Llama-3.1-Nemotron-Nano-VL-8B-V1-FP4-QAD is the quantized version of the NVIDIA Llama Nemotron Nano VL model, which is an auto-regressive vision language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Llama Nemotron Nano VL FP4 QAD model is quantized with TensorRT Model Optimizer.
The intended users of this model are AI foundry enterprise customers, as well as researchers or developers. This model may be used for image summarization, text-image analysis, Optical Character Recognition, interactive Q&A on images, and Chain-of-Thought reasoning.
Input Format(s): Image (Red, Green, Blue (RGB)), and Text (String)
Input Parameters: Image (Two-Dimensional - 2D), Text (One-Dimensional - 1D)
Other Properties Related to Input:
Language Supported: English only
Input + Output Token: 16K
Maximum Resolution: Determined by a 12-tile layout constraint, with each tile being 512 × 512 pixels. This supports aspect ratios such as:
4 × 3 layout: up to 2048 × 1536 pixels
3 × 4 layout: up to 1536 × 2048 pixels
2 × 6 layout: up to 1024 × 3072 pixels
6 × 2 layout: up to 3072 × 1024 pixels
Other configurations allowed, provided total tiles ≤ 12
Channel Count: 3 channels (RGB)
Alpha Channel: Not supported (no transparency)
Output
Output Type(s): Text
Output Formats: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Input + Output Token: 16K
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.
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.
Less than a Billion Tokens
NV-Pretraining and NV-CosmosNemotron-SFT were used for training and evaluation
Data Collection Method by dataset:
Hybrid: Human, Synthetic
Labeling Method by dataset:
Hybrid: Human, Synthetic
Properties:
The dataset collection (for training and evaluation) consists of a mix of internal and public datasets designed for training and evaluation across various tasks. It includes:
• Internal datasets built with public commercial images and internal labels, supporting tasks like conversation modeling and document analysis.
• Public datasets sourced from publicly available images and annotations, adapted for tasks such as image captioning and visual question answering.
• Synthetic datasets generated programmatically for specific tasks like tabular data understanding.
• Specialized datasets for safety alignment, function calling, and domain-specific tasks (e.g., science diagrams, financial question answering).
Evaluation Dataset:
NV-Pretraining and NV-CosmosNemotron-SFT were used for training and evaluation.
Data Collection Method by dataset:
Hybrid: Human, Synthetic
Labeling Method by dataset:
Hybrid: Human, Synthetic
Properties:
Additionally, the dataset collection (for training and evaluation) consists of a mix of internal and public datasets designed for training and evaluation across various tasks. It includes:
• Internal datasets built with public commercial images and internal labels, supporting tasks like conversation modeling and document analysis.
• Public datasets sourced from publicly available images and annotations, adapted for tasks such as image captioning and visual question answering.
• Synthetic datasets generated programmatically for specific tasks like tabular data understanding.
• Specialized datasets for safety alignment, function calling, and domain-specific tasks (e.g., science diagrams, financial question answering).
Evaluation Benchmarks:
Benchmark
Score (FP4)
Score (BF16)
MMMU Val with chatGPT as a judge
47.9%
48.2%
AI2D
85.0%
85.0%
ChartQA
86.5%
86.3%
InfoVQA Val
77.6%
77.4%
OCRBench
836
839
OCRBenchV2 English
59.5%
60.1%
OCRBenchV2 Chinese
38.0%
37.9%
DocVQA val
91.5%
91.2%
VideoMME*
54.6%
54.7%
*Calculated with 1 tile per image.
The evaluation for this checkpoint was done with FP4 simulated quantization on H100.
Inference
Engine: vLLM Test Hardware:
1x NVIDIA B100/B200
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 internal 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 Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.
Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
