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DeepSparse is able to improve latency performance on a 10 core laptop by 4.8X and up to 3.5X on a 16 core AWS instance.


This is the quantized (INT8) ONNX variant of the bge-large-en-v1.5 embeddings model accelerated with Sparsify for quantization and DeepSparseSentenceTransformers for inference.

pip install -U deepsparse-nightly[sentence_transformers]
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-large-en-v1.5-quant', export=False)

# Our sentences we like to encode
sentences = ['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 embeddings
for sentence, embedding in zip(sentences, embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding.shape)

For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.

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