Supertron2 Reranker
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Supertron2-Reranker-8B: A Compact Cross-Encoder Reranking Model
Model Description
Supertron2-Reranker-8B is a reranking model built on top of Qwen/Qwen3-VL-Reranker-8B. It is designed to score query-document pairs for retrieval pipelines, search systems, and RAG applications where a stronger second-stage ranker is useful.
- Developed by: Surpem
- Model type: Cross-Encoder Reranker
- Architecture: Qwen3-VL reranker, 8B parameters
- License: Apache 2.0
Capabilities
Search Reranking
Supertron2-Reranker-8B can compare a user query against candidate passages and assign relevance scores. It is intended as a second-stage reranker after a faster retriever has already selected candidate documents.
RAG Pipelines
The model can help improve retrieval-augmented generation by pushing more relevant documents toward the top of the context window before answer generation.
Question-Document Matching
Supertron2-Reranker-8B is useful for matching questions to passages, snippets, help-center articles, documentation chunks, and other text candidates.
Instruction-Aware Retrieval
The model is prompted for relevance scoring, making it suitable for natural language search tasks where query intent matters.
Get Started
from sentence_transformers import CrossEncoder
model_id = "Surpem/Supertron2-Reranker-8B"
model = CrossEncoder(model_id)
pairs = [
("What is the capital of France?", "Paris is the capital and largest city of France."),
("What is the capital of France?", "Mars is often called the red planet."),
]
scores = model.predict(pairs)
print(scores)
Example reranking:
query = "How do I reset my password?"
documents = [
"Use the account recovery page to reset your password.",
"Our refund policy allows returns within 30 days.",
"Two-factor authentication adds extra login security.",
]
results = model.rank(query, documents)
print(results)
Hardware Requirements
| Precision | Min VRAM | Recommended |
|---|---|---|
| bfloat16 | 18 GB | 24 GB+ |
| 4-bit quantized | 6 GB | 10 GB+ |
For larger batches or long documents, use more VRAM or reduce the batch size/max sequence length.
Intended Use
Supertron2-Reranker-8B is intended for:
- Search reranking
- RAG document reranking
- Query-passage relevance scoring
- Documentation and knowledge-base retrieval
- Evaluation of candidate retrieval results
It is not intended to be used as a standalone chat model.
Limitations
- The model scores relevance; it does not generate answers.
- It should be evaluated on your own retrieval domain before production use.
- Long documents may need chunking before reranking.
- Relevance scores are relative and may not be calibrated across unrelated queries.
- The model may still rank incorrect, outdated, or unsafe content highly if it appears textually relevant.
Citation
@misc{surpem2026supertron2-reranker-8b,
title={Supertron2-Reranker-8B -- Compact Cross-Encoder Reranking Model},
author={Surpem},
year={2026},
url={https://huggingface.co/Surpem/Supertron2-Reranker-8B},
}
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from sentence_transformers import CrossEncoder model = CrossEncoder("Surpem/Supertron2-Reranker-8B") 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)