baa.ai · Merino-Mini
One model that does both halves of RAG retrieval — bi-encoder embedding and cross-encoder reranking — over a single shared word-embedding table. A 384-dimensional English model, ~44M parameters, by BAA AI (Black Sheep AI).
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What it is
A two-role retrieval model over a shared input word-embedding matrix (stored once). The bi-encoder embedder and a compact 6-layer cross-encoder reranker are built on the same MiniLM-L6-H384-uncased backbone, so their word-embedding table is stored a single time and injected into the reranker at load — a smaller download at no measured quality loss, with no retraining.
- Embed role: bi-encoder, 384-d, L2-normalized. Prepend
"Represent this sentence for searching relevant passages: "to queries. - Rerank role: cross-encoder, single relevance logit per (query, document) pair.
- Router: call
.embed(...)or.rerank(...).
Usage
from modeling_baa import BaaEmbeddingReranker # included in this repo
m = BaaEmbeddingReranker("baa-ai/Merino-Mini")
qv = m.embed(["how does a cross-encoder reranker work?"], is_query=True)[0]
dv = m.embed(["a cross-encoder scores a (query, document) pair jointly",
"bi-encoders embed query and document separately for fast retrieval"])
ranked = m.rerank("how does a cross-encoder reranker work?",
["a cross-encoder scores a (query, document) pair jointly",
"the mitochondria is the powerhouse of the cell"])
# -> [(doc, score), ...] sorted best-first
Specs
| Embedding dim | 384 |
| Parameters | ~44M (embedder + reranker, shared word-embedding table) |
| Languages | English |
| Max sequence length | 512 |
| Hardware | CPU / edge / GPU |
License & attribution
- BAA Contributions (shared-embedding architecture, router/loader code, packaging, weights, docs) are proprietary to BAA AI (Black Sheep AI) — see
LICENSE. - Incorporates the
MiniLM-L6-H384-uncasedbackbone under the MIT License — seeLICENSE-minilm.txt.
© 2026 BAA AI (Black Sheep AI) — baa.ai. Provided "as is" without warranty.
Certification & corpus fit (2026-07)
Position Balance (PB): 0.17 — PB measures how findable a chunk is through its second fact when two facts share one embedding (second-fact / first-fact top-1 retrieval on an adversarial 1,300-chunk audit; fleet range 0.16–0.69). Compact deployment tier: use with strictly atomic chunking (one claim per embedded chunk) and parent-document retrieval.
4-bit quantization: certified lossless under distractor stress. Paired contested-region robustness (gold document injected into pools of up to 100 near-topical distractors, n=300 queries, bootstrap CIs) is statistically indistinguishable from fp16 — an axis standard hit@k benchmarks do not measure.
Chunking prescription: embed one atomic claim per chunk and lead with its key entity; retrieve small, return the parent section for context. Basis: single-vector embeddings preserve ~one independent fact per chunk regardless of encoder family (measured across 12 encoders).