Llama-3.3-Nemotron-Super-49B-v1.5-FP8 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens. Llama-3.3-Nemotron-Super-49B-v1.5-FP8 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to this paper The model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.
Llama-3.3-Nemotron-Super-49B-v1.5-NVFP4 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens. Llama-3.3-Nemotron-Super-49B-v1.5-NVFP4 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to this paper The model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.
`llama-embed-nemotron-8b` is a versatile text embedding model trained by NVIDIA and optimized for retrieval, reranking, semantic similarity, and classification use cases. This model has robust capabilities for multilingual and cross-lingual text retrieval. It is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model achieves state-of-the-art performance on the multilingual MTEB leaderboard as of October 21, 2025. Together with the model weights, we're releasing the full recipe behind the `llama-embed-nemotron-8b`: - A detailed technical report focusing on our Synthetic Data Generation (SDG) pipeline and core design choices. - The training dataset, featuring a curated mix of public and synthetic data. - The full training code via the NeMo AutoModel framework.
The **nvidia/llama-nemotron-colembed-vl-3b-v2** is a late interaction embedding model fine-tuned for query-document retrieval. Users can input `queries`, which are text, or `documents` which are page images, to the model. The model outputs ColBERT-style multi-vector numerical representations for input queries and documents. ✨ **Key Improvements in v2:** * ⚗️ **Advanced Model Merging:** Utilizes post-training model merging to combine the strengths of multiple fine-tuned checkpoints. This delivers the accuracy stability of an ensemble without any additional inference latency. * 🌍 **Enhanced Synthetic Data:** We significantly enriched our training mixture with diverse multilingual synthetic data, improving semantic alignment across languages and complex document types. This model is for non-commercial/research use only. Check the Nemotron ColEmbed v2 paper for more details.
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 representations. They are typically transformer encoders that process tokens of input text (for example: question, passage) to output an embedding.
llama-nemotron-embed-vl-1b-v2 was developed by NVIDIA for **multimodal** question-answering retrieval. The model can embed document pages in the form of image, text, or combined image–text inputs. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. We report the evaluation of this model on two internal multimodal retrieval benchmarks, and on the popular ViDoRe V1 and V2 benchmarks and the new Vidore V3 benchmark. An embedding model is a crucial component of a retrieval system because it transforms information into dense vector representations. An embedding model is typically a transformer encoder that processes tokens of input text or images (for example, questions, passages, or page images) to output an embedding. llama-nemotron-embed-vl-1b-v2 is a combined language and vision model. The llama-nemotron-embed-vl-1b-v2 is part of the Nemotron RAG collection of open models available on HuggingFace. It is also available for optimized inference as a NIM (NVIDIA Inference Microservice) from NVIDIA NeMo Retriever, which provides state-of-the-art, commercially-ready models and microservices optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can readily customize them for domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.
The Llama Nemotron Reranking 1B model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for **multilingual, cross-lingual** text question-answering retrieval, with support for **long documents (up to 8192 tokens)**. 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. This model is a component in a text retrieval system to improve the overall accuracy. A text retrieval system often uses an embedding model (dense) or lexical search (sparse) index to return relevant text passages given the input. A reranking model can be used to rerank the potential candidate into a final order. The reranking model has the question-passage pairs as an input and therefore, can process cross attention between the words. It’s not feasible to apply a Ranking model on all documents in the knowledge base, therefore, ranking models are often deployed in combination with embedding models. The Llama Nemotron Reranking 1B model is a part of the NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for their domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.
The `llama-nemotron-rerank-vl-1b-v2` was developed by NVIDIA for multimodal question-answering retrieval. It is optimized for providing a logit score that represents how relevant a document page is to a given query. The model can process documents in the form of image, text, or image and text combined. The expected images are screenshots of document pages or slides. Documents are ranked given a user query in text form. The model supports images containing text, tables, charts, and infographics. We report the model's performance by evaluating it on the popular ViDoRe V1, V2 and the new Vidore V3 (see Vidore LB for details) multimodal retrieval benchmarks, and on two internally curated visual retrieval datasets. The reranking model serves as a key component of a **multimodal** retrieval system, such as a vision RAG pipeline, where it helps improve overall accuracy. A multimodal retrieval system often uses a multimodal embedding model (dense) to return relevant documents given the input. A reranking model can be used to rerank the potential candidates into a final order. The reranking model takes the query and document pairs as input, and its self-attention can perform deeper interaction between their tokens. It’s not scalable to apply a ranking model to all documents in the knowledge base for a given query; therefore, ranking models are often deployed to rerank top candidate documents retrieved by embedding models. The llama-nemotron-rerank-vl-1b-v2 is most suitable for users who want to build a multimodal question-and-answer application over a large corpus, leveraging the latest dense retrieval technologies.
**llama-nv-embed-reasoning-3b** is a 3.2B-parameter embedding model designed to produce high‑quality sentence and document representations for retrieval, semantic search, and similarity tasks, with a strong focus on reasoning‑heavy content. Built on a Llama‑style encoder and trained with contrastive objectives on diverse text (including question–answer pairs, technical explanations, and multi‑step reasoning data), the model is optimized to: The model outputs dense vector embeddings suitable for use with standard vector databases and retrieval pipelines. Its 3B size offers a balance between quality and inference efficiency, making it suitable for both experimentation and latency‑sensitive workloads.
