mlboydaisuke / bge-reranker-v2-m3-ExecuTorch

bge-reranker-v2-m3 — ExecuTorch

A cross-encoder reranker: a query and one document in, one relevance score out. The second stage of on-device retrieval — an embedding model fetches candidates cheaply, this reads each candidate together with the query and scores it properly.

• Source : BAAI/bge-reranker-v2-m3 — 568M parameters, 24 XLM-RoBERTa layers, hidden 1024, 250k vocabulary
• License : Apache-2.0
• Input : input_ids and attention_mask , both [1, 512] int64
• Output : [1, 1] fp32 — the raw logit. sigmoid(x) maps it to 0..1 and does not change the ordering.

Variants

*Mac arm64, single process, median of 10, one query-document pair at 512 tokens — a reference point for relative cost, not a device number. PyTorch eager fp32 on the same machine: 172.7 ms.

Correlation is not reported because it cannot be: the output is a single number, and the correlation of a one-element vector is undefined. The column above is the error in the units the model is used in — logits — over 6 real query-document pairs, and every build listed reproduces eager's ranking order exactly.

What it does, on the shipped fp32 build

Query: "How many people live in Berlin?"

The narrowest gap between adjacent ranks here is 0.3211 logits.

It ranks across languages

The candidate list above includes a Japanese passage that answers the English query. This model puts it second at +6.73; ms-marco-MiniLM-L6, the English-only reranker on this shelf, scores the same passage -10.96 and puts it fifth of 6. That is what the 250k-token vocabulary buys, and it is also why this file is 25 times larger.

Not shipped

• int8 (dynamic) is not shipped : at 1363.3 MB it is larger than the fp16 build's 1136.3 MB, and its score error is 0.3883 logits. Dynamic int8 quantizes the linear weights and leaves the token embedding table in fp32, while fp16 halves that table too. The table here is 1024 MB of a 2272 MB model, and the arithmetic says int8 only comes out smaller when the table is under a third of the weights (757 MB) — measured on ten models on this shelf, the rule called all ten correctly.

Verification

python convert/export_rerank.py bge_reranker_v2_m3
python convert/check_rerank.py bge_reranker_v2_m3 fp32

The check has two halves. One is agreement with the model run in eager, in logits and in ranking order. The other is that the ranking is useful at all: the passage that answers the question has to outscore a passage about the same subject that does not — agreement alone would pass a build that ranked by document length in both arms.

(conversion scripts: executorch-models )