AI Models
Mist Reranker 150m
MIST Rerank family · open weights on Hugging Face.
Cross-encoder reranker (150M-parameter ModernBERT) that re-orders retrieved passages by true relevance for search and RAG.
Sentence TransformersSafetensorsModernbertRerankerCross EncoderRetrievalRAGMistText RankingEnglishDataset:olaverse/reranker General En Llm JudgedBase: answerdotai/ModernBERT-baseBase: answerdotai/ModernBERT-baseLicence: apache-2.0Model IndexText Embeddings InferenceEndpoints Compatible
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Jun 2026
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Built for production use
Open-weights repository on Hugging Face
Integrated ecosystem protocol tier.
Compatible with Transformers library
Integrated ecosystem protocol tier.
Optimised for low-latency inference
Integrated ecosystem protocol tier.
Community engagement: 0 likes
Integrated ecosystem protocol tier.
/ Quick start
Use Mist Reranker 150m, straight from its model card.
example.pyfrom the model card
from sentence_transformers import CrossEncoder
model = CrossEncoder("olaverse/mist-reranker-150m")
query = "What causes ocean tides?"
passages = [
"Tides are caused by the gravitational pull of the Moon and Sun on Earth's oceans.",
"The Pacific Ocean is the largest of Earth's oceanic divisions.",
"Tidal energy is a renewable power source generated from the movement of tides.",
]
scores = model.predict([(query, p) for p in passages])
for s, p in sorted(zip(scores, passages), reverse=True):
print(f"{s:.3f} {p}")
/ Model card
Official repository README
Dynamically loaded from Hugging Face
Model card metadata is available on Hugging Face.
/ Built with
Transformers PyTorch Python
Ready to try Mist Reranker 150m?
Cross-encoder reranker (150M-parameter ModernBERT) that re-orders retrieved passages by true relevance for search and RAG.
/ Model ecosystem