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-l00 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrekuruu/RetrievalRouter-lambda-l00 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrekuruu/RetrievalRouter-lambda-l00", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emrekuruu/RetrievalRouter-lambda-l00", trust_remote_code=True, device_map="auto") - PEFT
How to use emrekuruu/RetrievalRouter-lambda-l00 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| { | |
| "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" | |
| } |