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Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

Multimodal question-answer retrieval representing user queries as text and documents as images.

Predict complex structures using Boltz-2.

The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.

Generates a multiple sequence alignment from a query sequence and a protein sequence database search.

Predicts the 3D structure of a protein from its amino acid sequence, multiple sequence alignments, and templates.

Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.

Predicts the 3D structure of a protein from its amino acid sequence.

Generates embeddings of proteins from their amino acid sequences.

Predicts the 3D structure of a protein from its amino acid sequence.

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

ProteinMPNN is a deep learning model for predicting amino acid sequences for protein backbones.

English text embedding model for question-answering retrieval.

Multilingual text question-answering retrieval, transforming textual information into dense vector representations.

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.

A generative model of protein backbones for protein binder design.

Optimized community model for text embedding.

Predicts the 3D structure of a protein from its amino acid sequence.

Predicts the 3D structure of how a molecule interacts with a protein.