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
| from kalm_t5gemma2_vllm_plugin.client import main | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |