Text Classification
Transformers
Safetensors
PEFT
English
retrievalrouter
feature-extraction
retrieval
document-retrieval
information-retrieval
routing
RAG
query-routing
late-interaction
lora
custom_code
Instructions to use emrekuruu/RetrievalRouter-lambda-l30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrekuruu/RetrievalRouter-lambda-l30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrekuruu/RetrievalRouter-lambda-l30", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emrekuruu/RetrievalRouter-lambda-l30", trust_remote_code=True, device_map="auto") - PEFT
How to use emrekuruu/RetrievalRouter-lambda-l30 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Use arXiv citation
Browse files
README.md
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## Citation
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```bibtex
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```
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## Citation
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```bibtex
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@misc{kuru2026retrievalrouterjointmodalityarchitecture,
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title={RetrievalRouter: Joint Modality and Architecture Selection for Document Retrieval},
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author={Emre Kuru and Mehmet Onur Keskin and Reza Farahbakhsh and Noel Crespi},
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year={2026},
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eprint={2608.25625},
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archivePrefix={arXiv},
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primaryClass={cs.IR},
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url={https://arxiv.org/abs/2608.25625},
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}
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```
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