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NVIDIA-Nemotron-Nano-12B-v2-VL
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The model is an auto-regressive vision language model that uses an optimized transformer architecture. The model enables multi-image reasoning and video understanding, along with strong document intelligence, visual Q&A and summarization capabilities.
NVIDIA-Nemotron-Nano-12B-v2-VL
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license: other license_name: nvidia-open-model-license license_link: >- https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: image-text-to-text library_name: transformers tags:
- nvidia
- VLM
- FP8
NVIDIA-Nemotron-Nano-VL-12B-V2-FP8
Model Overview
Description
NVIDIA-Nemotron-Nano-VL-12B-V2-FP8 is the quantized version of the NVIDIA Llama Nemotron Nano VL V2 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.
This model was trained on commercial images for all three stages of training and supports single image inference.
License/Terms of Use
Governing Terms:
Your use of the model is governed by the NVIDIA Open License Agreement.
Additional Information:
Backbone LLM: NVIDIA-Nemotron-Nano-12B-v2.
Deployment Geography:
Global
Use Case:
Customers: AI foundry enterprise customers
Use Cases: Image summarization. Text-image analysis, Optical Character Recognition, Interactive Q&A on images, Text Chain-of-Thought reasoning
Release Date:
- Hugging Face [October 28th, 2025]
Model Architecture:
Network Type: Transformer
Network Architecture:
Vision Encoder: C-RADIOv2-H
Language Encoder: NVIDIA-Nemotron-Nano-12B-v2
Input
Input Type(s): Image, Text
- Input Images
- Language Supported: German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese, English
Input Format(s): Image (Red, Green, Blue (RGB)), and Text (String)
Input Parameters: Image (2D), Text (1D)
Other Properties Related to Input:
- Context length up to 128K
- 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): Sequences up to 128K
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.
Software Integration
Runtime Engine(s): vLLM
Supported Hardware Microarchitecture Compatibility: H100 SXM 80GB
Supported Operating System(s): Linux
Model Versions:
Nemotron-Nano-VL-12B-V2-FP8
Quick Start
Install Dependencies
pip install causal_conv1d "transformers>4.53,<4.54" torch timm "mamba-ssm==2.2.5" accelerate open_clip_torch numpy pillow
Usage
To serve this checkpoint with vLLM, you can start the docker vllm/vllm-openai:nightly and run the sample command below:
python3 -m vllm.entrypoints.openai.api_server --model nvidia/Nemotron-Nano-VL-12B-V2-FP8 --trust-remote-code --quantization modelopt
Training, Testing, and Evaluation Datasets:
Training Datasets:
Data Modalities
** Total Size: 39'486'703 samples
** Total Number of Datasets: 270
** Text-only datasets: 33
** Text-and-image datasets: 176
** Video-and-text datasets: 61
** Total size: 27.7 TB
** Data modalities: Text, Image, Video
** Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
** Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
** Dataset partition: Training [100%], Testing [0%], Validation [0%]
** Time period for training data collection: 2023-2025
** Time period for testing data collection: N/A
** Time period for validation data collection: N/A
The post-training datasets consist of a mix of internal and public datasets designed for training vision language models across various tasks. It includes:
- Public datasets sourced from publicly available images and annotations, supporting tasks like classification, captioning, visual question answering, conversation modeling, document analysis and text/image reasoning.
- Internal text and image datasets built with public commercial images and internal labels, adapted for the same tasks as listed above.
- Synthetic image datasets generated programmatically for specific tasks like tabular data understanding and optical character recognition (OCR), for English, Chinese as well as other languages.
- Video datasets supporting video question answering and reasoning tasks from publicly available video sources, with either publicly available or internally generated annotations.
- Specialized datasets for safety alignment, function calling, and domain-specific tasks (e.g., science diagrams, financial question answering).
- NVIDIA-Sourced Synthetic Datasets for text reasoning.
- Private datasets for safety alignment or VQA on invoices.
- Crawled or scraped captioning, VQA, and video datasets.
- Some datasets were improved with Qwen2.5-72B-Instruct annotations
For around ~30% of our total training corpus and several of the domains listed above, we used commeNVIDIA-Nemotron-Nano-VL-12B-V2-FP8 is the quantized version of the NVIDIA Llama Nemotron Nano VL V2 model, which is an auto-regressive vision language model that uses an optimized transformer architecturercially permissive models to perform:
- Language translation
- Re-labeling of annotations for text, image and video datasets
- Synthetic data generation
- Generating chain-of-thought (CoT) traces
Additional processing for several datasets included rule-based QA generation (e.g., with templates), expanding short answers into longer responses, as well as proper reformatting. More details can be found here.
** Image based datasets were all scanned against known CSAM to make sure no such content was included in training.
Public Datasets
| Dataset Name | Type | Modalities | Number of Samples | Size |
|---|---|---|---|---|
| Captioning on Open Images (subset, relabeled) | VQA | image, text | 1'278'221 | 378.34 GB |
| Localized Narratives (subset, relabeled) | VQA | image, text | 503'275 | 147.67 GB |
| TextCaps (subset) | Image Captioning | image, text | 21'953 | 5.76 GB |
| TextCaps (subset) | Image Captioning | image, text | 109'765 | 28.81 GB |
| TextVQA (subset) | Image Captioning | image, text | 34'602 | 9.08 GB |
| RefCoco | Referring Expression Grounding | image, text | 14'694 | 2.39 GB |
| VQAv2 | VQA | image, text | 28'555 | 4.41 GB |
| AOKVQA | VQA | image, text | 20'832 | 3.39 GB |
| GQA | VQA | image, text | 21'433 | 2.94 GB |
| AOKVQA | VQA | image, text | 16'131 | 2.62 GB |
| synthdog-en | OCR | image, text | 29'672 | 2.31 GB |
| WIT | Image Captioning | image, text | 538'916 | 745.24 GB |
| CLEVR | Image Reasoning | image, text | 70'000 | 12.57 GB |
| CLEVR-Math | Image Reasoning | image, text | 70'000 | 12.47 GB |
| OpenAssistant (oasst1, oasst2) | Text Instruction Tuning | text | 47'118 | 0.09 GB |
| VATEX | Video Captioning | video, text | 2'880 | 5.50 GB |
| YouCook2 | Video Captioning | video, text | 36 | 0.17 GB |
| VCG+ 112K | VideoQA | video, text | 164 | 2.82 GB |
| Video Localized Narratives | Video Captioning | video, text | 373 | 0.64 GB |
| CLEVRER | VQA | video, text | 40'000 | 46.05 GB |
| NExT-QA | VideoQA | video, text | 10'368 | 57.06 GB |
| CLEVRER | Video Reasoning | video, text | 42'620 | 49.10 GB |
| ScreenQA | VQA | image, text | 302'004 | 30.52 GB |
| WikiSQL | Image Reasoning | image, text | N/A | N/A |
| WikiTableQuestions | TextQA | text | N/A | N/A |
| RenderedText | OCR | image, text | N/A | N/A |
| FinQA | Text Reasoning | text | N/A | N/A |
| TAT-QA | Text Reasoning | text | N/A | N/A |
| Databricks Dolly 15K | Text Instruction Tuning | text | N/A | N/A |
| WebSight | Image Classification | image, text | N/A | N/A |
| RAVEN | Image Reasoning | image, text | N/A | N/A |
| VizWiz | VQA | image, text | N/A | N/A |
| Inter-GPS | Image Reasoning | image, text | N/A | N/A |
| OCR dataset from arXiv data | OCR | image, text | 120'000 | 49.99 GB |
| OCR dataset from arXiv data | OCR | image, text | 599'927 | 249.93 GB |
| OCR dataset from arXiv data | OCR | image, text | 1'565'011 | 1637.79 GB |
| OCR dataset from arXiv data | OCR | image, text | 418'059 | 422.04 GB |
| OCR dataset from arXiv data | OCR | image, text | 200'001 | 200.89 GB |
| OCR dataset from arXiv data | OCR | image, text | 200'000 | 198.94 GB |
| OCR dataset from arXiv data | OCR | image, text | 200'001 | 196.08 GB |
| OCR dataset from arXiv data | OCR | image, text | 400'000 | 382.95 GB |
| OCR dataset from arXiv data | OCR | image, text | 400'000 | 388.16 GB |
| OCR dataset from arXiv data | OCR | image, text | 18'280 | 20.98 GB |
| DocLayNet (curated) | OCR | image, text | 48'369 | 18.59 GB |
| DocLayNet (curated & augmented) | OCR | image, text | 48'249 | 9.12 GB |
| DocLayNet (curated & augmented) | OCR | image, text | 48'267 | 9.09 GB |
| SynthTabNet | OCR | image, text | 200'000 | 9.70 GB |
| OCR dataset based on pdfs from CommonCrawl | OCR | image, text | 14'309 | 17.00 GB |
| OCR dataset based on pdfs from CommonCrawl | OCR | image, text | 8'461 | 7.77 GB |
| OCR dataset based on pdfs from CommonCrawl | OCR | image, text | 8'462 | 7.99 GB |
| OCR dataset based on pdfs from CommonCrawl | OCR | image, text | 14'236 | 5.84 GB |
| OCR dataset based on pdfs from CommonCrawl | OCR | image, text | 14'232 | 5.92 GB |
| SynthTables | OCR | image, text | 4'887 | 0.38 GB |
| TabRecSet | OCR | image, text | 25'281 | 2.46 GB |
| TabRecSet | OCR | image, text | 25'281 | 1.61 GB |
| FinTabNet | OCR | image, text | 57'137 | 9.22 GB |
| FinTabNet | OCR | image, text | 57'131 | 21.76 GB |
| FinTabNet | OCR | image, text | 57'129 | 21.68 GB |
| PubTables-1M | OCR | image, text | 224'170 | 29.55 GB |
| PubTables-1M | OCR | image, text | 224'169 | 36.32 GB |
| PubTables-1M | OCR | image, text | 225'108 | 36.45 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 37.13 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 33.38 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 32.85 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 31.15 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 30.30 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 38.40 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 27.09 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 29.52 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 30.49 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 30.14 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 100.14 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 93.82 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 93.96 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 90.61 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 89.89 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 95.75 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 85.65 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 91.01 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 90.29 GB |
| OCR dataset based on Wikimedia | OCR | image, text | 200'000 | 84.66 GB |
| TextOCR | OCR | image, text | 21'727 | 5.83 GB |
| TextOCR | OCR | image, text | 21'138 | 2.83 GB |
| Table OCR on pdfs from CommonCrawl | OCR | image, text | 19'359 | 12.92 GB |
| Table OCR on pdfs from CommonCrawl | OCR | image, text | 19'351 | 14.57 GB |
| Table OCR on pdfs from CommonCrawl | OCR | image, text | 19'350 | 14.44 GB |
| HierText | OCR | image, text | 8'278 | 2.60 GB |
| FUNSD | OCR | image, text | 149 | 0.01 GB |
| Gretel Synthetic Safety Alignment | Safety | Text | 19'779 | 0.03 GB |
| Internal safety alignment multimodal dataset | Safety | image, text | 22'559 | 8.27 GB |
| ALFRED Action | Safety | video, text | 6'524 | 5.92 GB |
| ALFRED Goal | Safety | video, text | 6'464 | 5.86 GB |
| VQA-RAD | Safety | image, text | 1'793 | 0.09 GB |
| SLAKE | Safety | image, text | 9'835 | 0.85 GB |
| STEM MMLU-aux (subset) | Safety | text | 37'444 | 0.49 GB |
| Glaive & Xlam | Function call | text | 8'000 | 0.02 GB |
| Textbooks VQA | VQA | image, text | 46'745 | 10.85 GB |
| ai2d | VQA | image, text | 12'413 | 2.23 GB |
| ScienceQA | VQA | image, text | 12'716 | 0.39 GB |
| ScienceQA from LlaVA-OneVision | VQA | image, text | 19'196 | 0.65 GB |
| ChartQA | VQA | image, text | 15'121 | 0.68 GB |
| ChartQA (augmented) | VQA | image, text | 15'050 | 0.65 GB |
| ChartQA (CoT) | VQA | image, text | 23'571 | 1.04 GB |
| ChartQA | VQA | image, text | 60'438 | 2.69 GB |
| Geo170K | VQA | image, text | 13'263 | 0.07 GB |
| InfographicVQA | VQA | image, text | 23'946 | 8.21 GB |
| DocVQA | VQA | image, text | 39'463 | 26.29 GB |
| DocVQA (CoT) | Image Reasoning | image, text | 16'881 | 10.65 GB |
| ALLaVA-4V (subset) | Visual Instruction Tuning | image, text | 524'892 | 96.99 GB |
| ALLaVA-4V (subset) | Visual Instruction Tuning | image, text | 227'776 | 42.52 GB |
| TabMWP | Image Reasoning | image, text | 23'058 | 0.30 GB |
| PMC-VQA | VQA | image, text | 2'266 | 0.04 GB |
| OCR-VQA from The Cauldron | VQA | image, text | 165'746 | 5.79 GB |
| ST-VQA from The Cauldron | VQA | image, text | 17'232 | 0.68 GB |
| WebSight from The Cauldron | OCR | image, text | 9'809 | 1.84 GB |
| EST-VQA | VQA | image, text | 17'043 | 4.25 GB |
| TAL Handwritten English OCR | OCR | image, text | 9'998 | 0.22 GB |
| TAL Handwritten Math writing | OCR | image, text | 22'244 | 0.33 GB |
| SlideVQA | VQA | image, text | 5'773 | 0.42 GB |
| pixmo-docs | VQA | image, text | 251'165 | 34.88 GB |
| pixmo-cap | Image Captioning | image, text | 706'897 | 261.63 GB |
| pixmo-cap-qa | VQA | image, text | 214'978 | 56.72 GB |
| pixmo-ask-model-anything | Visual Instruction Tuning | image, text | 153'592 | 20.50 GB |
| TallyQA | VQA | image, text | 68'775 | 10.64 GB |
| Bounding box to text annotations on a subset of Open Images | VQA | image, text | 1'664'533 | 490.37 GB |
| Bounding box to text annotations on a subset of Open Images | VQA | image, text | 1'664'533 | 488.17 GB |
| Bounding box to text annotations on a subset of Open Images | VQA | image, text | 1'128'326 | 324.46 GB |
| TabMWP (CoT) | Image Reasoning | image, text | 20'305 | 0.28 GB |
| VisualWebInstruct | Visual Instruction Tuning | image, text | 260'419 | 7.41 GB |
| Internal collection of public text SFT datasets | Text Instruction Tuning | text | 197'938 | 1.04 GB |
| ReCTS from ICDAR2019 | OCR | image, text | 20'000 | 1.77 GB |
| RCTW from ICDAR2017 | OCR | image, text | 8'034 | 7.85 GB |
| OCR equation heavy dataset from arXiv data | OCR | image, text | 2'000 | 0.03 GB |
| Mulberry-SFT (CoT) | Image Reasoning | image, text | 191'332 | 30.80 GB |
| LLaVA-CoT-100k (CoT) | Image Reasoning | image, text | 63'013 | 8.18 GB |
| GeomVerse (CoT) | Image Reasoning | image, text | 9'298 | 0.90 GB |
| MapQA (CoT) | Image Reasoning | image, text | 16'832 | 1.77 GB |
| MetaMathQA (CoT) | Text Reasoning | text | 225'408 | 4.55 GB |
| MetaMathQA (CoT) | Image Reasoning | image, text | 220'544 | 4.48 GB |
| PlotQA (CoT) | Image Reasoning | image, text | 16'256 | 0.76 GB |
| Visual7W Telling (CoT) | Image Reasoning | image, text | 62'592 | 3.21 GB |
| Visual7W Pointing | VQA | image, text | 25'733 | 0.93 GB |
| VisText | Image Captioning | image, text | 9'969 | 0.52 GB |
| ScreenQA | VQA | image, text | 32'724 | 3.51 GB |
| wave-ui-25k | OCR | image, text | 24'978 | 11.44 GB |
| Charts2500 | VQA | image, text | 2'486 | 0.09 GB |
| Cyrillic | OCR | image, text | 72'284 | 1.49 GB |
| CMM-Math | Image Reasoning | image, text | 13'148 | 0.05 GB |
| SimChart9K | Image Reasoning | image, text | 9'536 | 0.69 GB |
| UniChart | Image Reasoning | image, text | 504'885 | 17.04 GB |
| CASIA-HWDB2-line | OCR | image, text | 2'193 | 0.09 GB |
| MMTab | VQA | image, text | 232'746 | 59.23 GB |
| ArxivQA | VQA | image, text | 99'995 | 17.32 GB |
| docmatix-single | VQA | image, text | 19'992 | 3.94 GB |
| DocReason525K | Image Reasoning | image, text | 25'863 | 33.80 GB |
| FigureQA | VQA | image, text | 100'000 | 2.37 GB |
| LRV-Instruction | Visual Instruction Tuning | image, text | 7'198 | 0.37 GB |
| VisualWebInstruct (CoT) | Image Reasoning | image, text | 48'929 | 4.37 GB |
| DocMatix (multi-page) | Image Reasoning | image, text | 19'969 | 8.66 GB |
| spot-the-diff | Image Reasoning | image, text | 8'007 | 1.45 GB |
| DocVQA (CoT) | Image Reasoning | image, text | 36'333 | 24.32 GB |
| DocVQA (CoT) | Image Reasoning | image, text | 45'710 | 2.10 GB |
| DocVQA (CoT) | Image Reasoning | image, text | 19'548 | 6.70 GB |
| Mulberry-SFT (subset, CoT) | Image Reasoning | image, text | 103'763 | 18.45 GB |
| UniGeo (CoT) | Image Reasoning | image, text | 9'728 | 0.05 GB |
| NIGHTS | Image Reasoning | image, text | 12'906 | 37.01 GB |
| Mantis-Instruct (CoT) | Image Reasoning | image, text | 67'723 | 13.86 GB |
| OCR dataset based on pdfs from CommonCrawl | Image Reasoning | image, text | 2'858 | 1.23 GB |
| OCR dataset based on pdfs from CommonCrawl | Image Reasoning | image, text | 586 | 0.46 GB |
| FinTabNet (relabeled) | Image Reasoning | image, text | 8'356 | 3.17 GB |
| Table OCR on pdfs from CommonCrawl | Image Reasoning | image, text | 4'846 | 3.65 GB |
| HierText (relabeled for QA) | Image Reasoning | image, text | 514 | 0.07 GB |
| ECD-10k-Images | Image Reasoning | image, text | 132'613 | 15.38 GB |
| ActivityNet (open-ended QA) | VideoQA | video, text | 6'490 | 162.22 GB |
| NExT-QA (multi-choice QA) | VideoQA | video, text | 5'496 | 11.07 GB |
| NExT-QA (open-ended QA) | VideoQA | video, text | 5'492 | 10.99 GB |
| NExT-QA (multi-choice QA) | VideoQA | video, text | 52 | 0.74 GB |
| NExT-QA (open-ended QA) | VideoQA | video, text | 61 | 0.85 GB |
| NExT-QA (open-ended QA) | VideoQA | video, text | 6'843 | 27.83 GB |
| NExT-QA (multi-choice QA) | VideoQA | video, text | 6'843 | 27.85 GB |
| ActivityNet (open-ended QA) | VideoQA | video, text | 7'420 | 102.81 GB |
| ActivityNet (open-ended QA) | VideoQA | video, text | 3'840 | 25.84 GB |
| NExT-QA (multi-choice QA) | VideoQA | video, text | 4'633 | 35.38 GB |
| NExT-QA (open-ended QA) | VideoQA | video, text | 4'694 | 35.84 GB |
| ActivityNet (open-ended QA) | VideoQA | video, text | 2'580 | 7.46 GB |
| Perception Test (multi-choice QA) | VideoQA | video, text | 1'785 | 18.67 GB |
| Perception Test (multi-choice QA) | VideoQA | video, text | 618 | 11.52 GB |
| NExT-QA | VideoQA | video, text | 34'132 | 150.86 GB |
| CLEVRER | VideoQA | video, text | 40'000 | 46.03 GB |
| Video dataset based on Kinetics | VideoQA | video, text | 39'452 | 26.15 GB |
| EGO4D | VideoQA | video, text | 7'797 | 3.38 GB |
| TVQA | VideoQA | video, text | 34'868 | 100.05 GB |
| EgoExoLearn | VideoQA | video, text | 36'373 | 8558.27 GB |
| Video dataset based on Kinetics | VideoQA | video, text | 647'883 | 890.56 GB |
| Mementos | VideoQA | video, text | 4'060 | 14.07 GB |
| Perception Test | VideoQA | video, text | 7'392 | 94.95 GB |
| ActivityNet | VideoQA | video, text | 10'021 | 191.49 GB |
| EGO4D | VideoQA | video, text | 1'506 | 137.00 GB |
| FineAction | VideoQA | video, text | 7'504 | 169.76 GB |
| HACS | VideoQA | video, text | 31'223 | 829.25 GB |
| HiREST | VideoQA | video, text | 822 | 42.50 GB |
| Perception Test | VideoQA | video, text | 2'135 | 25.98 GB |
| ActivityNet | VideoQA | video, text | 9'064 | 181.24 GB |
| HiREST | VideoQA | video, text | 525 | 27.54 GB |
| YouCook2 | VideoQA | video, text | 1'180 | 77.65 GB |
| DiDeMo | VideoQA | video, text | 7'452 | 33.90 GB |
| EGO4D | VideoQA | video, text | 2'665 | 194.01 GB |
| MedVidQA | VideoQA | video, text | 933 | 40.35 GB |
| QuerYD | VideoQA | video, text | 1'562 | 50.69 GB |
| YouCook2 | VideoQA | video, text | 2'270 | 158.77 GB |
| EgoExoLearn (open-ended QA) | VideoQA | video, text | 9'998 | 1751.69 GB |
| Breakfast Actions | VideoQA | video, text | 1'204 | 3.45 GB |
| EgoExoLearn (multi-choice QA) | VideoQA | video, text | 6'832 | 1196.41 GB |
| CrossTask (multi-choice QA) | VideoQA | video, text | 75'686 | 417.50 GB |
| CrossTask (open-ended QA) | VideoQA | video, text | 20'399 | 112.02 GB |
| EgoProceL (multi-choice QA) | VideoQA | video, text | 4'789 | 42.74 GB |
| EgoProceL (open-ended QA) | VideoQA | video, text | 5'667 | 50.58 GB |
| HC-STVG (multi-choice QA) | VideoQA | video, text | 147'799 | 796.18 GB |
| HC-STVG (open-ended QA) | VideoQA | video, text | 41'050 | 221.82 GB |
| TAPOS (multi-choice QA) | VideoQA | video, text | 33'941 | 218.50 GB |
| TAPOS (open-ended QA) | VideoQA | video, text | 13'991 | 88.00 GB |
| Multi-page OCR based on CommonCrawl pdf data | VQA | image, text | 7'262 | 48.19 GB |
| Multi-page QA based on CommonCrawl pdf data | VQA | image, text | 455 | 31.88 GB |
| Table OCR dataset based on CommonCrawl pdf data | OCR | image, text | 4'281 | 0.68 GB |
| Table OCR dataset based on CommonCrawl pdf data | OCR | image, text | 4'285 | 0.67 GB |
| Table OCR dataset based on CommonCrawl pdf data | OCR | image, text | 4'282 | 0.67 GB |
| Selection of public datasets (relabeled) | Image Reasoning | image, text | 13'843 | 4.18 GB |
| Selection of public datasets (relabeled) | Image Reasoning | image, text | 18'442 | 3.89 GB |
| Perception Test | VideoQA | video, text | 7'392 | 94.95 GB |
| Perception Test (CoT) | VideoQA | video, text | 4'977 | 64.55 GB |
Private Datasets
| Dataset Name | Type | Modalities | Number of Samples | Size |
|---|---|---|---|---|
| Internal safety alignment text dataset | Safety | Text | N/A | N/A |
| Internal safety alignment text dataset | Safety | Text | N/A | N/A |
| Synthetic dataset with HLE data with DeepSeek-R1-0528 | Text Reasoning | text | 445'958 | 9.01 GB |
| Internal QA dataset on invoices | Image Reasoning | image, text | 6'471 | 5.22 GB |
| Internal QA dataset on invoices | Image Reasoning | image, text | 11'258 | 10.19 GB |
Data Crawling and Scraping
| Dataset Name | Type | Modalities | Number of Samples | Size |
|---|---|---|---|---|
| Internal video dataset | VideoQA | video, text | 274'472 | 348.84 GB |
| Internal video dataset | VideoQA | video, text | 14'256 | 44.46 GB |
| Internal VQA and captioning dataset | Image Captioning | image, text | 14'872 | 3.27 GB |
| Internal VQA dataset | VQA | image, text | 20'250 | 1.87 GB |
| Internal VQA dataset | VQA | image, text | 20'098 | 2.07 GB |
| Internal Captioning dataset | Image Captioning | image, text | 24'998 | 6.97 GB |
User-Sourced Data (Collected by Provider including Prompts)
Self-Sourced Synthetic Data
| Dataset Name | Type | Modalities | Number of Samples | Size |
|---|---|---|---|---|
| Random ASCII characters for OCR | OCR | image, text | 14'533 | 5.76 GB |
| Random ASCII characters for OCR | OCR | image, text | 14'533 | 9.26 GB |
| Random Chinese characters for OCR | OCR | image, text | 29'108 | 15.00 GB |
| Random Chinese characters for OCR | OCR | image, text | 29'108 | 24.11 GB |
| Random English characters for OCR | OCR | image, text | 14'525 | 5.65 GB |
| Random English characters for OCR | OCR | image, text | 14'525 | 9.39 GB |
| Synthetic sparse table dataset | OCR | image, text | 100'000 | 14.36 GB |
| Synthetic dataset with OpenCodeReasoning 2.0 from DeepSeek-R1-0528 | Text Reasoning | text | 1'165'591 | 54.15 GB |
| Synthetic dataset with OpenCodeReasoning 2.0 from DeepSeek-R1-0528 | Text Reasoning | text | 175'000 | 0.95 GB |
| Synthetic dataset with OpenSTEM from DeepSeek-R1-0528 | Text Reasoning | text | 1'922'012 | 28.00 GB |
| Synthetic dataset with OpenSTEM from DeepSeek-R1-0528 | Text Reasoning | text | 288'000 | 0.57 GB |
| Synthetic dataset with HLE data with DeepSeek-R1-0528 | Text Reasoning | text | 67'000 | 0.22 GB |
| Synthetic tool-calling data with seed tools from ToolBench, Glaive, xLAM and responses from Qwen3-235B-A22B with reasoning | Text Reasoning | text | 403'619 | 6.55 GB |
| Synthetic safety data with responses from DeepSeek-R1-0528 | Text Reasoning | text | 30'710 | 0.12 GB |
| Dummy conversation dataset | Text Reasoning | text | 2'262 | 0.00 GB |
| Chat data with HelpSteer2 HelpSteer3 as seed user prompts and responses from Qwen3-235B-A22B with reasoning | Text Reasoning | text | 32'752 | 0.26 GB |
| Chat data with HelpSteer2 HelpSteer3 as seed user prompts and responses from Qwen3-235B-A22B without reasoning | Text Reasoning | text | 3'636 | 0.01 GB |
| Synthetic chat dataset with responses from DeepSeek-R1 | Text Reasoning | text | 389'350 | 3.30 GB |
| Chat dataset with LMSYS-1M as seed user prompts and responses from Qwen3-235B-A22B with reasoning | Text Reasoning | text | 353'526 | 2.61 GB |
| Chat dataset with LMSYS-1M as seed user prompts and responses from Qwen3-235B-A22B without reasoning | Text Reasoning | text | 361'733 | 1.12 GB |
| Synthetic multilingual STEM from DeepSeek-R1-0528, Qwen2.5-32B-Instruct-AWQ, Qwen2.5-14B-Instruct | Text Reasoning | text | 4'999'794 | 86.68 GB |
| Chat dataset with WildChat-1M as seed user prompts and responses from Qwen3-235B-A22B with reasoning | Text Reasoning | text | 545'844 | 5.25 GB |
| Chat dataset with WildChat-1M as seed user prompts and responses from Qwen3-235B-A22B without reasoning | Text Reasoning | text | 81'876 | 0.43 GB |
| Synthetic Math with OpenMathReasoning from DeepSeek-R1-0528 | Text Reasoning | text | 1'591'641 | 58.63 GB |
| Synthetic Math with OpenMathReasoning from DeepSeek-R1-0528 | Text Reasoning | text | 239'467 | 0.52 GB |
| Synthetic dataset with OpenCodeReasoning 2.0 from DeepSeek-R1-0528 | Code | text | 1'165'591 | 54.15 GB |
| Synthetic tool calling dataset from DeepSeek-R1-0528 | Text Reasoning | text | 74'044 | 46.43 GB |
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 Datasets:
The following external benchmarks are used for evaluating the model:
| Dataset |
|---|
| AI2D Test |
| ChartQA Test |
| OCRBench |
| OCRBenchV2 English |
| DocVQA Val |
Data Collection Method by dataset:
- Hybrid: Human, Automated
Labeling Method by dataset:
- Hybrid: Human, Automated
Properties (Quantity, Dataset Descriptions, Sensor(s)): N/A
Dataset License(s): N/A
Evaluation Benchmarks:
| Benchmark | Score (FP8) | Score (BF16) |
|---|---|---|
| AI2D | 87.6% | 87.1% |
| OCRBenchV2 | 61.8% | 62.0% |
| OCRBench | 85.4% | 85.6% |
| ChartQA | 89.4% | 89.7% |
| DocVQA val | 94.3% | 94.4% |
Inference:
Engine: vLLM
Test Hardware:
- 1x NVIDIA H100 SXM 80GB
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.
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.
Outputs generated by these models may contain political content or other potentially misleading information, issues with content security and safety, or unwanted bias that is independent of our oversight.
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