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-l10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrekuruu/RetrievalRouter-lambda-l10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrekuruu/RetrievalRouter-lambda-l10", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emrekuruu/RetrievalRouter-lambda-l10", trust_remote_code=True, device_map="auto") - PEFT
How to use emrekuruu/RetrievalRouter-lambda-l10 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 760 Bytes
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"architectures": [
"RetrievalRouterModel"
],
"auto_map": {
"AutoConfig": "configuration_retrievalrouter.RetrievalRouterConfig",
"AutoModel": "modeling_retrievalrouter.RetrievalRouterModel"
},
"base_model_name": "Qwen/Qwen3-0.6B-Base",
"classifier_dropout": 0.1,
"dtype": "float32",
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2",
"3": "LABEL_3",
"4": "LABEL_4"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2,
"LABEL_3": 3,
"LABEL_4": 4
},
"model_type": "retrievalrouter",
"strategy_names": [
"MULTIMODAL_RERANK",
"MULTIMODAL-SINGLE",
"TEXT_RERANK",
"TEXT-SINGLE",
"BM25"
],
"transformers_version": "5.13.0"
} |