Intended Use Case

Ethical Considerations

Example Curl Request

#!/bin/bash

curl -X 'POST' \
'<ENDPOINT_URL>/v1/chat/completions' \
    -H 'Accept: application/json' \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer <API_KEY>" \
    -d '{
    "messages": [
        {
            "role": "system",
            "content": "/think"
        },
        {
            "role": "user",
            "content": "Write a limerick about the wonders of GPU computing."
        }
    ],
    "max_tokens": 256
    }'

Training, Testing, and Evaluation Datasets

Training Datasets

Post-Training Datasets

• 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.

• Language translation
• Re-labeling of annotations for text, image and video datasets
• Synthetic data generation
• Generating chain-of-thought (CoT) traces

Public Datasets

Private Datasets

Data Crawling and Scraping

User-Sourced Data (Collected by Provider including Prompts)

Self-Sourced Synthetic Data

Properties

• 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

• RDTableBench
• NVIDIA internal test set for OCR
• MMMU Val with ChatGPT as judge
• AI2D Test
• ChartQA Test
• InfoVQA Val
• OCRBench
• OCRBenchV2 English
• DocVQA Val
• SlideQA Val
• Video MME