Aravind Srinivas· @AravSrinivas · X·· 2 小时前精选AI 评分65
AI 导读
Perplexity 开源 pplx-embed-v2-late，两个针对文本和图像的 late-interaction 多向量嵌入模型，大小为 9B 和 0.6B，共享同一嵌入空间，权重已在 Hugging Face 提供。9B 可用于索引多模态数据，0.6B 可在设备端查询，无需 OCR 即可检索 PDF 页面；模型在 MADQA 得分 92.4%，BrowseComp+ 得分 64%。
推荐理由
原文给出两个规模的嵌入模型、共享空间用法和基准分数，读者可据此评估多模态检索与端侧查询的可行方案。
正文 · 原文
We’re open-sourcing pplx-embed-v2-late, multi-vector embeddings for text and images, 9B and 0.6B, in one shared embedding space. You can use these to index multimodal data with 9B, and query on device with 0.6B. This also enables you to search over PDF pages with no OCR. And scores 92.4% on MADQA, 64% on BrowseComp+. Weights available on @huggingface now.
We're releasing pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages with a shared embedding space for cross-model querying. Both models achieve frontier performance and are publicly available on Hugging Face. https://www.perplexity.ai/hub/blog/multimodal-embeddings-beyond-a-single-vector在 X 查看被引用的帖子
来源：Aravind Srinivas · x.com