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)
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", device_map="auto")
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{ "query_max_length": 512, "document_max_length": 1024, "encoder_chunk_size": 4, "system_instruction": "Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\"." }
