The NVIDIA Retrieval QA Llama3.2 1b Embedding NIM is an embedding NIM optimized for multilingual and crosslingual text question-answering retrieval.
Text Embedding NIM
NeMo Retriever Text Embedding NIM (Text Embedding NIM) brings the power of state-of-the-art text embedding models to your applications, offering unparalleled natural language processing and understanding capabilities. You can use Text Retriever NIM for semantic search, Retrieval Augmented Generation (RAG) pipelines, or any application that uses text embeddings. Text Embedding NIM is built on the NVIDIA software platform, incorporating CUDA, TensorRT, and Triton to offer out-of-the-box GPU acceleration.
Getting started with the NIM
Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more.
Security Vulnerabilities in Open Source Packages
Please review the Security Scanning tab on NGC to view the latest security scan results.
For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.
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NVIDIA AI Enterprise Documentation
Visit the NVIDIA Blueprints Documentation Hub for release documentation, deployment guides and more.
Enterprise Support
Get access to knowledge base articles and support cases or submit a ticket.
Governing Terms
The NIM container is governed by NVIDIA Agreements | Enterprise Software | NVIDIA Software License Agreement and NVIDIA Agreements | Enterprise Software | Product Specific Terms for AI Product; and the use of this model is governed by the ai-foundation-models-community-license.pdf.
Additional information: Llama 3.2 community license.
You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.