Text Ranking
Transformers
Safetensors
multilingual
t5gemma2
text2text-generation
reranker
encoder-decoder
FBNL
Retrieval
RAG
Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Nano with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Nano") model = AutoModelForMultimodalLM.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Nano") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 444d2a947a65106616b1684a723d5ed1dc1ce80aadedce2df71b60032ced5261
- Size of remote file:
- 293 kB
- SHA256:
- 076c0afa6e9a032cd65552385b6ac93e442e5a5ae602d096c7aa44c2bebbb2d3
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