The Llama Nemotron Embedding 1B model is optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 8192 tokens) and dynamic embedding size (Matryoshka Embeddings). This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.
In addition to enabling multilingual and cross-lingual question-answering retrieval, this model reduces the data storage footprint by 35x through dynamic embedding sizing and support for longer token length, making it feasible to handle large-scale datasets efficiently.
An embedding model is a crucial component of a text retrieval system, as it transforms textual information into dense vector repre…
nemotron-3-ultra-550b-a55bСемейство: nemotronКонтекст (макс.): 1,05 млн токеновВвод: текст; вывод: текстВозможности: рассуждение, инструменты, структурированный выводПоставщики: Bothub, CommandCode, Kenari, Kilo Gateway, Kilo Gateway, NanoGPT и ещё 8
nebius/nvidia/nemotron-3-ultra-550b-a55bСемейство: nemotronКонтекст (макс.): 1,05 млн токеновВвод: текст; вывод: текстВозможности: рассуждение, инструментыПоставщики: Eden AI
Источник каталога: https://models.dev/api.json. Получено: 01.11.2026 01:14 UTC. Цены и лимиты — данные источника; перед использованием проверяйте условия поставщика.