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
- Xet hash:
- 73686d5eef678ec10b022d31a663a051b4596d3cafa6b4525cb527da01e7406b
- Size of remote file:
- 2.38 GB
- SHA256:
- f11f4a9f699915110fad6a0e488ec310f7bffbf07acd90ffea1344de0805d708
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