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README.md

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+---
+license: apache-2.0
+tags:
+- executorch
+- xnnpack
+- pte
+- on-device
+- feature-extraction
+- sentence-similarity
+base_model:
+- sentence-transformers/all-mpnet-base-v2
+---
+# all-mpnet-base-v2 — ExecuTorch
+
+The most downloaded sentence-transformer there is. Text in, one
+768-dimensional vector out, for search and retrieval that never leaves the
+device.
+
+- **Source**: sentence-transformers/all-mpnet-base-v2 — 12 layers, 768 dimensions, 30,527 vocabulary
+- **License**: apache-2.0
+- **Input**: `input_ids` and `attention_mask`, both `[1, 256]` int64
+- **Output**: `[1, 768]`, mean-pooled and L2-normalised inside the graph
+
+## The recipe is in the graph, and it was read off this repo
+
+sentence-transformers stores it per model, and the shelf's seven embedding models do
+not agree. This one pools **mean** and
+**normalises**, read from
+`1_Pooling/config.json` and `modules.json` rather than inferred from the family name.
+Getting it wrong does not throw; it returns vectors that look fine and rank wrong.
+
+## Verification
+
+| build | file | size (MB) | Mac ms* | worst cosine vs eager | retrieval budget |
+|---|---|---|---|---|---|
+| fp32 | `embed_all_mpnet_xnnpack_fp32.pte` | 435.8 | 35.2 | 1.000000 | 0% |
+| fp16 | `embed_all_mpnet_xnnpack_fp16.pte` | 218.1 | 56.2 | 1.000000 | 11% |
+| Core ML (fp16, iOS) | `embed_all_mpnet_coreml_all.pte` | 220.2 | 6.2 | 0.999993 | 32% |
+
+\*Mac arm64, median of 10, one 256-token sequence — a reference point for relative
+cost, not a device number. Torch eager fp32 on the same machine is
+37.0 ms.
+
+Cosine is measured against the model run in eager through its own pooling, over eight
+sentences. The last column is the one that decides: rank those eight against each
+other, and ask whether this build's score error is smaller than the gap between the
+document a query retrieves and the runner-up. Every shipped build keeps all eight
+top-1 results.
+
+## Not shipped: int8
+
+`embed_all_mpnet_xnnpack_int8.pte` is **181.3 MB** — smaller than fp16's 218.1 MB, because the token embedding
+table is only 94 MB of the 435.8 MB model (22%), leaving most of the
+weight in linears for int8 to shrink.
+
+It is withheld on the number that decides. Ranking the eight test sentences against
+each other, this build moves a pair score by at most **0.0081** while the
+closest fp32 decision — the gap between the document a query retrieves and the
+runner-up — is **0.0026**. That is **316%** of
+the room available, against a bar of 50%.
+
+Correlation reads 0.998871 for this build, which no correlation gate
+would stop.
+
+torch.export -> to_edge_transform_and_lower(partitioner) -> .pte
+(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))