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.
This workflow shows how generative AI can generate DNA sequences that can be translated into proteins for bioengineering.
This blueprint shows how generative AI and accelerated NIM microservices can design protein binders smarter and faster.
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.
Predicts the 3D structure of a protein from its amino acid sequence.
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.
Generates high-quality numerical embeddings from text inputs.
A generative model of protein backbones for protein binder design.
Optimized community model for text embedding.
GPU-accelerated generation of text embeddings used for question-answering retrieval.