Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for
  Multilingual and Cross-Lingual Tasks
Authors: name: Yauhen Babakhin, name: Radek Osmulski, name: Ronay Ak, name: Gabriel Moreira, name: Mengyao Xu, name: Benedikt Schifferer, name: Bo Liu, name: Even Oldridge
Organization: NVIDIA

We introduce llama-embed-nemotron-8b, an open-weights text embedding model
that achieves state-of-the-art performance on the Multilingual Massive Text
Embedding Benchmark (MMTEB) leaderboard as of October 21, 2025. While recent
models show strong performance, their training data or methodologies are often
not fully disclosed. We aim to address this by developing a fully open-source
model, publicly releasing its weights and detailed ablation studies, and
planning to share the curated training datasets. Our model demonstrates
superior performance across all major embedding tasks -- including retrieval,
classification and semantic textual similarity (STS) -- and excels in
challenging multilingual scenarios, such as low-resource languages and
cross-lingual setups. This state-of-the-art performance is driven by a novel
data mix of 16.1 million query-document pairs, split between 7.7 million
samples from public datasets and 8.4 million synthetically generated examples
from various open-weight LLMs. One of our key contributions is a detailed
ablation study analyzing core design choices, including a comparison of
contrastive loss implementations, an evaluation of synthetic data generation
(SDG) strategies, and the impact of model merging. The llama-embed-nemotron-8b
is an instruction-aware model, supporting user-defined instructions to enhance
performance for specific use-cases. This combination of top-tier performance,
broad applicability, and user-driven flexibility enables it to serve as a
universal text embedding solution.