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.
Build a custom deep researcher powered by state-of-the-art models that continuously process and synthesize multimodal enterprise data, enabling reasoning, planning, and refinement to generate comprehensive reports.
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.
Power fast, accurate semantic search across multimodal enterprise data with NVIDIA’s RAG Blueprint—built on NeMo Retriever and Nemotron models—to connect your agents to trusted, authoritative sources of knowledge.
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.
