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Llama‑Embed‑Nemotron‑8B Ranks Among Top Open Models for Multilingual Retrieval
NVIDIA’s Llama‑Embed‑Nemotron‑8B model has been recognized as the top open and portable model on the Multilingual Text Embedding Benchmark leaderboard.
Built on the meta‑llama/Llama‑3.1‑8B architecture, Llama‑Embed‑Nemotron‑8B is a research text embedding model that converts text into 4,096‑dimensional vector representations. Designed for flexibility, it supports a wide range of use cases, including retrieval, reranking, semantic similarity and classification across more than 1,000 languages.
Trained on a diverse collection of 16 million query–document pairs — half from public sources and half synthetically generated — the model benefits from refined data generation techniques, hard‑negative mining and model‑merging approaches that contribute to its broad generalization capabilities.
This result builds on NVIDIA’s ongoing research in open, high‑performing AI models. Following earlier leaderboard recognition for the Llama NeMo Retriever ColEmbed model, the success of Llama‑Embed‑Nemotron‑8B highlights the value of openness, transparency and collaboration in advancing AI for the developer community.
Check out Llama-Embed-Nemotron-8B on Hugging Face, and learn more about the model, including architectural highlights, training methodology and performance evaluation.
