World-class multilingual and cross-lingual question-answering retrieval.

Generates embeddings of proteins from their amino acid sequences.

Cutting-edge open multimodal model exceling in high-quality reasoning from images.

NV-DINOv2 is a visual foundation model that generates vector embeddings for the input image.

Vision foundation model capable of performing diverse computer vision and vision language tasks.

GPU-accelerated generation of text embeddings used for question-answering retrieval.

GPU-accelerated generation of text embeddings used for question-answering retrieval.

NV-CLIP is a multimodal embeddings model for image and text.

Generates high-quality numerical embeddings from text inputs.

Embedding model for text retrieval tasks, excelling in dense, multi-vector, and sparse retrieval.

Cutting-edge open multimodal model exceling in high-quality reasoning from images.

GPU-accelerated generation of text embeddings.

GPU-accelerated generation of text embeddings used for question-answering retrieval.

Groundbreaking multimodal model designed to understand and reason about visual elements in images.

One-shot visual language understanding model that translates images of plots into tables.

Multi-modal model for a wide range of tasks, including image understanding and language generation.