Text Ranking
sentence-transformers
Safetensors
Transformers
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
- sentence-transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Nano with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("KaLM-Embedding/KaLM-Reranker-V1-Nano") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Nano with Transformers:
# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
30 days ago
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Update vllm_support/README.md
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Fix FastAPI shutdown handler and add request examples
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Update vllm_support/README.md
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Update vllm_support/src/kalm_t5gemma2_vllm_plugin/client.py
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update vllm support
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