onebrain-rerank

About

What onebrain-rerank is

OneBrain Rerank v1 The first entry in the OneBrain model line — the cross-encoder reranker used by OneBrain native search (Tier-2 precision stage, CLI v3.4.7+). v1 is not fine-tuned. It is a size/speed-optimized int8 build of BAAI/bge-reranker-v2-m3 (Apache-2.0). Future versions (v2+) may be fine-tuned for OneBrain's vault-search domain; each version ships as its own repo and is sha256-pinned by the OneBrain CLI. Intended use - For: reranking a small set of (query, passage) candidates returned by a first-stage retriever (BM25 / vector), producing a calibrated 0–1 relevance score. This is the Tier-2 precision stage of OneBrain native search. - Not for: retrieval or embedding — it scores query–passage pairs, it does not produce vectors. It is a general (not domain-fine-tuned) reranker, so it is not tuned for any specialized corpus in v1. Provenance - Base: BAAI/bge-reranker-v2-m3 (Apache-2.0) - ONNX export: rozgo/bge-reranker-v2-m3 (fp32, 2.27 GB) - Quantization: dynamic int8 (QInt8 weights), onnxruntime 1.27.0, single-file output (DefaultTensorType=FLOAT for 2GB shape-inference skip) - modelint8.onnx — 569,011,484 bytes · sha256 dd7b26f4a233732aefbe857bef026050582dc7c1bdb8aeda909080bf15b2ad88 Why int8? OneBrain runs local-first and CPU-only (no GPU assumption; Raspberry-Pi-class hardware is the floor). A cross-encoder scores every candidate at query time, so both download size and CPU latency matter: - Size — the fp32 ONNX export is 2.27 GB; this int8 build is 569 MB (~4× smaller). That is the difference between a reranker most users won't download and one that can ship on by default. - CPU latency — dynamic-QInt8 integer kernels run faster on CPU. OneBrain's design target is ≤500 ms P50 for the Tier-2 stage on a warm daemon at the default candidate depth (a design target, not a published benchmark — no measured latency number is claimed here). - Quality cost — negligible — quantization perturbs the raw logits slightly, but reranking only needs relative ordering and calibrated separation, both preserved. On the validation pairs below the sigmoid-score delta vs fp32 is ≤ 0.0046 and the pairwise ranking is identical. int8 is not more accurate than fp32 — it is the

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys onebrain-rerank for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (onebrain-rerank below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"onebrain-rerank","input":"text to embed"}'

Details

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Licence

Open weights

Sources

Weights & code

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