gte-base-quant
This is the quantized (INT8) ONNX variant of the gte-base embeddings model created with DeepSparse Optimum for ONNX export/inference and Neural Magic's Sparsify for one-shot quantization.
Current list of sparse and quantized gte ONNX models:
Links | Sparsification Method |
---|---|
zeroshot/gte-large-sparse | Quantization (INT8) & 50% Pruning |
zeroshot/gte-large-quant | Quantization (INT8) |
zeroshot/gte-base-sparse | Quantization (INT8) & 50% Pruning |
zeroshot/gte-base-quant | Quantization (INT8) |
zeroshot/gte-small-sparse | Quantization (INT8) & 50% Pruning |
zeroshot/gte-small-quant | Quantization (INT8) |
pip install -U deepsparse-nightly[sentence_transformers]
from deepsparse.sentence_transformers import SentenceTransformer
model = SentenceTransformer('zeroshot/gte-base-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)
print("")
For further details regarding DeepSparse & Sentence Transformers integration, refer to the DeepSparse README.
For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.
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