Instructions to use muvon/octomind-rerank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use muvon/octomind-rerank with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("muvon/octomind-rerank") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
muvon/octomind-rerank
Fine-tuned MS-MARCO MiniLM-L-6 cross-encoder (22M params, English) for
octomind capability auto-activation. Designed as the second stage after
the muvon/octomind-embed bi-encoder retrieves the top-N candidates.
Trained with BinaryCrossEntropyLoss over (anchor, positive) and
(anchor, hard_negative) pairs from the octomind-tap catalog. The hard
negatives target the confusable neighbors the bi-encoder alone cannot
reliably separate.
Use
Wired into octomind via octolib's HuggingFace reranker provider (candle backend).
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Model tree for muvon/octomind-rerank
Base model
microsoft/MiniLM-L12-H384-uncased Quantized
cross-encoder/ms-marco-MiniLM-L12-v2 Quantized
cross-encoder/ms-marco-MiniLM-L6-v2