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-l70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrekuruu/RetrievalRouter-lambda-l70 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrekuruu/RetrievalRouter-lambda-l70", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emrekuruu/RetrievalRouter-lambda-l70", trust_remote_code=True, device_map="auto") - PEFT
How to use emrekuruu/RetrievalRouter-lambda-l70 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from emrekuruu/RetrievalRouter-lambda-l70: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/emrekuruu/RetrievalRouter-lambda-l70/resolve/main/tokenizer.json
- Command line
-
hf download hf://emrekuruu/RetrievalRouter-lambda-l70/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/emrekuruu/RetrievalRouter-lambda-l70/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 4a1d9a548938f3d35920b87587c9dd32646c1e2db022ad2cc7dde3b2be62cb50
- Size of remote file:
- 11.4 MB
- SHA256:
- 6adb90dd2c38b281238c0f606f444f129358c61dc015dd772cf77654df557cee
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