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license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-VL-Reranker-2B
pipeline_tag: text-ranking
library_name: sentence-transformers
tags:
- reranking
- retrieval
- rag
- cross-encoder
- qwen3-vl
- pytorch
---
# **Supertron2-Reranker-2B: A Compact Cross-Encoder Reranking Model**
## **Model Description**
**Supertron2-Reranker-2B** is a reranking model built on top of [Qwen/Qwen3-VL-Reranker-2B](https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B). 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, 2B parameters
* **License:** Apache 2.0
---
## **Capabilities**
### **Search Reranking**
Supertron2-Reranker-2B 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-2B 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**
```python
from sentence_transformers import CrossEncoder
model_id = "Surpem/Supertron2-Reranker-2B"
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:
```python
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 | 6 GB | 10 GB+ |
| 4-bit quantized | 3 GB | 6 GB+ |
For larger batches or long documents, use more VRAM or reduce the batch size/max sequence length.
---
## **Intended Use**
Supertron2-Reranker-2B 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**
```bibtex
@misc{surpem2026supertron2-reranker-2b,
title={Supertron2-Reranker-2B -- Compact Cross-Encoder Reranking Model},
author={Surpem},
year={2026},
url={https://huggingface.co/Surpem/Supertron2-Reranker-2B},
}
```
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