. **Core ML quantizes the whole model.** Core ML's |
| `op_linear_quantizer_config` reaches the convolutions, so int8/per_channel |
| halves the artifact again (43.9 MB fp16 -> 22.1 MB) at identical latency. |
| |
| ## Scope |
| |
| This applies to convolution-dominated vision models. It does **not** generalize: |
| |
| - `rfdetr-nano` (conv + transformer) measured only **1.24x** behind Core ML. |
| - The `lfm2.5` text encoders measured **2.7-3.1x faster on MLX** than XNNPACK. |
| |
| MLX suits matmul-heavy graphs. The right backend depends on model class, so |
| this file is not a statement about MLX in general. |
| |
| ## Availability history |
| |
| MLX for this model was only ever published on `main` / `v0.10.0`. No earlier tag |
| (`v0.9.0` and below) ever carried an MLX artifact for it, so there is no pinned |
| revision from which these files can still be fetched. They remain recoverable |
| from this repository's git history. |
| |
| A commit description in the removal commit stated that earlier tags "still |
| contain the MLX artifacts". That is incorrect, and this section is the |
| correction; HuggingFace commit descriptions cannot be edited after the fact. |
| |
