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", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 71331f57e7e87779ff21c307d8575dc7a200e423bb622bbf632389dbbe2b40b7
- Size of remote file:
- 365 kB
- SHA256:
- e496b19a15b17327e357091d4f4cd3e9325e11514cf68498e7b47817ecf009a0
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