baa.ai · Merino-XL-v2
One model that does both halves of RAG retrieval — bi-encoder embedding and cross-encoder reranking — over a single shared word-embedding table. A 1024-dimensional multilingual model, ~880M parameters, by BAA AI (Black Sheep AI).
Get the optimal model for your data
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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 v2 cross-encoder reranker are built on the same xlm-roberta-large 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, 1024-d, L2-normalized. Prepend
"query: "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-XL-v2")
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 | 1024 |
| Parameters | ~880M (embedder + reranker, shared word-embedding table) |
| Languages | multilingual |
| 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
xlm-roberta-largebackbone under the MIT License — seeLICENSE-xlm-roberta-large.txt.
© 2026 BAA AI (Black Sheep AI) — baa.ai. Provided "as is" without warranty.
⚠️ Retired from the recommended lineup (2026-07)
This model is superseded by Merino-Large and Merino-Large-v2, which match or exceed it on our evaluations, pass 4-bit distractor-stress certification cleanly, and cost less to serve. This model's 4-bit variant failed that certification (a small but statistically separated robustness regression, invisible to standard hit@k) and will not be published. fp16 weights remain available for reproducibility.