Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
FLUX.1 Kontext is a multimodal model that enables in-context image generation and editing.
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
Multi-modal model to classify safety for input prompts as well output responses.
Multimodal question-answer retrieval representing user queries as text and documents as images.
Efficient multimodal model excelling at multilingual tasks, image understanding, and fast-responses
Powerful, multimodal language model designed for enterprise applications, including software development, data analysis, and reasoning.
A general purpose multimodal, multilingual 128 MoE model with 17B parameters.
A multimodal, multilingual 16 MoE model with 17B parameters.
The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
Cutting-edge open multimodal model exceling in high-quality reasoning from images.
Cutting-edge open multimodal model exceling in high-quality reasoning from image and audio inputs.
Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Cutting-edge open multimodal model exceling in high-quality reasoning from images.
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.
Optimized community model for text embedding.
