Model reference · open weights
llama-nemotron-rerank-vl is an open-weight embedding model from nvidia, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Model Overview Description: 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. This model is ready for commercial use. <br Use Case: <br 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. <br License/Terms of Use: This model and its associated configuration files are licensed under the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). Additional Information: Llama 3.2 Community Model License Agreement. Built with Llama. This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use. Release Date 12/18/2025 via https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | nvidia |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 1.7B |
| Variants | 1 |
| Runs with | transformers |
| Released | 2025-12-04 |
| Popularity | 34k downloads / month |
| Likes | 60 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| llama-nemotron-rerank-vl-1b-v2 | 1.7B | BF16 | ~3.9 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys llama-nemotron-rerank-vl for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llama-nemotron-rerank-vl below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"llama-nemotron-rerank-vl","input":"text to embed"}'
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗
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