nvidia/llama-nemotron-rerank-vl-1b-v2

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.

Use Case:

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.

License/Terms of Use:

The use of this model is governed by the NVIDIA Open Model License Agreement and the use of the post-processing scripts are licensed under Apache 2.0. Additional Information: Llama 3.2 Community Model License Agreement. Built with Llama.

Release Date

12/18/2025 via https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b-v2

Model Architecture:

Architecture Type: Transformer
Network Architecture: : Eagle VLM architecture with SigLIP 2 400M vision encoder and llama-nemotron-rerank-1b-v2 model as language model.

The llama-nemotron-rerank-vl-1b-v2 is a cross-encoder model with approximately 1.7B parameters. It is a fine-tuned version of an NVIDIA Eagle-family model, which consists of the SigLIP 2 400M vision encoder and the Llama 3.2 1B language model. The final embedding output by the decoder is aggregated using a mean pooling strategy, and a binary classification head is fine-tuned for the ranking task. The CrossEntropy loss is used to maximize the likelihood of (visual) documents containing information to answer the question and minimize the likelihood for (negative) documents that do not contain information to answer the question.

The vision-language model reranker incorporates key innovations from NVIDIA, including Eagle 2 work which uses a tiling-based VLM architecture, and nemoretriever-parse. The Eagle 2 architecture, available on Hugging Face, significantly enhances multimodal understanding through its dynamic tiling and mixture of vision encoders design. It particularly improves performance on tasks that involve high-resolution images and complex visual content.

Input(s):

Input Type(s): Image, Text

Input Format(s):

• Image: Red, Green, Blue (RGB)
  
• Text: String
  

Input Parameters:

• Image: Two-Dimensional (2D)
  
• Text: One-Dimensional (1D)
  

Other Properties Related to Input:

The model was fine-tuned exclusively on image data, using max_input_tiles = 4 and the maximum context length of 2048 tokens. For evaluation, it was tested on image-only, image+text, and text-only inputs, with max_input_tiles = 6 and the maximum context length of 10240 tokens. Inputs exceeding the maximum length are truncated.

Output(s)

Output Type(s): Floats

Output Format(s): List of Floats

Output Parameters: 1D

Other Properties Related to Output: Each value corresponds to a raw logit. Users can choose to apply a Sigmoid activation function to the logits to convert them into probabilities during model usage.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Installation

The model requires transformers version >= 4.47.1, and also flash-attention.

pip install "transformers>=4.47.1,<5.0.0"
pip install "flash-attn>=2.6.3,<2.8" --no-build-isolation

Transformers Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoProcessor
from transformers.image_utils import load_image

modality = "image"

# Load model
model_path = "nvidia/llama-nemotron-rerank-vl-1b-v2"
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForSequenceClassification.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    attn_implementation="flash_attention_2",
    device_map="auto"
).eval()

# Build processor kwargs (base settings)
processor_kwargs = {
    "trust_remote_code": True,
    "max_input_tiles": 6,
    "use_thumbnail": True
}

# Set rerank_max_length based on modality
if modality == "image":
    processor_kwargs["rerank_max_length"] = 2048
elif modality == "image_text":
    processor_kwargs["rerank_max_length"] = 10240
elif modality == "text":
    processor_kwargs["rerank_max_length"] = 8192

# Load processor with modality-specific kwargs
processor = AutoProcessor.from_pretrained(
    model_path,
    **processor_kwargs
)

query = "How is AI improving the intelligence and capabilities of robots?"
image_paths = [
    "https://developer.download.nvidia.com/images/isaac/nvidia-isaac-lab-1920x1080.jpg",
    "https://blogs.nvidia.com/wp-content/uploads/2018/01/automotive-key-visual-corp-blog-level4-av-og-1280x680-1.png",
    "https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/hc-press-evo2-nim-25-featured-b.jpg"
]

# Load all images
images = [load_image(img_path) for img_path in image_paths]

# Text descriptions corresponding to each image/document
document_texts = [
    "AI enables robots to perceive, plan, and act autonomously.",
    "AI is transforming autonomous vehicles by enabling safer, smarter, and more reliable decision-making on the road.",
    "A biological foundation model designed to analyze and generate DNA, RNA, and protein sequences.",
]

if modality == "image":
    # Prepare inputs: same query, different images
    examples = [{
        "question": query,
        "doc_text": "",
        "doc_image": image
    } for image in images]

elif modality == "image_text":
    examples = [{
        "question": query,
        "doc_text": doc_text,
        "doc_image": image
    } for image, doc_text in zip(images, document_texts)]

elif modality == "text":
    # Prepare inputs: same query, different texts
    examples = [{
        "question": query,
        "doc_text": doc_text,
        "doc_image": ""
    } for doc_text in document_texts]

else:
    raise ValueError(f"Invalid modality: {modality}. Must be 'image', 'image_text', or 'text'")

# Process with processor
batch_dict = processor.process_queries_documents_crossencoder(examples)

# Move to device
batch_dict = {
    k: v.to(device) if isinstance(v, torch.Tensor) else v
    for k, v in batch_dict.items()
}

# Run inference
with torch.no_grad():
    outputs = model(**batch_dict, return_dict=True)

# Get logits
logits = outputs.logits
logits_flat = logits.squeeze(-1)
    
# Get sorted indices (highest to lowest)
sorted_indices = torch.argsort(logits_flat, descending=True)

print(f"\nRanking (highest to lowest relevance for the modality {modality}):")
for rank, idx in enumerate(sorted_indices, 1):
    doc_idx = idx.item()
    logit_val = logits_flat[doc_idx].item()
    if modality == "text":
        print(f"  Rank {rank}: logit={logit_val:.4f} | Text: {document_texts[doc_idx]}")
    else:  # image or image_text modality
        print(f"  Rank {rank}: logit={logit_val:.4f} | Image: {image_paths[doc_idx]}")

Software Integration:

Runtime Engine(s):

• TensorRT
  

Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace, NVIDIA Blackwell
Preferred/Supported Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

llama-nemotron-rerank-vl-1b-v2

Training and Evaluation Datasets:

Training Dataset

The development of large-scale, public open-QA datasets has driven significant progress in powerful vision-language models, as well as vision embedding and reranking models. However, following issues limit the use of these models in commercial settings:

• Commercial licensing restrictions in popular public datasets, such as MS MARCO.
  
• Many multimodal datasets rely on synthetic data generated with proprietary models.

NVIDIA's training dataset is based on public QA datasets, and only includes datasets that have a license for commercial applications.

Properties: The model was fine-tuned with publicly available image datasets. We also generated synthetic queries for the image corpora, whose original queries were produced using proprietary models.

Data Modality

• Image

Image Training Data Size

• Less than a Million Images

Data Collection Method by dataset Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

Evaluation Dataset:

We evaluate the embedding + reranking pipeline on a set of evaluation benchmarks. We applied the ranking model to the candidates retrieved from the llama-nemotron-embed-vl-1b-v2 model.

Vision document retrieval benchmarks
We evaluated llama-nemotron-rerank-vl-1b-v2 on five various visual document retrieval datasets: the popular ViDoRe V1, V2, the new Vidore V3, and two internal visual document retrieval datasets:

• DigitalCorpora-10k: A dataset with questions based on a corpus of 10k documents from DigitalCorpora that have a good mixture of text, tables, and charts.
• Earnings V2: an internal retrieval dataset of 287 questions based on 500 PDFs, mostly consisting of earnings reports from big tech companies.

For those interested in reproducing our results, one of our internal datasets (DigitalCorpora-10k) can be created by following instructions in this notebook from the NeMo Retriever Extraction GitHub repository.

Text retrieval benchmarks
We evaluated llama-nemotron-rerank-vl-1b-v2 on 92 text retrieval datasets, from the benchmarks BEIR, MIRACL (multi-language), MLQA (cross-language) and MLDR (long-context).

Evaluation Results

Visual Document Retrieval (page retrieval)

In this section, we report the performance of llama-nemotron-rerank-vl-1b-v2 on different input modalities. In the table below, we can see that compared to the VLM embedding baseline, the VLM reranking model increases the Avg Recall@5 by approximately 7.2% for text modality, 6.9% for image modality, and 6% for image + text modality on 5 evaluation datasets.

Note: Image+Text modality means that both the page image and its text (extracted using ingestion libraries like NV-Ingest) are fed as input to the reranking model for more accurate representation and retrieval.

Visual Document Retrieval benchmarks- Avg Recall@5 on DC10k, Earnings V2, ViDoRe V1, V2, V3

	Modality
Model	Text	Image	Image + Text
llama-nemotron-embed-vl-1b-v2	71.04%	71.20%	73.24%
+ llama-nemotron-rerank-vl-1b-v2	76.12%	76.12%	77.64%

The table below demonstrates the llama-nemotron-rerank-vl-1b-v2's evaluation accuracy performance compared to two other publicly available multimodal reranker models: jina-reranker-m0 and MonoQwen2-VL-v0.1. The Jina model does not have commercial license and it does not support image+text modality out of the box, thus, we report image only and text only evaluation scores for this model.

	Modality
Model	Text	Image	Image+Text
llama-nemotron-rerank-vl-1b-v2	76.12%	76.12%	77.64%
jina-reranker-m0	69.31%	78.33%	NA
MonoQwen2-VL-v0.1	74.70%	75.80%	75.98%

Text Retrieval (chunk retrieval)

The llama-nemotron-rerank-vl-1b-v2 demonstrates competitive retrieval accuracy on text retrieval benchmarks, comparable to NVIDIA's text-only reranking model llama-nemotron-rerank-1b-v2. This means you can deploy NVIDIA's VLM-based llama-nemotron-embed-vl-1b-v2 embedding model along with llama-nemotron-rerank-vl-1b-v2 reranking model, regardless of whether your retrieval corpus consists of images, text, or both.

Text Retrieval benchmarks (chunk retrieval) - Avg. Recall@5

Model	BEIR retrieval + TechQA	MIRACL	MLQA	MLDR	Average
llama-nemotron-embed-1b-v2 + llama-nemotron-rerank-1b-v2	73.64%	65.80%	86.83%	68.49%	73.69%
llama-nemotron-embed-vl-1b-v2 + llama-nemotron-rerank-vl-1b-v2	73.18%	65.71%	87.05%	69.98%	73.98%

Data Collection Method by dataset Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
Properties More details on ViDoRe benchmarks can be found on their Hugging Face page.

Inference

• Acceleration Engine: TensorRT
  
• Test Hardware: H100, A100, L40S, A10G, B200, RTX PRO 6000

Citation

@inproceedings{moreira2025_nvretriever,
author = {Moreira, Gabriel de Souza P. and Osmulski, Radek and Xu, Mengyao and Ak, Ronay and Schifferer, Benedikt and Oldridge, Even},
title = {Improving Text Embedding Models with Positive-aware Hard-negative Mining},
year = {2025},
isbn = {9798400720406},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3746252.3761254},
doi = {10.1145/3746252.3761254},
pages = {2169–2178},
numpages = {10},
keywords = {contrastive learning, distillation, embedding models, hard-negative mining, rag, text retrieval, transformers},
location = {Seoul, Republic of Korea},
series = {CIKM '25}
}

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Explainability, Bias, Safety, and Privacy sections.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Bias

Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing	None
Measures taken to mitigate against unwanted bias	None

Explainability

Field	Response
Intended Application & Domain:	Passage or document ranking for question and answer retrieval.
Model Type:	Transformer cross-encoder.
Intended User:	Generative AI developers working with conversational AI models. This is suitable for users building question-answering applications over large multimodal corpora and aiming to improve retrieval performance by reranking a set of candidate documents for a given question. The corpus may include visually rich document images (e.g., pages with text, figures, tables, charts, or infographics) and/or text extracted from documents.
Output:	List of Floats (Score/Logit indicating if a passage/document relevant to a question).
Describe how the model works:	The model outputs a relevance score (logit) reflecting how strongly it predicts that the passage or document contains the information required to answer the question.
Technical Limitations:	The model's max sequence length is 10240. Longer text inputs should be truncated.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:	N/A
Verified to have met prescribed NVIDIA quality standards:	Yes
Performance Metrics:	Accuracy, Throughput, and Latency.
Potential Known Risks:	This model does not always guarantee to provide a meaningful ranking of document(s) for a given query.
Licensing & Terms of Use:	The use of this model is governed by the NVIDIA Open Model License Agreement and the use of the post-processing scripts are licensed under Apache 2.0. Additional Information: Llama 3.2 Community Model License Agreement. Built with Llama.

Privacy

Field	Response
Generatable or reverse engineerable personal data?	No
Personal data used to create this model?	None Known
How often is dataset reviewed?	Dataset is initially reviewed upon addition, and subsequent reviews are conducted as needed or upon request for changes.
Is there provenance for all datasets used in training?	Yes
Does data labeling (annotation, metadata) comply with privacy laws?	Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?	No, not possible with externally-sourced data.
Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?	No
Was consent obtained for any personal data used?	Not Applicable
Applicable Privacy Policy	https://www.nvidia.com/en-us/about-nvidia/privacy-policy/

Safety

Field	Response
Model Application(s):	Document Reranking for Retrieval. User queries can be text and documents can be text, document page images, charts, tables, and infographics.
Describe the physical safety impact (if present).	Not Applicable
Use Case Restrictions:	The use of this model is governed by the NVIDIA Open Model License Agreement and the use of the post-processing scripts are licensed under Apache 2.0. Additional Information: Llama 3.2 Community Model License Agreement. Built with Llama.
Model and dataset restrictions:	The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.