AI Models
OlaverseMist Reranker 22.7M
MIST Rerank family · open weights on Hugging Face.
Cross-encoder reranker (22.7M-parameter micro) that re-orders retrieved passages by true relevance for search and RAG.
Sentence Transformers
Safetensors
Bert
Reranker
Cross Encoder
Text Ranking
Mist
English
Dataset:olaverse/reranker General En Llm Judged
Base: cross-encoder/ms-marco-MiniLM-L6-v2
Base: cross-encoder/ms-marco-MiniLM-L6-v2
Licence: apache-2.0
Model Index
Text Embeddings Inference
Endpoints Compatible
0
Likes
38
Downloads
Jun 2026
Created
Model Card
Built for production use.
Quick Start
Use Mist Reranker 22.7M, straight from its model card.
example.py
from the model cardimport 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}")
Model Card
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Model card metadata is available on Hugging Face.
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Transformers PyTorch Python
Built by Olaverse Labs
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Cross-encoder reranker (22.7M-parameter micro) that re-orders retrieved passages by true relevance for search and RAG.
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