mlboydaisuke/all-mpnet-base-v2-ExecuTorch

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)