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

- Xet hash:
- 725f89c812df95c7d5a1af9f932feb0402a84829d9607c9681d4bc72a617cc00
- Size of remote file:
- 195 kB
- SHA256:
- 80b5c5a3c6f27ca583ef7d1c61cc95fe680a07da67eb5d92f64dd71959b84bdd
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