Perplexity开源嵌入模型pplx-embed-v2-late，PDF检索可跳过OCR步骤

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• Perplexity开源嵌入模型pplx-embed-v2-late，PDF检索可跳过OCR步骤

  huggingface · 主条目

  Perplexity在Hugging Face以MIT许可证发布开源嵌入模型pplx-embed-v2-late，提供0.6B和9B两种规格，基于Qwen3.5构建，采用ColBERT式迟交互检索，支持文本、图像及PDF等渲染页面。两种规格共享同一嵌入空间；在ViDoRe v3基准图像赛道上，Perplexity报告的nDCG@10分别为62.3%和65.2%。

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  RT Laurence Moroney 🇺🇸🇮🇪 🏴󠁧󠁢󠁷󠁬󠁳󠁿 Still running OCR over your PDFs before you can search them? Perplexity's new open embedding models are built to skip that step. Perplexity released pplx-embed-v2-late on Hugging Face under the MIT license, in 0.6B and 9B sizes built on Qwen3.5. They're ColBERT-style late-interaction retrievers: every token gets its own 128-dimensional vector, and a query is scored by matching each of its tokens to the closest one in the document (MaxSim). They handle text, images, and rendered pages such as PDFs and slides. Both sizes share one embedding space, so the small model can query an index built with the 9B. On the image track of the public ViDoRe v3 benchmark, Perplexity reports nDCG@10 of 62.3% for the 0.6B and 65.2% for the 9B. Both were distilled from an internal 18B teacher. Storing a vector for every token makes these indexes big, so test on your own data first. Index a few hundred of your pages with the 9B, compare results when you query with the 0.6B and the 9B, and note how much disk the index takes. 🔍 #RAG https://huggingface.co/perplexity-ai/pplx-embed-v2-late-0.6b

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