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Cataloged from RedHatAI/bge-base-en-v1.5-quant
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DeepSparse is able to improve latency performance on a 10 core laptop and a 16 core AWS instance by up to 4.5X.
This is the quantized (INT8) ONNX variant of the bge-base-en-v1.5 embeddings model accelerated with Sparsify for quantization and DeepSparseSentenceTransformers for inference.
For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.
pip install -U deepsparse-nightly[sentence_transformers]from deepsparse.sentence_transformers import DeepSparseSentenceTransformermodel = DeepSparseSentenceTransformer('neuralmagic/bge-base-en-v1.5-quant', export=False)
# Our sentences we like to encodesentences = ['This framework generates embeddings for each input sentence', 'Sentences are passed as a list of string.', 'The quick brown fox jumps over the lazy dog.']
# Sentences are encoded by calling model.encode()embeddings = model.encode(sentences)
# Print the embeddingsfor sentence, embedding in zip(sentences, embeddings): print("Sentence:", sentence) print("Embedding:", embedding.shape) print("")