# Efficient retrieval

This is a quantized model based on ONNX, which is the uint8 quantized version of Qwen/Qwen3-Embedding-0.6B. It reduces the model size while maintaining retrieval performance.

This model is a quantized version of ntproctor/mass-academy-faq-embedder, offering multiple quantization type options, suitable for efficient feature extraction and sentence similarity calculation.

This is the Qwen3-Embedding-4B model after GPTQ quantization, with significantly reduced video memory usage and minimal performance loss.

bge_micro is a sentence embedding model based on sentence-transformers, focusing on sentence similarity calculation and feature extraction tasks.

A multilingual vectorizer developed by Sinequa that generates embedding vectors for input paragraphs or queries, used for similarity calculation and information retrieval.