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-l50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrekuruu/RetrievalRouter-lambda-l50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrekuruu/RetrievalRouter-lambda-l50", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emrekuruu/RetrievalRouter-lambda-l50", trust_remote_code=True, device_map="auto") - PEFT
How to use emrekuruu/RetrievalRouter-lambda-l50 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Commit History
-
Use arXiv citation
Verified signature42732e6 -
Update paper link to real arXiv (2608.25625)
Verified signature43eb2b3
-
Fix baseline link
Verified signature55dd78d -
Delete files configuration_irouterlm.py modeling_irouterlm.py with huggingface_hub
Verified signature8965161 -
Set model_type=retrievalrouter
Verified signaturebc4b3d1 -
Rename custom code to RetrievalRouter
Verified signaturee1da9a2 -
Rename custom code to RetrievalRouter
Verified signatureedfeb82 -
Add model card
Verified signature867a318
-
Upload tokenizer
Verified signature04451c6 -
Upload model
Verified signaturecd9c425 -
initial commit
Verified signature3ecba85