Perplexity AI הוציאה לאור pplx-embed-v2-late: מודל 0.6B לגבול ו-9B שסוקר 92.4% ב-MADQA

תקציר מקורי באנגליתPerplexity has released pplx-embed-v2-late , a pair of ColBERT-style multimodal embedding models. They come in 2 sizes: 0.6B for fast, cheap queries and 9B for maximum quality. Both models retrieve text, images and rendered PDF pages, and they share one embedding space. Is it deployable? Yes, if you host it yourself. Both models are on Hugging Face under the MIT license. A hosted Perplexity API endpoint is planned but not live. TL;DR The best The 0.6B model uses about 340M active parameters for images and stays close to 8B rivals. A 9B index can be searched with 0.6B queries, recovering about half the 9B quality gap on text at 0.6B query cost. Its 128-dim token vectors are 16x to 32x narrower than rivals at 2,048 to 4,096 dims. MIT license, with commercial use allowed. The worst It stores