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Input
:
input_ids
and
attention_mask
, both
[1, 256]
int64
Output
:
[1, 1024]
, CLS-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
CLS
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_bge_large_en_xnnpack_fp32.pte
1336.7
104.5
1.000000
0%
fp16
embed_bge_large_en_xnnpack_fp16.pte
668.8
223.3
1.000000
2%
Core ML (fp16, iOS)
embed_bge_large_en_coreml_all.pte
669.2
26.2
0.999997
9%
*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
100.7 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_bge_large_en_xnnpack_int8.pte
is
431.7 MB
— smaller than fp16's 668.8 MB, because the token embedding
table is only 125 MB of the 1336.7 MB model (9%), 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
0.0123
while the
closest fp32 decision — the gap between the document a query retrieves and the
runner-up — is
0.0090
. That is
137%
of
the room available, against a bar of 50%.
Correlation reads 0.999490 for this build, which no correlation gate
would stop.
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