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:
- ef3591349fcb8a615e3ddef40585080c9cc15012f699be25d838962daed557bc
- Size of remote file:
- 4.23 GB
- SHA256:
- 4b6ae77ac85b39e855e96d6b82d89a213cdda6aa71ad02a75d45a71a626b51c6
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