mist-reranker-22.7M
A compact, fast English cross-encoder reranker. It scores a (query, passage) pair directly and
reorders the top-k candidates from a first-stage retriever — the second stage of a search / RAG
pipeline. At ~22.7M parameters it runs comfortably on a single modest GPU or CPU, and recovers most of
the ranking quality of rerankers an order of magnitude larger.
📄 Model details
| Property | mist-reranker-22.7M |
|---|---|
| Type | Cross-encoder reranker (single relevance score) |
| Total parameters | ~22.7M |
| Backbone | cross-encoder/ms-marco-MiniLM-L6-v2 (MiniLM-L6-H384) |
| Layers | 6 |
| Hidden size | 384 |
| Vocabulary size | 30,522 |
| Output | 2-class logits → relevance = softmax(logits)[:, 1] |
| Max sequence length | 512 |
| Training precision | BF16 |
| Language | English |
| License | Apache-2.0 |
Training: fine-tuned on olaverse/reranker-general-en-llm-judged
(pairs-graded, 844k pairs) with a hybrid objective — binary cross-entropy on the LLM-judge relevance
label, plus an auxiliary term distilling the continuous teacher score from BAAI/bge-reranker-v2-m3.
🏃 How to run
Install sentence-transformers:
pip install -U sentence-transformers
This is a 2-class head, so relevance is the positive-class probability, softmax(logits)[:, 1]:
import torch
from sentence_transformers import CrossEncoder
model = CrossEncoder("olaverse/mist-reranker-22.7M")
query = "who wrote hamlet"
passages = [
"Hamlet is a tragedy written by William Shakespeare around 1600.",
"The capital of France is Paris.",
"Macbeth is one of Shakespeare's shortest tragedies.",
]
logits = model.predict([[query, p] for p in passages], convert_to_tensor=True)
scores = torch.softmax(logits, dim=-1)[:, 1] # relevance = P(relevant)
for p, s in sorted(zip(passages, scores.tolist()), key=lambda x: -x[1]):
print(f"{s:.4f} {p}")
To rerank a retrieved candidate list, score every candidate against the query and sort by the
relevance score descending. Keep query first and passage second in each pair — the model is trained
on that order.
📈 Performance
NanoBEIR (NanoNQ, NanoHotpotQA, NanoFEVER), NDCG@10. Every reranker reorders the same candidate sets; the candidate order before reranking (first-stage floor) scores 0.7126.
| Model | Params | NDCG@10 |
|---|---|---|
| BAAI/bge-reranker-v2-m3 | ~568M | 0.9058 |
| cross-encoder/ms-marco-MiniLM-L12-v2 | ~33M | 0.8670 |
| mist-reranker-22.7M | ~22.7M | 0.8543 |
| cross-encoder/ms-marco-MiniLM-L6-v2 | ~22.7M | 0.8495 |
| BAAI/bge-reranker-base | ~278M | 0.8238 |
| mixedbread-ai/mxbai-rerank-xsmall-v1 | ~70M | 0.8184 |
| first-stage floor (no reranker) | — | 0.7126 |
At ~22.7M it lifts NDCG@10 by +0.14 over the first-stage candidate order, beats bge-reranker-base
(~12× larger) and mxbai-rerank-xsmall-v1, and closes most of the gap to its bge-reranker-v2-m3
teacher.
License
Released under Apache-2.0.
Citation
@misc{mist-reranker-22.7M,
title = {mist-reranker-22.7M},
author = {Olaverse},
year = {2026},
url = {https://huggingface.co/olaverse/mist-reranker-22.7M}
}