Text Ranking
Transformers
Safetensors
multilingual
t5gemma2
text2text-generation
reranker
encoder-decoder
FBNL
Retrieval
RAG
Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Small with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Small") model = AutoModelForMultimodalLM.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Small") - Notebooks
- Google Colab
- Kaggle
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# Citation
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If you find this model useful, please
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@misc{zhao2026kalmrerankerv1,
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title={KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking},
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```
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# Citation
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If you find this model useful, please consider citing our papers.
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```
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@misc{zhao2026kalmrerankerv1,
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title={KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking},
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