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The authors introduce Llama-Embed-Nemotron-8B, a new open-weights text embedding model achieving state-of-the-art results on the MMTEB benchmark. The model is trained on a novel data mix of 16.1 million query-document pairs, combining public datasets with synthetically generated data from open-weight LLMs. Key findings include the effectiveness of their data mix, the impact of different contrastive loss implementations, and the benefits of instruction-aware training for various embedding tasks, especially in multilingual scenarios.
Forget closed-source embedding models: llama-embed-nemotron-8b just topped the MMTEB leaderboard with fully open weights and a data recipe you can actually reproduce.
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.
NVIDIA