This repository contains code to run faster sentence-transformers using tools like quantization, ONNX and pruning.
Project description
Fast Sentence Transformers
This repository contains code to run faster sentence-transformers using tools like quantization and ONNX. Just run your model much faster, while reducing memory a lot of memory. There is not much to it!
Quickstart
from fast_sentence_transformers import FastSentenceTransformer as SentenceTransformer
# use any sentence-transformer
encoder = SentenceTransformer("all-MiniLM-L6-v2", device=-1, quantize=True)
encoder.encode("Hello hello, hey, hello hello")
encoder.encode(["Life is too short to eat bad food!"] * 2)
Benchmark
Indicative benchmark for CPU usage with smallest and largest model on sentence-transformers.
| model | Type | default | ONNX | ONNX+quantized |
|---|---|---|---|---|
| paraphrase-albert-small-v2 | memory | 1x | 1x | 1x |
| speed | 1x | 2x | 5x | |
| paraphrase-multilingual-mpnet-base-v2 | memory | 1x | 1x | 4x |
| speed | 1x | 2x | 5x |
Shout-Out
This package heavily leans on sentence-transformers and txtai.
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